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Segmentation

Marketing Market segmentation
Market segmentation pertains to the division of a market of consumers into persons with similar needs and wants. For instance, Kellogg's cereals, Frosties are marketed to children. Crunchy Nut Cornflakes are marketed to adults. Both goods denote two products which are marketed to two distinct groups of persons, both with similar needs, traits, and wants. In another example, Sun Microsystems can use market segmentation to classify its clients according to their promptness to adopt new products.

Market segmentation allows for a better allocation of a firm's finite resources. A firm only possesses a certain amount of resources. Accordingly, it must make choices (and incur the related costs) in servicing specific groups of consumers. In this way, the diversified tastes of contemporary Western consumers can be served better. With growing diversity in the tastes of modern consumers, firms are taking note of the benefit of servicing a multiplicity of new markets.

Market segmentation can be viewed as a key dynamic in interpreting and executing a logical perspective of Strategic Marketing Planning. The manifestation of this process is considered by many traditional thinkers to include the following;Segmenting, Targeting and Positioning.

Governance, risk management, and compliance GRC market segmentation
A GRC program can be instituted to focus on any individual area within the enterprise, or a fully integrated GRC is able to work across all areas of the enterprise, using a single framework.

A fully integrated GRC uses a single core set of control material, mapped to all of the primary governance factors being monitored. The use of a single framework also has the benefit of reducing the possibility of duplicated remedial actions.

When reviewed as individual GRC areas, the three most common individual headings are considered to be Financial GRC, IT GRC, and Legal GRC.

Financial GRC relates to the activities that are intended to ensure the correct operation of all financial processes, as well as compliance with any finance-related mandates.

IT GRC relates to the activities intended to ensure that the IT (Information Technology) organization supports the current and future needs of the business, and complies with all IT-related mandates.

Legal GRC focuses on tying together all three components via an organization's legal department and chief compliance officer.

Analysts disagree on how these aspects of GRC are defined as market categories. Gartner has stated that the broad GRC market includes the following areas:

They further divide the IT GRC management market into these key capabilities. Although this list relates to IT GRC, a similar list of capabilities would be suitable for other areas of GRC.

Policy distribution and response

IT Controls self-assessment and measurement

Automated general computer control (GCC) collection

Advanced IT risk evaluation and compliance dashboards

Demand chain - Demand chain budget segmentation, targeting and optimization
Demand chain budgets for marketing, sales and service expenditure are substantial. Maximising their impact on shareholder value has become an important financial goal for decision makers. Developing a shared language across marketing and finance is one the challenges to achieving this goal.

Segmentation is the initial thing to decide. From a strategic finance perspective "segments are responsibility centers for which a separate measure of revenues and costs is obtained". From a marketing perspective "segmentation is the act of dividing the market into distinct groups of buyers who might require separate products and/or marketing mixes". An important challenge for decision makers is how to align these two marketing and finance perspectives on segmentation.

Targeting of the budget is the final thing to decide. From the marketing perspective the challenge is how "to optimally allocate a given marketing budget to various target markets". From a finance perspective the problem is one of resource and budget allocation "determining the right quantity of resources to implement the value maximising strategy".

Optimization provides the technical basis for targeting decisions. Whilst mathematical optimization theory has been in existence since the 1950s, its application to marketing only began in the 1970s, and lack of data and computer power were limiting factors until the 1990s.

Since 2000, applying maths to budget segmentation, targeting and optimization has become more commonplace. In the UK the IPA Awards have documented over 1000 cases of modelling over 15 years, as part of their award process. The judging criteria are rigorous and not a matter of taste or fashion. Entrants must prove beyond all reasonable doubt that the marketing is profitable. It enables marketing to be brought centre stage in four important ways

First, it translates the language of marketing and sales into the language of the boardroom. Finance and profits are the preferred language of the modern executive suite. Marketing and sales strategies have to be justified in terms of their ability to increase the financial value of the business. It provides a bridge between marketing and the other functions.

Second, it strengthens demand chain accountability. In Marketing Departments awareness, preference and satisfaction are often tracked as alternative objectives to shareholder value. In Sales Departments, sales promotion spending is often used to boost volumes, even when the result is unprofitable. Optimization modelling can assess these practices and support more rigorous accountability methods.

Third, it provides a counter-argument to the arbitrary cutting of demand-chain budgets. Return on marketing investment models can help demonstrate where financial impact of demand driving activities is positive and negative, and so help support fact-based budgeting.

Finally, demand-chain profitability modelling encourages a strategic debate. Because long-term cashflow and NPV calculations can show the shareholder value effect of marketing, sales and service, strong arguments can be made for putting the Demand Chain on an equal footing to the Supply Chain.

Intel Segmentation There are also four 16-bit segment registers (see figure) that allow the 8086 CPU to access one megabyte of memory in an unusual way

Intel Segmentation The 16-byte separation between segment bases (due to the 4-bit shift) is called a paragraph. Although considered complicated and cumbersome by many programmers, this scheme also has advantages; a small program (less than 64 KB) can be loaded starting at a fixed offset (such as 0000) in its own segment, avoiding the need for relocation, with at most 15 bytes of alignment waste.

Intel Segmentation Compilers for the 8086-family commonly support two types of pointer, near and far

Intel Segmentation To avoid the need to specify near and far on numerous pointers, data structures, and functions, compilers also support "memory models" which specify default pointer sizes

Intel Segmentation According to Morse et al., the designers actually contemplated using an 8-bit shift (instead of 4-bit), in order to create a 16 MB physical address space. However, as this would have forced segments to begin on 256-byte boundaries, and 1 MB was considered very large for a microprocessor around 1976, the idea was dismissed. Also, there were not enough pins available on a low-cost 40-pin package for the additional four address bus pins.

Intel Segmentation In principle, the address space of the x86 series could have been extended in later processors by increasing the shift value, as long as applications obtained their segments from the operating system and did not make assumptions about the equivalence of different segment:offset pairs.[note 11] In practice the use of "huge" pointers and similar mechanisms was widespread and the flat 32-bit addressing made possible with the 32-bit offset registers in the eventually extended the limited addressing range in a more general way .

Memory protection - Segmentation
Segmentation refers to dividing a computer's memory into segments. A reference to a memory location includes a value that identifies a segment and an offset within that segment.

Memory protection - Segmentation
The x86 architecture has multiple segmentation features, which are helpful for using protected memory on this architecture

Memory protection - Simulated segmentation
Such an Instruction Set Simulator can provide memory protection by using a segmentation-like scheme and validating the target address and length of each instruction in real time before actually executing them

Memory protection - Simulated segmentation
It is generally not advisable to use this method of memory protection where adequate facilities exist on a CPU, as this takes valuable processing power from the computer. However, it is generally used for debugging and testing purposes to provide an extra fine level of granularity to otherwise generic storage violations and can indicate precisely which instruction is attempting to overwrite the particular section of storage which may have the same storage key as unprotected storage.

ISO 25178 - Parameters related to segmentation
These parameters are derived from a segmentation of the surface into motifs (dales and hills). Segmentation is carried out using the watersheds method.

Market segmentation Market segmentation is a marketing strategy that involves dividing a broad target market into subsets of consumers who have common needs, and then designing and implementing strategies to target their needs and desires using media channels and other touch-points that best allow to reach them.

Market segmentation Market segments allow companies to create product differentiation strategies to target them.

Market segmentation - Criteria for segmenting
It is possible to measure.

It must be large enough to earn profit.

It must be stable enough that it does not vanish after some time.

It is possible to reach potential customers via the organization's promotion and distribution channel.

It is internally homogeneous (potential customers in the same segment prefer the same product qualities).

It is externally heterogeneous, that is, potential customers from different segments have different quality preferences.

It responds consistently to a given market stimulus.

It is useful in deciding on the marketing mix.

Market segmentation - Geographic segmentation
Marketers can segment according to geographic criteria—nations, states, regions, countries, cities, neighbourhoods, or postal codes

Market segmentation - Behavioural segmentation
Behavioural segmentation divides consumers into groups according to their knowledge of, attitude towards, use of or response to a product

Market segmentation - Segmentation by occasions
Segmentation according to occasions relies on the special needs and desires of consumers on various occasions - for example, for products for use in relation with a certain holiday

Market segmentation - Segmentation by benefits
Segmentation can take place according to benefits sought by the consumer or according to perceived benefits which a product/service may provide.

Market segmentation - Using segmentation in customer retention
The basic approach to retention-based segmentation is that a company tags each of its active customers with three values:

Is this customer at high risk of canceling the company's service?

One of the most common indicators of high-risk customers is a drop off in usage of the company's service. For example, in the credit card industry this could be signaled through a customer's decline in spending on his or her card.

This determination boils down to whether the post-retention profit generated from the customer is predicted to be greater than the cost incurred to retain the customer.

What retention tactics should be used to retain this customer?

For customers who are deemed worthy of saving, it is essential for the company to know which save tactics are most likely to be successful. Tactics commonly used range from providing special customer discounts to sending customers communications that reinforce the value proposition of the given service.

Market segmentation - Price discrimination
Where a monopoly exists, the price of a product is likely to be higher than in a competitive market and the quantity sold less, generating monopoly profits for the seller

Market segmentation - Algorithms and approaches
Any existing discrete variable is a segmentation - this is called "a priori" segmentation, as opposed to "post-hoc" segmentation resulting from a research project commissioned to collect data on many customer attributes. Customers can be segmented by gender ('Male' or 'Female') or attitudes ('progressive' or 'conservative'), but also by discretized numeric variables, such as by age ("<30" or ">=30") or income ("The 99% (AGI<US $300,000)" vs "The 1% (AGI >= US $300,000)").

Common statistical techniques for segmentation analysis include:

Clustering algorithms such as K-means or other Cluster analysis

Statistical mixture models such as Latent Class Analysis

Market segmentation - Segmentation by Demography
Segmentation according to demography is based on variables such as age, gender, occupation and education level.

Reid, Robert D.; Bojanic, David C. (2009). Hospitality Marketing Management (5 ed.). John Wiley and Sons. p ISBN Retrieved "[...] market segmentation can be based on the benefits that consumers are seeking when they purchase a product."

Jump up ^ Gupta, Sunil. Lehmann, Donald R. Managing Customers as Investments: The Strategic Value of Customers in the Long Run, pages (“Customer Retention” section). Upper Saddle River, NJ: Pearson Education/Wharton School Publishing, ISBN

Network switch - Microsegmentation
In the extreme of microsegmentation, each device is located on a dedicated switch port

Grid computing - Market segmentation of the grid computing market
For the segmentation of the grid computing market, two perspectives need to be considered: the provider side and the user side:

X86 - Segmentation Minicomputers during the late 1970s were running up against the 16-bit 64-kilobyte|KB address limit, as memory had become cheaper

X86 - Segmentation Data and/or code could be managed within near 16-bit segments within this 1 megabyte|MB address space, or a compiler could operate in a far mode using 32-bit segment:offset pairs reaching (only) 1MB

X86 - Segmentation In real mode, segmentation is achieved by Bit shift|shifting the segment address left by 4bits and adding an offset in order to receive a final 20-bit address

X86 - Segmentation In this scheme, two different segment/offset pairs can point at a single absolute location. Thus, if DS is A111h and SI is 4567h, DS:SI will point at the same A5677h as above. This scheme makes it impossible to use more than four segments at once. CS and SS are vital for the correct functioning of the program, so that only DS and ES can be used to point to data segments outside the program (or, more precisely, outside the currently executing segment of the program) or the stack.

X86 - Segmentation In protected mode, a segment register no longer contains the physical address of the beginning of a segment, but contain a selector that points to a system-level structure called a segment descriptor

X86 - Segmentation The segmented nature can make programming and compiler design difficult because the use of near and far pointers affects performance.

X-ray computed tomography - Image segmentation
Where different structures have similar radiodensity, it can become impossible to separate them simply by adjusting volume rendering parameters. The solution is called segmentation, a manual or automatic procedure that can remove the unwanted structures from the image.

Neurogastroenterology - Segmentation
Segmentation contractions are the contractions in intestines carried out by the smooth muscle walls. Unlike peristalsis, which involves the contraction and relaxation of muscles in one direction, segmentation occurs simultaneously in both directions as the circular muscles alternatively contract. This allows for thorough mixing of intestinal contents, known as chyme, to allow greater absorption.

Arthropod exoskeleton - Segmentation
The arthropod exoskeleton is typically divided into different functional units to allow flexibility in an often otherwise rigid structure

Insect - Segmentation The head is enclosed in a hard, heavily sclerotized, unsegmented, exoskeletal head capsule, or epicranium, which contains most of the sensing organs, including the antennae, ocellus or eyes, and the mouthparts

Insect - Segmentation The thorax is a tagma composed of three sections, the prothorax, mesothorax and the metathorax

Insect - Segmentation The abdomen: is the largest tagma of the insect, which typically consists of 11–12 segments and is less strongly sclerotized than the head or thorax

Segmentation (image processing)
More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.

The result of image segmentation is a set of segments that collectively cover the entire image, or a set of Contour line|contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, luminous intensity|intensity, or Image texture|texture. Adjacent regions are significantly different with respect to the same characteristic(s).

When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes.

Segmentation (image processing) - Applications
** Locate tumors and other pathologies

** Measure tissue volumes

** Diagnosis, study of anatomical structure

* Video surveillance

Several general-purpose algorithms and techniques have been developed for image segmentation. To be useful, these techniques must typically be combined with a domain's specific knowledge in order to effectively solve the domain's segmentation problems.

Segmentation (image processing) - Thresholding
The simplest method of image segmentation is called the Thresholding (image processing)|thresholding method. This method is based on a clip-level (or a threshold value) to turn a gray-scale image into a binary image.

The key of this method is to select the threshold value (or values when multiple-levels are selected). Several popular methods are used in industry including the maximum entropy method, Otsu's method (maximum variance), and k-means clustering.

Recently, methods have been developed for thresholding computed tomography (CT) images

.K J. Batenburg, and J. Sijbers, Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization, IEEE Transactions on Medical Imaging, vol. 28, no. 5, pp , June, 2009 [ Download paper]

Segmentation (image processing) - Clustering methods
The K-means algorithm is an iterative technique that is used to Cluster analysis|partition an image into K clusters. The basic algorithm is:

# Pick K cluster centers, either randomly or based on some heuristic

# Assign each pixel in the image to the cluster that minimizes the distance between the pixel and the cluster center

# Re-compute the cluster centers by averaging all of the pixels in the cluster

# Repeat steps 2 and 3 until convergence is attained (i.e. no pixels change clusters)

In this case, distance is the squared or absolute difference between a pixel and a cluster center

Segmentation (image processing) - Compression-based methods
Compression based methods postulate that the optimal segmentation is the one that minimizes, over all possible segmentations, the coding length of the data.Hossein Mobahi, Shankar Rao, Allen Yang, Shankar Sastry and Yi Ma.

The connection between these two concepts is that segmentation tries to find patterns in an image and any regularity in the image can be used to compress it

# The boundary encoding leverages the fact that regions in natural images tend to have a smooth contour. This prior is used by Huffman coding to encode the difference chain code of the contours in an image. Thus, the smoother a boundary is, the shorter coding length it attains.

# Texture is encoded by lossy compression in a way similar to minimum description length (MDL) principle, but here the length of the data given the model is approximated by the number of samples times the entropy of the model

The distortion in the lossy compression determines the coarseness of the segmentation and its optimal value may differ for each image

Segmentation (image processing) - Histogram-based methods
Histogram-based methods are very efficient when compared to other image segmentation methods because they typically require only one pass through the pixels. In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate the Cluster analysis|clusters in the image. Hue|Color or Brightness|intensity can be used as the measure.

A refinement of this technique is to Recursion (computer science)|recursively apply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This is repeated with smaller and smaller clusters until no more clusters are formed.

One disadvantage of the histogram-seeking method is that it may be difficult to identify significant peaks and valleys in the image.

This approach segments based on active objects and a static environment, resulting in a different type of segmentation useful in Video tracking.

Segmentation (image processing) - Edge detection
Region boundaries and edges are closely related,

since there is often a sharp adjustment in intensity at the region boundaries.

Edge detection techniques have therefore been used as the base of another segmentation technique.

The edges identified by edge detection are often disconnected. To segment an object from an image however, one needs closed region boundaries. The desired edges are the boundaries between such objects.

Li Segmentation and classification of edges using minimum description length approximation and complementary junction cues, Computer Vision and Image Understanding, vol

Segmentation (image processing) - Region-growing methods
The first region-growing method was the seeded region growing method

It starts off with a single region A_1 – the pixel chosen here does not significantly influence final segmentation

One variant of this technique, proposed by Haralick and Shapiro (1985), is based on pixel Brightness|intensities. The Arithmetic mean|mean and scatter of the region and the intensity of the candidate pixel is used to compute a test statistic. If the test statistic is sufficiently small, the pixel is added to the region, and the region’s mean and scatter are recomputed. Otherwise, the pixel is rejected, and is used to form a new region.

A special region-growing method is called \lambda-connected segmentation (see also lambda-connectedness)

Segmentation (image processing) - Split-and-merge methods
Split-and-merge segmentation is based on a quadtree partition of an image. It is sometimes called quadtree segmentation.

Segmentation (image processing) - Split-and-merge methods
Chen, [ The lambda-connected segmentation and the optimal algorithm for split-and-merge segmentation], Chinese J

Segmentation (image processing) - Partial differential equation-based methods
Using a partial differential equation (PDE)-based method and solving the PDE equation by a numerical scheme, one can segment the image

Segmentation (image processing) - Parametric methods
Lagrangian techniques are based on parameterizing the contour according to some sampling strategy and then evolve each element according to image and internal terms

Segmentation (image processing) - Level set methods
The level set method was initially proposed to track moving interfaces by Osher and Sethian in 1988 and has spread across various imaging domains in the late nineties

Segmentation (image processing) - Level set methods
[ Geometric Level Set Methods in Imaging Vision and Graphics], Springer Verlag, ISBN , Furthermore, research into various level set data structures has led to very efficient implementations of this method.

Segmentation (image processing) - Fast marching methods
The fast marching method has been used in image segmentation, and this model has been improved (permitting a both positive and negative speed propagation speed) in an approach called the generalized fast marching method.

Segmentation (image processing) - Graph partitioning methods
3 and minimum spanning tree-based segmentation.C

Segmentation (image processing) - Watershed transformation
The Watershed (algorithm)|watershed transformation considers the gradient magnitude of an image as a topographic surface. Pixels having the highest gradient magnitude intensities (GMIs) correspond to watershed lines, which represent the region boundaries. Water placed on any pixel enclosed by a common watershed line flows downhill to a common local intensity minimum (LIM). Pixels draining to a common minimum form a catch basin, which represents a segment.

Segmentation (image processing) - Model based segmentation
State of the art methods in the literature for knowledge-based segmentation involve active shape and appearance models, active contours and deformable templates and level-set based methods.

Segmentation (image processing) - Multi-scale segmentation
Image segmentations are computed at multiple scales in scale space and sometimes propagated from coarse to fine scales; see scale-space segmentation.

Segmentation (image processing) - Multi-scale segmentation
Segmentation criteria can be arbitrarily complex and may take into account global as well as local criteria. A common requirement is that each region must be connected in some sense.

Segmentation (image processing) - One-dimensional hierarchical signal segmentation
in scale space included the notion that a one-dimensional signal could be unambiguously segmented into regions, with one scale parameter controlling the scale of segmentation.

Segmentation (image processing) - One-dimensional hierarchical signal segmentation
A key observation is that the zero-crossings of the second derivatives (minima and maxima of the first derivative or slope) of multi-scale-smoothed versions of a signal form a nesting tree, which defines hierarchical relations between segments at different scales

Segmentation (image processing) - Image segmentation and primal sketch
There have been numerous research works in this area, out of which a few have now reached a state where they can be applied either with interactive manual intervention (usually with application to medical imaging) or fully automatically. The following is a brief overview of some of the main research ideas that current approaches are based upon.

and Pizer, S.: A multiresolution hierarchical approach to image segmentation based on intensity extrema, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:6, 529–540, 1990.]

Unfortunately, however, the intensity of image features changes over scales, which implies that it is hard to trace coarse-scale image features to finer scales using iso-intensity information.

Lindeberg[ Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention, International Journal of Computer Vision, 11(3), 283–318, 1993.][ Lindeberg, Tony, Scale-Space Theory in Computer Vision, Kluwer Academic Publishers, 1994], ISBN studied the problem of linking local extrema and saddle points over scales, and proposed an image representation called the scale-space primal sketch which makes explicit the relations between structures at different scales, and also makes explicit which image features are stable over large ranges of scale including locally appropriate scales for those

, Dobrzenieck, A., Hermann, N., Kitai, N., Kreiborg, S., Larsen, P., Nielsen, M.: Interactive multi-scale segmentation in clinical use in European Congress of Radiology 2000

Lindeberg (2003) Fully Automatic Segmentation of MRI Brain Images using Probabilistic Anisotropic Diffusion and Multi-Scale Watersheds, Proc

These ideas for multi-scale image segmentation by linking image structures over scales have also been picked up by Florack and Kuijper.Florack, L

Segmentation (image processing) - Semi-automatic segmentation
In this kind of segmentation, the user outlines the region of interest with the mouse clicks and algorithms are applied so that the path that best fits the edge of the image is shown.

Segmentation (image processing) - Semi-automatic segmentation
Techniques like Simple Interactive Object Extraction|SIOX, Livewire Segmentation Technique|Livewire, Intelligent Scissors or IT-SNAPS are used in this kind of segmentation.

Segmentation (image processing) - Trainable segmentation
Most segmentation methods are based only on color information of pixels in the image. Humans use much more knowledge than this when doing image segmentation, but implementing this knowledge would cost considerable computation time and would require a huge domain-knowledge database, which is currently not available. In addition to traditional segmentation methods, there are trainable segmentation methods which can model some of this knowledge.

Neural Network segmentation relies on processing small areas of an image using an artificial neural networkMahinda Pathegama Ö Göl (2004): Edge-end pixel extraction for edge-based image segmentation, Transactions on Engineering, Computing and Technology, vol

Pulse-coupled networks|Pulse-coupled neural networks (PCNNs) are neural models proposed by modeling a cat’s visual cortex and developed for high-performance biomimetic image processing.

Over the past decade, PCNNs have been utilized for a variety of image processing applications, including: image segmentation, feature generation, face extraction, motion detection, region growing, noise reduction, and so on.

The temporal series of pulse outputs contain information of input images and can be utilized for various image processing applications, such as image segmentation and feature generation

'Open-source implementations of trainable segmentation':

* [ Trainable Segmentation Plugin]

* [ IMMI]

Segmentation (image processing) - Segmentation benchmarking
* [ Prague On-line Texture Segmentation Benchmark]Haindl, M. – Mikeš, S. [ Texture Segmentation Benchmark], Proc. of the 19th Int. Conference on Pattern Recognition. IEEE Computer Society, 2008, pp. 1–4 ISBN ISSN

Segmentation (image processing) - Segmentation benchmarking
* [ The Berkeley Segmentation Dataset and Benchmark]

Time series analysis - Segmentation
In time-series segmentation, the goal is to identify the segment boundary points in the time-series, and to characterize the dynamical properties associated with each segment

Market analysis - Market segmentation
To identify and classify the relevant market, a market classification or segmentation has to be done.

Ethernet switch - Microsegmentation
microsegmentation, each device is located on a dedicated switch port

Text segmentation 'Text segmentation' is the process of dividing writing|written text into meaningful units, such as words, Sentence (linguistics)|sentences, or topic (linguistics)|topics

Text segmentation Compare speech segmentation, the process of dividing speech into linguistically meaningful portions.

Text segmentation - Word segmentation
:See also: Word#Word boundaries|Word ref/refref/ref!-- WindowDiff(ref,hyp) 1 \over \sum |b(ref_i,ref_)-b(hyp_i,hyp_)| --

Text segmentation - Other segmentation problems
Processes may be required to segment text into segments besides mentioned, including morphemes (a task usually called morphology (linguistics)|morphological analysis) or paragraphs.

Text segmentation - Automatic segmentation approaches
Automatic segmentation is the problem in natural language processing of implementing a computer process to segment text.

Effective natural language processing systems and text segmentation tools usually operate on text in specific domains and sources

The process of developing text segmentation tools starts with collecting a large corpus of text in an application domain. There are two general approaches:

* Manual analysis of text and writing custom software

* Annotate the sample corpus with boundary information and use Machine Learning

Some text segmentation systems take advantage of any markup like HTML and know document formats like PDF to provide additional evidence for sentence and paragraph boundaries.

Social network analysis - Segmentation
Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as Structural cohesion|structurally cohesive blocks if precision is wanted.

Clustering coefficient: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater 'cliquishness'.

Cohesion: The degree to which actors are connected directly to each other by Social cohesion|cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.

Speech perception - Linearity and the segmentation problem
Although listeners perceive speech as a stream of discrete units (phonemes, syllables, and words), this linearity is difficult to see in the physical speech signal (see Figure 2 for an example). Speech sounds do not strictly follow one another, rather, they overlap. A speech sound is influenced by the ones that precede and the ones that follow. This influence can even be exerted at a distance of two or more segments (and across syllable- and word-boundaries).

Speech perception - Linearity and the segmentation problem
Having disputed the linearity of the speech signal, the problem of segmentation arises: one encounters serious difficulties trying to delimit a stretch of speech signal as belonging to a single perceptual unit. This can be illustrated by the fact that the acoustic properties of the phoneme will depend on the production of the following vowel (because of coarticulation).

Volume rendering - Volume segmentation
By sectioning out large portions of the volume that one considers uninteresting before rendering, the amount of calculations that have to be made by ray casting or texture blending can be significantly reduced. This reduction can be as much as from O(n) to O(log n) for n sequentially indexed voxels. Volume segmentation also has significant performance benefits for other ray tracing algorithms.

Market segment - Behavioural segmentation
Behavioural segmentation divides consumers into groups according to their knowledge of, attitude towards, usage rate or response to a product Fripp, Geoff.[ “Market Segmentation Bases”]Market Segmentation Study Guide

Market segment - Using segmentation in customer retention
;Is this customer at high risk of canceling the company's service?

[ “What is Customer Segmentation?”] MindofMarketing.net, May 2007

;What retention tactics should be used to retain this customer?

For customers who are deemed worthy of saving, it is essential for the company to know which save tactics are most likely to be successful. Tactics commonly used range from providing special customer discounts to sending customers communications that reinforce the value proposition of the given service.

Social mobility - Social science and understanding segmentation
The social definition of groups creates entry and exit barriers that can help explain why social mobility across group boundaries can be difficult

Segmentation (memory)
'Memory segmentation' is the division of a computer's primary memory into 'segments' or 'sections'. In a computer system using segmentation, a reference to a memory location includes a value that identifies a segment and an offset (computer science)|offset within that segment. Segments or sections are also used in object files of compiled programs when they are Linker (computing)|linked together into a program image and when the image is Loader (computing)|loaded into memory.

Segments usually correspond to natural divisions of a program such as individual routines or data tables so segmentation is generally more visible to the programmer than paging alone.

Different segments may be created for different program module (programming)|modules, or for different classes of memory usage such as Code segment|code and data segments. Certain segments may be shared between programs.

Segmentation (memory) - Hardware implementation
In a system using segmentation computer memory addresses consist of a segment id and an offset within the segment.

A hardware memory management unit (MMU) is responsible for translating the segment and offset into a Physical memory#Primary storage|physical memory address, and for performing checks to make sure the translation can be done and that the reference to that segment and offset is permitted.

Each segment has a length and set of permissions (for example, read, write, execute) associated with it. A process (computing)|process is only allowed to make a reference into a segment if the type of reference is allowed by the permissions, and if the offset within the segment is within the range specified by the length of the segment. Otherwise, a hardware exception such as a segmentation fault is raised.

Segments may also be used to implement virtual memory.

Segmentation is one method of implementing memory protection. Page (computer memory)|Paging is another, and they can be combined. The size of a memory segment is generally not fixed and may be as small as a single byte.

Segmentation has been implemented in several different ways on different hardware, with or without paging. The Intel x86 implementation of segments does not fit either model and is discussed separately below.

Segmentation (memory) - Segmentation without paging
An implementation of virtual memory on a system using segmentation without paging requires that entire segments be swapped back and forth between main memory and secondary storage. When a segment is swapped in the operating system has to allocate enough contiguous free memory to hold the entire segment. Often Fragmentation (computing)|memory fragmentation results in there being not enough contiguous memory even though there may be enough in total.

Segmentation (memory) - Segmentation with paging
Instead of an actual memory location the segment information includes the address of a page table for the segment.

When a program references a memory location the offset is translated to a memory address using the page table. A segment can be extended simply by allocating another memory page and adding to to the segment's page table.

An implementation of virtual memory on a system using segmentation with paging usually only moves individual pages back and forth between main memory and secondary storage, similar to a paged non-segmented system. Pages of the segment can be located anywhere in main memory and need not be contiguous. This usually results in less paging input/output and reduced memory fragmentation

Segmentation (memory) - History
The Burroughs Corporation Burroughs large systems|B5000 computer was one of the first to implement segmentation, and perhaps the first commercial computer to provide virtual memory based on segmentation. The later Burroughs large systems#B6500|B6500 computer also implemented segmentation; a version of its architecture is still in use today on the Unisys ClearPath Libra servers.

The GE-600 series|GE-645 computer, a modification of the GE-635 with segmentation and paging support added, was designed in 1964 to support Multics.

The Intel iAPX 432, begun in 1975, attempted to implement a true segmented architecture with memory protection on a microprocessor.

Prime Computer|Prime, Stratus Technologies|Stratus, Apollo Computer|Apollo, IBM System/38, and IBM AS/400 computers use memory segmentation.

Segmentation (memory) - x86 architecture
The memory segmentation used by early x86 processors, beginning with the Intel 8086, does not provide any protection. Any program running on these processors can access any segment with no restrictions. A segment is only identified by its starting location; there is no length checking.

Segmentation in the Intel and later provides protection: with the introduction of the 80286, Intel retroactively named the sole operating mode of the previous x86 CPU models real mode and introduced a new protected mode with protection features

In the x86 implementation of segmentation the segment table, rather than pointing to a page table for the segment, contains the segment address in linear memory. This address is then mapped to a physical address using a separate page table. Unlike other paged implementations of segmentation this prevents segments from dynamically growing in size.

The x86-64 architecture does not use segmentation in long mode (64-bit mode). Four of the segment registers: CS, SS, DS, and ES are forced to 0, and the limit to 264. The segment registers FS and GS can still have a nonzero base address. This allows operating systems to use these segments for special purposes.

Intel x86 - Segmentation Data and/or code could be managed within near 16-bit segments within 64KB portions of the total 1megabyte|MB address space, or a compiler could operate in a far mode using 32-bit segment:offset pairs reaching (only) 1MB

Revenue Management - Segmentation
Market segmentation based upon customer behavior is essential to the next step, which is forecasting demand associated with the clustered segments.

Segmentation violation
In computing, a 'segmentation fault' (often shortened to 'segfault') or 'access violation' is a Fault (computing)|fault raised by hardware with memory protection, notifying an operating system (OS) about a memory access violation; on x86 computers this is a form of general protection fault

Segmentation violation
Segmentation faults have various causes, and are a common problem in programs written in the C (programming language)|C programming language, where they arise primarily due to errors in use of Pointer (computer programming)|pointers for virtual memory addressing, particularly illegal access

Segmentation violation - Overview
A segmentation fault occurs when a program attempts to access a computer memory|memory location that it is not allowed to access, or attempts to access a memory location in a way that is not allowed (for example, attempting to write to a read-only memory|read-only location, or to overwrite part of the operating system).

Thus attempting to read outside of the program's address space, or writing to a read-only segment of the address space, results in a segmentation fault, hence the name.

Note that segmentation faults can also occur independently of page faults: illegal access to a valid page is a segmentation fault, but not an invalid page fault, and segmentation faults can occur in the middle of a page (hence no page fault), for example in a buffer overflow that stays within a page but illegally overwrites memory.

At the hardware level, the fault is initially raised by the memory management unit (MMU) on illegal access (if the referenced memory exists), as part of its memory protection feature, or an invalid page fault (if the referenced memory does not exist). If the problem is not an invalid logical address but instead an invalid physical address, a bus error is raised instead, though these are not always distinguished.

On Unix-like operating systems, a signal called SIGSEGV (abbreviated from segmentation violation) is sent to the offending process

Segmentation violation - Causes
Determining the root cause – debugging the bug – can be simple in some cases, where the program will consistently cause a segmentation fault (e.g., dereferencing a null pointer), while in other cases the bug can be difficult to reproduce and depend on memory allocation on each run (e.g., dereferencing a dangling pointer).

The following are some typical causes of a segmentation fault:

* Dereferencing NULL pointers – this is special-cased by memory management hardware

* Attempting to access a nonexistent memory address (outside process's address space)

* Attempting to access memory the program does not have rights to (such as kernel structures in process context)

* Attempting to write read-only memory (such as code segment)

These in turn are often caused by programming errors that result in invalid memory access:

* Dereferencing or assigning to an uninitialized pointer (wild pointer, which points to a random memory address)

* Attempting to execute a program that does not compile correctly. Some compilers will output an executable file despite the presence of compile-time errors.

In C code, segmentation faults most often occur because of errors in pointer use, particularly in C dynamic memory allocation. Dereferencing a null pointer will always result in a segmentation fault, but wild pointers and dangling pointers point to memory that may or may not exist, and may or may not be readable or writable, and thus can result in transient bugs. For example:

char *p1 = NULL; // Null pointer

Now, dereferencing any of these variables could cause a segmentation fault: dereferencing the null pointer generally will cause a segfault, while reading from the wild pointer may instead result in random data but no segfault, and reading from the dangling pointer may result in valid data for a while, and then random data as it is overwritten.

Segmentation violation - Handling
The default action for a segmentation fault or bus error is abnormal termination of the process that triggered it. A core file may be generated to aid debugging, and other platform-dependent actions may also be performed. For example, Linux systems using the grsecurity patch may log SIGSEGV signals in order to monitor for possible intrusion attempts using buffer overflows.

Segmentation violation - Writing to read-only memory
Writing to read-only memory raises a segmentation fault. At the level of code errors, this occurs when the program writes to part of its own code segment or the read-only portion of the data segment, as these are loaded by the OS into read-only memory.

Here is an example of ANSI C code that will generally cause a segmentation fault on platforms with memory protection. Note that modifying a string literal is undefined behavior according to the ANSI C standard, but most compilers will not catch this at compile time, and instead compile this to executable code that will crash:

Program received signal SIGSEGV, Segmentation fault.

Note that in this case the code can be corrected by using an array instead of a character pointer, as this allocates memory on stack and initializes it to the value of the string literal:

Lastly, note that even though string literals cannot be modified (rather, this has undefined behavior in the C standard), in C they are of char * type, so there is no implicit conversion in the original code, while in C++ they are of const char * type, and thus there is an implicit conversion, so compilers will generally catch this particular error.

Segmentation violation - Null pointer dereference
Because a very common program error is a null pointer dereference (a read or write through a null pointer, used in C to mean pointer to no object and as an error indicator), most operating systems map the null pointer's address such that accessing it causes a segmentation fault.

This sample code creates a null pointer, and then tries to access its value (read the value). Doing so causes a segmentation fault at runtime on many operating systems.

Dereferencing a null pointer and then assigning to it (writing a value to a non-existent target) also usually causes a segmentation fault:

Note that the following code includes a null pointer dereference, but when compiled will often not result in a segmentation fault, as the value is unused and thus the dereference will often be optimized away by dead code elimination:

Segmentation violation - Stack overflow
Another example is recursion without a base case:

Segmentation violation - Stack overflow
which causes the stack overflow|stack to overflow which results in a segmentation fault.[ What is the difference between a segmentation fault and a stack overflow?] at Stack Overflow Note that infinite recursion may not necessarily result in a stack overflow depending on the language, optimizations performed by the compiler and the exact structure of a code

Marketing campaign - Market segmentation
In another example, Sun Microsystems can use market segmentation to classify its clients according to their promptness to adopt new products.D.S

Marketing campaign - Market segmentation
Market segmentation can be viewed as a key dynamic in interpreting and executing a logical perspective of Strategic Marketing Planning. The manifestation of this process is considered by many traditional thinkers to include the following;'S'egmenting, 'T'argeting and 'P'ositioning.

Insight Segmentation and Registration Toolkit
Segmentation is the process of identifying and classifying data found in a digitally sampled representation

The toolkit provides leading-edge segmentation and registration algorithms in two, three, and more dimensions

Because ITK is an open-source project, developers from around the world can use, debug, maintain, and extend the software

Insight Segmentation and Registration Toolkit - Origins
In 1999 the US National Library of Medicine of the National Institutes of Health awarded a three-year contract to develop an open-source registration and segmentation toolkit, which eventually came to be known as the Insight Toolkit (ITK)

Insight Segmentation and Registration Toolkit - Technical details
Segmentation is the process of identifying and classifying data found in a digitally sampled representation

Insight Segmentation and Registration Toolkit - Technical details
ITK is implemented in C++. ITK is cross-platform, using the CMake build environment to manage the compilation process. In addition, an automated wrapping process generates interfaces between C++ and interpreted programming languages such as Java and Python. This enables developers to create software using a variety of programming languages. ITK's implementation employs the technique of generic programming through the use of C++ templates.

Insight Segmentation and Registration Toolkit - Developers and Contributors
* University of Utah

* Columbia University

After its inception the software continued growing with contributions from other institutions including

* Stanford University

Insight Segmentation and Registration Toolkit - Funding
The funding for the project is from the National Library of Medicine at the National Institutes of Health. NLM in turn was supported by member institutions of NIH (see sponsors).

The goals for the project include the following:

* Support the Visible Human Project.

* Establish a foundation for future research.

* Create a repository of fundamental algorithms.

* Support commercial application of the technology.

* Create conventions for future work.

* Grow a self-sustaining community of software users and developers.

The source code of the Insight Toolkit is distributed under an Apache 2 License (as approved by the Open Source Initiative)

The philosophy of Open Source of the Insight Toolkit was extended to support Open Science, in particular by providing Open Access to publications in the domain of Medical Image Processing. These publications are made freely available through the Insight Journal

Insight Segmentation and Registration Toolkit - Community Participation
Because ITK is an open-source system, anybody can make contributions to the project. A person interested in contributing to ITK can take the following actions

# Read this document and especially the ITK Software Guide. (This book can be purchased from Kitware's store.)

# Obtain access to [ ITK's Gerrit Code Review instance].

Anyone can submit a patch, and write access to the repository is not necessary to get a patch merged or retain authorship credit. For more information, see the [ ITK Bar Camp documentation on how to submit a patch].

Insight Segmentation and Registration Toolkit - Copyright and License
ITK is copyrighted by the Insight Software Consortium, a non-profit alliance of organizations and individuals interested in supporting ITK

Insight Segmentation and Registration Toolkit - Copyright and License
The licensed was changed to Apache 2.0 with version 4.0 to adopt a modern license with patent protection provisions. From version 3.6 to 3.20, a simplified BSD license was used. Versions of ITK previous to ITK 3.6 were distributed under a modified BSD License. The main motivation for adopting a BSD license starting with ITK 3.6, was to have an Open Source Initiative|OSI-approved license.

Insight Segmentation and Registration Toolkit - Technical Summary
The following sections summarize the technical features of the NLM's Insight ITK toolkit.

The following are key features of the toolkit design philosophy.

* The toolkit provides data representation and algorithms for performing segmentation and registration. The focus is on medical applications; although the toolkit is capable of processing other data types.

* The toolkit provides data representations in general form for images (arbitrary dimension) and (unstructured) meshes.

* The toolkit does not address visualization or graphical user interface. These are left to other toolkits (such as VTK, VisPack, 3DViewnix, MetaImage, etc.)

* Multi-threaded (shared memory) parallel processing is supported.

* The development of the toolkit is based on principles of extreme programming. That is, design, implementation, and testing is performed in a rapid, iterative process. Testing forms the core of this process. In Insight, testing is performed continuously as files are checked in, and every night across multiple platforms and compilers. The ITK testing dashboard, where testing results are posted, is central to this process.

Insight Segmentation and Registration Toolkit - Architecture
* The toolkit is organized around a data-flow architecture. That is, data is represented using data objects which are in turn processed by process objects (filters). Data objects and process objects are connected together into pipelines. Pipelines are capable of processing the data in pieces according to a user-specified memory limit set on the pipeline.

* Object factories are used to instantiate objects. Factories allow run-time extension of the system.

* A command/observer design pattern is used for event processing.

Insight Segmentation and Registration Toolkit - Implementation Philosophy
* The toolkit is implemented using generic programming principles. Such heavily templated C++ code challenges many compilers; hence development was carried out with the latest versions of the MSVC, Sun, gcc, Intel, and SGI compilers.

* The toolkit is cross-platform (Unix, Windows and Mac OS X).
Insight Segmentation and Registration Toolkit - Implementation Philosophy * The toolkit is cross-platform (Unix, Windows and Mac OS X).

* The toolkit supports multiple language bindings, including such languages as Tcl, Python, and Java. These bindings are generated automatically using an auto-wrap process.

* The memory model depends on smart pointers that maintain a reference count to objects. Smart pointers can be allocated on the stack, and when scope is exited, the smart pointers disappear and decrement their reference count to the object that they refer to.

Insight Segmentation and Registration Toolkit - Build Environment
ITK uses the CMake (cross-platform make) build environment. CMake is an operating system and compiler independent build process that produces native build files appropriate to the OS and compiler that it is run with. On Unix CMake produces makefiles and on Windows CMake generates projects and workspaces.

Insight Segmentation and Registration Toolkit - Testing Environment
ITK supports an extensive testing environment. The code is tested daily (and even continuously) on many hardware/operating system/compiler combinations and the results are posted daily on the ITK testing dashboard. We use Dart to manage the testing process, and to post the results to the dashboard.

Insight Segmentation and Registration Toolkit - Background References: C++ Patterns and Generics
ITK uses many advanced design patterns and generic programming. You may find these references useful in understanding the design and syntax of Insight.

Insight Segmentation and Registration Toolkit - Background References: C++ Patterns and Generics
* Generic Programming and the Stl : Using and Extending the C++ Standard Template Library (Addison-Wesley Professional Computing Series) by Matthew H. Austern

Insight Segmentation and Registration Toolkit - Resources
A number of resources are available to learn more about ITK.

* Users and developers alike should read the [ ITK Software Guide]

* Tutorials are available at

* Developers, or users interested in contributing code, should look in the document Insight/Documentation/InsightDeveloperStart.pdf or InsightDeveloperStart.doc found in the source code distribution.

* Developers should also look at the ITK style guide Insight/Documentation/Style.pdf found in the source distribution.

Insight Segmentation and Registration Toolkit - Applications
A great way to learn about ITK is to see how it is used. There are four places to find applications of ITK.

# The Insight/Examples/ source code examples distributed with ITK. The source code is available. In addition, it is heavily commented and works in combination with the ITK Software Guide.

# The separate InsightApplications checkout.

# The [ Applications web pages]. These are extensive descriptions, with images and references, of the examples found in #1 above.

# The testing directories distributed with ITK are simple, mainly undocumented examples of how to use the code.

In 2004 ITK-SNAP ([ website]) was developed from SNAP and became a popular free segmentation software using ITK and having a nice and simple user interface.

Insight Segmentation and Registration Toolkit - Data
* Data is available via anonymous ftp from:

Department store - Segmentation
* Upscale:Barneys New York, Bergdorf Goodman, Bloomingdale's, Dillard's Saks Fifth Avenue, Neiman Marcus, Nordstrom, Lord Taylor, Von Maur

* Middle-market: Macy's, Carson Pirie Scott, Century 21 (department store)

* Discount department stores: Target Corporation|Target, Kmart, Wal-Mart, Meijer

* T.J. Maxx, Marshalls, Ross, and Burlington Coat Factory are stores that sell designer goods at lower prices, often on a surplus basis.

Stores that carry a general line (merchandise)|general line of groceries and other product lines similar to those of department stores are considered warehouse clubs or supercenters. Warehouse clubs require a nominal annual membership fee, while supercenters do not. Costco, BJ's Wholesale Club, and Sam's Club are examples of warehouse clubs.

Market segments - Behavioral segmentation
Behavioral segmentation divides consumers into groups according to their knowledge of, attitude towards, usage rate or response to a productFripp, Geoff.[ “Market Segmentation Bases”] Market Segmentation Study Guide

Medical Image Computing - Segmentation
Although there are many Segmentation (image processing)|computer vision techniques for image segmentation, some have been adapted specifically for medical image computing

Methods of this style are typically referred to as atlas-based segmentation methods

* 'Shape-Based Segmentation': Many methods parametrize a template shape for a given structure, often relying on control points along the boundary. The entire shape is then deformed to match a new image. Two of the most common shape-based techniques are Active Shape Models and Active Appearance Models. These methods have been very influential, and have given rise to similar models.

Recently, principles from feedback control theory have been incorporated into segmentation, which give the user much greater flexibility and allow for the automatic correction of errors.

Labor market segmentation
A labor market is seen as 'segmented' if it consists of various sub-groups with little or no crossover capability. Segmentation can result in different groups, for example men and women, receiving different wages for the same work. The 19th-century Irish political economist John Elliott Cairnes referred to this phenomenon as that of noncompeting groups.

Labor market segmentation
A similar, almost synonymous concept is that of a dual labour market (DLM). However, as the word dual implies, a DLM usually refers to two parallel markets, whilst segmentation in the broadest sense may involve several labor markets.

Labor market segmentation - Overview
The theory of 'labor market segmentation' contrasts to the views of neo-classical economic theory, which posits the existence of a unified market for labor, consisting of buyers and sellers in open competition with each other

Labor market segmentation - Overview
[

Labor market segmentation - Historical background
The labor-market segmentation theory revolves around the identification of a split between two analytic divisions in the economy and the labor-market.

Labor market segmentation - The primary sector
In a primary sector the workforce as a whole is motivated to serve their employer because of wages, health benefit, and pension and job security

Labor market segmentation - The secondary sector
In a secondary sector, job management is entitled to complete control because there is a larger turnout

Labor market segmentation - Theoretical explanation
This model of the labor market segmentation has been developed over the years to accommodate the fact that different job professionals work in completely different job markets

The two markets are connected, with movement between them at specified ports of entry and exit

The concept of [ Globally Segmented Labor Markets] by John Asimakopoulos.[ Critical Sociology] argues within a Marxist political economy framework using Social Structures of Accumulation theory, developed by American economist David Gordon (economist)|David Gordon, that neoliberal globalization has expanded labor market segmentation internationally

Labor market segmentation - Labor market segmentation debates and propositions
**An alternative position: Total incomes vary largely because of variation in capital incomes and other non-labor income sources.

*Employment is important primarily as a means of raising incomes and thereby reducing poverty.

**An alternative position: The goal of policy is employment maximization or unemployment minimization.

*There are multiple labor markets.
Labor market segmentation - Labor market segmentation debates and propositions *There are multiple labor markets.

*The various labor markets are linked to one another.
Labor market segmentation - Labor market segmentation debates and propositions *The various labor markets are linked to one another.

*Workers maximize utility. In low-income countries, utility can usefully be thought of as a function of income alone, although sometimes utility is a function of working conditions as well as income.

**An alternative position: No single unified purpose guides workers’ behavior.

**An alternative position: Firms exist to serve the interests of multiple stakeholders, of whom workers are one.

*The number of good jobs is limited. Workers who take up bad jobs do so in preference to unemployment.

**An alternative position: The number of good jobs is not limited. Each worker is doing the type of job that maximizes his/her utility.

*Open unemployment is less important a problem than is working poverty.

**An alternative position: Unemployment is the main labor market problem [

Workers who are in the similar trade are treated differently according to the sector they are placed in. The differences in the labor market segmentation imply differences in the way which they are trained, allocated, organized and paid. This has to be directly related to the way in which the labor process is organized. The two sectors organize the skills and education credentials.The Structuring of Labor Markets

All employers should be able to receive the same employment standards examples, at least minimum wages, maximum hour laws, health laws etc. regardless of what sector they belong to. The management will not have complete control, there should be employment standards for all employees that allows them to monitor occupational safety, security, health safety and other required standards such as minimum wage requirement, maximum hour laws etc.Labor Markets and Integrating National Economies

Ambiguous image - Texture segmentation and figure-ground assignments
Texture segmentation rules often both cooperate and compete with each other, and examining the texture can yield information about the layers of the image, disambiguating the background, foreground, and the object.Tang, X.A model for figure-ground segmentation by self-organized cue integration

Intel Segmentation Data and code could be managed within near 16-bit segments within 64KB portions of the total 1megabyte|MB address space, or a compiler could operate in a far mode using 32-bit segment:offset pairs reaching (only) 1MB

Enhancer (genetics) - Enhancers in Invertebrate Segmentation
In the early fly embryo, the gap gene transcription factors are responsible for activating and repressing a number of segmentation genes, such as the pair rule genes

Chelicerata - Segmentation and cuticle
The Chelicerata are arthropods as they have: Segmentation (biology)|segmented bodies with jointed limbs, all covered in a cuticle made of chitin and proteins; heads that are composed of several segments that fuse during the development of the embryo; a much reduced coelom; a hemocoel through which the blood circulates, driven by a tube-like heart

The cephalothorax is formed in the embryo by fusion of the prostomium|acron, which carries the eyes, with segments two to seven, which all have paired appendages, while segment one is lost during the embryo's development

The abdomen consists of twelve or fewer segments that originally formed two groups, a preabdomen or mesoma of seven segments and a postabdomen or metasoma of five, terminating with a telson or spike. The abdominal appendages of modern chelicerates are missing or heavily modified – for example in spiders the remaining appendages form spinneret (spider)|spinnerets that extrude spider silk|silk, while those of horseshoe crabs (Xiphosura) form gills.

Like all arthropods, chelicerates' bodies and appendages are covered with a tough cuticle made mainly of chitin and chemically hardened proteins. Since this cannot stretch, the animals must molt to grow. In other words, they grow new but still soft cuticles, then cast off the old one and wait for the new one to harden. Until the new cuticle hardens the animals are defenseless and almost immobilized.

Price discrimination - Segmentation by age group and student status
Many movie theaters, amusement parks, tourist attractions, and other places have different admission prices per market segment: typical groupings are Youth, Student, Adult, and Senior. Each of these groups typically have a much different demand curve. Children, people living on student wages, and people living on retirement generally have much less disposable income.

ATM Adaptation Layer 5 - Convergence, segmentation, and reassembly
The process of dividing a block of data into cells and regrouping them is known as ATM segmentation and reassembly (SAR).

By separating the functions of segmentation and reassembly from cell transport, AAL5 follows the layering principle

The AAL5 on the receiving side knows how many cells comprise a packet because the sending AAL5 uses the low-order bit of the PAYLOAD TYPE field of the ATM cell header to mark the final cell in a packet

Network segmentation 'Network segmentation' in computer networking is the act or profession of splitting a computer network into subnetworks, each being a network segment or network layer. Advantages of such splitting are primarily for boosting performance and improving security.

Network segmentation - Advantages
* 'Reduced congestion': Improved performance is achieved because on a segmented network there are fewer hosts per subnetwork, thus minimizing local traffic

* 'Improved security': Broadcasts will be contained to local network. Internal network structure will not be visible from outside

* 'Containing network problems': Limiting the effect of local failures on other parts of network

Shopper marketing - Market segmentation|Segmenting shoppers
When conducting shopper segmenting, the market is divided into essential and measurable groups, that is, segments on the basis of the buying behaviour data. Shopper segmenting makes it easier to answer the requirements of individual segments. For example, price-sensitive and traditional shoppers clearly differ from one another as far as their buying behaviour is concerned. Segmenting makes it possible to target marketing measures at the most profitable shoppers.

Annelida - Segmentation
Most of an annelid's body consists of segments that are practically identical, having the same sets of internal organs and external chaetae (Greek χαιτη, meaning hair) and, in some species, appendages

The segments develop one at a time from a growth zone just ahead of the pygidium, so that an annelid's youngest segment is just in front of the growth zone while the peristomium is the oldest. This pattern is called teloblast|teloblastic growth. Some groups of annelids, including all leeches, have fixed maximum numbers of segments, while others add segments throughout their lives.

The phylum's name is derived from the Latin word annelus, meaning little ring.

Image segmentation - Applications
** Virtual surgery simulation

Image segmentation - Applications
** Intra-surgery navigation

Image segmentation - Clustering methods
The K-means algorithm is an iterative technique that is used to Cluster analysis|partition an image into K clusters.Barghout, Lauren, and Jacob Sheynin. Real-world scene perception and perceptual organization: Lessons from Computer Vision. Journal of Vision 13.9 (2013): The basic algorithm is

Image segmentation - Edge detection
Visual Taxometric approach Image Segmentation using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions

Image segmentation - Region-growing methods
Region-growing methods mainly rely on the assumption that the neighboring pixels within one region have similar values. The common procedure is to compare one pixel with its neighbors. If a similarity criterion is satisfied, the pixel can be set belong to the cluster as one or more of its neighbors. The selection of the similarity criterion is significant and the results are influenced by noise in all instances.

The method of Statistical Region MergingR. Nock and F. Nielsen, Statistical Region Merging, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26, No 11, pp , (SRM) starts by building the graph of pixels using the 4-connectedness with edges weighted by the absolute value of the intensity difference. Initially each pixel forms a single pixel region.

SRM then sorts those edges in a priority queue and decide whether to merge or not the current regions belonging to the edge pixels using a statistical predicate.

Because Seeded region growing requires seeds as additional input, the segmentation results are dependent on the choice of seeds, and noise in the image can cause the seeds to be poorly placed.

It starts off with a single region A_1 – the pixel chosen here does not significantly influence final segmentation

Image segmentation - Partial differential equation-based methods
Using a partial differential equation (PDE)-based method and solving the PDE equation by a numerical scheme, one can segment the image.V

Image segmentation - Semi-automatic segmentation
In one kind of segmentation, the user outlines the region of interest with the mouse clicks and algorithms are applied so that the path that best fits the edge of the image is shown.

Image segmentation - Semi-automatic segmentation
Visual Taxometric Approach to Image Segmentation using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions

Labor-market segmentation - Overview
The theory of 'labor market segmentation' contrasts with the view of neo-classical economic theory, which posits the existence of a unified market for labor, consisting of buyers and sellers in open competition with each other

Labor-market segmentation - Theoretical explanation
The primary sector and secondary sector, both these sectors possess different wages and each employment characteristics are different.[ The two markets are connected, with movement between them at specified ports of entry and exit

Labor-market segmentation - Theoretical explanation
The concept of [ Globally Segmented Labor Markets] by John Asimakopoulos.[ Critical Sociology] argues within a Marxist political economy framework using Social Structures of Accumulation theory, developed by American economist David Gordon (economist)|David Gordon, that neoliberal globalization has expanded labor market segmentation internationally

Tehran Stock Exchange - Market segmentation
Besides the Main and Secondary Market, there is a Corporate Banking in Iran#Bond market|participation certificates Market (corporate bonds)

Segmentation *Packet segmentation, the process of dividing a data packet into smaller units for transmission over a data communications network.

*Memory segmentation, the division of computer memory into segments

Segmentation *Network segmentation, the splitting of a computer network into subnetworks, each being a network segment or network layer

*In natural language processing:
Segmentation *In natural language processing:

**Speech segmentation, the identification of word boundaries in speech

Segmentation **Text segmentation, the identification of lexical units in writing systems

Segmentation (biology)
Segmentation of the body plan is important for allowing different regions of the body to develop differentially for different uses.

Segmentation (biology) - Definition
Segmentation is a difficult process to satisfactorily define. Many taxa (for instance the molluscs) have some form of serial repetition in their units, but are not conventionally thought of as segmented. Truly segmented animals would be considered to have organs that were repeated, or having a body composed of self-similar units, but talking of a segmented organism is vague: rather, one should refer to which parts of the organism are segmented.

Segmentation (biology) - Animals
Segmentation in animals typically falls into three types characteristic of the different phylum|phyla: Arthropoda, Vertebrata, and Annelida

Segmentation (biology) - Arthropods: Drosophila
Although Drosophila segmentation is not representative of the arthropod phylum in general, it is the most highly studied. The drosophila as a model organism is ideal for genetic screens. Early screens to identify genes involved in cuticle development led to the discovery of a class of genes that was necessary for proper segmentation of the drosophila embryo.

To properly segment the drosophila embryo, the anterior-Posterior (anatomy)|posterior axis is defined by maternally supplied transcripts giving rise to gradients of these proteins

Within the arthropods, the body wall, nervous system, kidneys, muscles and body cavity are segmented, as are the appendages (when they are present). Some of these elements (e.g. musculature) are not segmented in their sister taxon, the onychophora.

Segmentation (biology) - Annelids: Leech
Segmentation appears to be regulated by the gene Hedgehog (gene)|Hedgehog, suggesting its common evolutionary origin in the ancestor of arthropods and annelids.

Segmentation (biology) - Annelids: Leech
Within the annelids, as with the arthropods, the body wall, nervous system, kidneys, muscles and body cavity are generally segmented. However, this is not true for all of the traits all of the time: many lack segmentation in the body wall, coelom and musculature.

Segmentation (biology) - Chordates: Zebrafish
Although perhaps not as well studied as Drosophila, segmentation in zebrafish is actively studied. The process is similar in zebrafish and other chordates, such as the chicken and the mouse. Segmentation in chordates is characterized as the formation of a pair of somites on either side of the midline. This is often referred to as somitogenesis.

Segmentation (biology) - Chordates: Zebrafish
In the zebrafish, segmentation is coordinated by the “clock and wavefront.” The “clock” refers to the periodic oscillation of specific genes, such as Her1, a hairy/Enhancer of split- gene

Segmentation (biology) - Other taxa
In other taxa, there is some evidence of segmentation in some organs, but this segmentation is not pervasive to the full list of organs mentioned above for arthropods and annelids. One might think of the serially repeated units in many Cycloneuralia, or the segmented body armature of the chitons (which is not accompanied by a segmented coelom).

Segmentation (biology) - Origin
Segmentation can be seen as originating in two ways

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|format=PDF |doi= /S (03) |title=Miniaturized imaging systems

| author = Völkel, R., Eisner, M., and Weible, K. J. |journal=Microelectronic Engineering

| contribution=Reproduction and LifeCycles in Invertebrates |author=Olive, P.J.W.

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Segmentation contractions
Unlike peristalsis, segmentation actually can slow progression of chyme through the system.

Packet segmentation In data communications networks, 'packet segmentation' is the process of dividing a data packet into smaller units for transmission over the network. Packet segmentation happens at layer four of the OSI model or the transport layer. Segmentation may be required when:

Packet segmentation * The data packet is larger than the maximum transmission unit supported by the network

Packet segmentation * The network is unreliable and it's desirable to divide the information into smaller segments to maximize the probability that each one of them can be delivered correctly to the destination

Packet segmentation Protocols that perform packet segmentation at the source usually include a mechanism at the destination to reverse the process and reassemble the original packet from individual segments. This process may include automatic repeat-request (ARQ) mechanisms to detect missing segments and to request the source to re-transmit specific segments.

Packet segmentation In a communication system based on a layered OSI model, packet segmentation may be responsible for splitting one MPDU into multiple physical layer service data units so that reliable transmission (and potential re-transmission via ARQ) of each one can be performed individually.

Packet segmentation The ITU-T G.hn standard, which provides a way to create a high-speed (up to 1 gigabit/s) local area network using existing home wiring (Power line communication|power lines, phone lines and Ethernet over coax|coaxial cables), is an example of a protocol that employs packet segmentation to increase reliability over noisy media.

Puerto Ricans in the United States - Segmentation
These shifts in the relative sizes of Latino populations have also changed the role of the stateside Puerto Rican community.De Genova and Ramos-Zayas 2003 Thus, many long-established Puerto Rican institutions have had to revise their missions (and, in some cases, change their names) to provide services and advocacy on behalf of non-Puerto Rican Latinos

Motel - Market segmentation
In the 1970s and 1980s, independent motels were losing ground to chains such as Motel 6 and Ramada, existing roadside locations were increasingly bypassed by freeways, and the development of the motel chain led to a blurring of motel and hotel.

While family-owned motels with as few as five rooms could still be found, especially along older highways, these were forced to compete with a proliferation of Hotel#Economy and limited service|Economy Limited Service chains. ELS hotels typically do not offer cooked food or mixed drinks; they may offer a very limited selection of continental breakfast foods but have no restaurant, bar, or room service.

Journey's End Corporation (founded 1978 in Belleville, Ontario), built two-story hotel buildings with no on-site amenities to compete directly in price with existing motels

New limited-service brands from existing franchisors provided market segmentation; by using a different trademark and branding, major hotel chains could build new limited-service properties near airports and freeways without undermining their existing mid-price brands

By the 1990s, Motel 6 and Super 8 Worldwide|Super 8 were built with inside corridors (so were nominally hotels) while other former motel brands (including Ramada and Holiday Inn) had become mid-price hotel chains. Some individual franchisees built new hotels with modern amenities alongside or in place of their former Holiday Inn motels; by 2010 a mid-range hotel with an indoor pool was the standard required to remain a Holiday Inn hotel.

Bioimage informatics - Segmentation
Image segmentation|Segmentation of cells is an important sub-problem in many of the fields below (and sometimes useful on its own if the goal is only to obtain a cell count in a viability assay. The goal is to identify the boundaries of cells in a multi-cell image. This allows for processing each cell individually to measure parameters. In 3D data, segmentation must be performed in 3D space.

As the imaging of a nuclear marker is common across many images, a widely used protocol is to segment the nuclei. This can be useful by itself if nuclear measurements are needed or it can serve to seed a Watershed (image processing)|watershed which extends the segmentation to the whole image.

All major segmentation methods have been reported on cell images, from simple Thresholding (image processing)|thresholding to level set methods. Because there are multiple image modalities and different cell types, each of which implies different tradeoffs, there is no single accepted solution for this problem.

Simultaneous segmentation and recognition of cells has also been proposed as a more accurate solution for this problem when an atlas or other prior information of cells is available

Speech segmentation 'Speech segmentation' is the process of identifying the boundaries between words, syllables, or phonemes in spoken natural languages. The term applies both to the human mind|mental processes used by humans, and to artificial processes of natural language processing.

Speech segmentation Though it seems that coarticulation - a phenomenon which may happen between adjacent words just as easily as within a single word - presents the main challenge in speech segmentation across languages, some other problems and strategies employed in solving those problems can be seen in the following sections.

Speech segmentation However, even for those languages, text segmentation is often much easier than speech segmentation, because the written language usually has little interference between adjacent words, and often contains additional clues not present in speech (such as the use of kanji|Chinese characters for word stems in Japanese).

Speech segmentation - Lexical recognition
In natural languages, the meaning of a complex spoken sentence can be understood by decomposing it into smaller lexical segments (roughly, the words of the language), associating a meaning to each segment, and combining those meanings according to the grammar rules of the language.

Though lexical recognition is not thought to be used by infants in their first year, due to their highly limited vocabularies, it is one of the major processes involved in speech segmentation for adults

To give an example, in a whole-word model, the word cats might be stored and searched for by letter, first c, then ca, cat, and finally cats

Though proponents of the decompositional model recognize that a morpheme-by-morpheme analysis may require significantly more computation, they argue that the unpacking of morphological information is necessary for other processes (such as syntactic structure) which may occur parallel to lexical searches.

As a whole, research into systems of human lexical recognition is limited due to little experimental evidence that fully discriminates between the three main models.Badecker, William and Mark Allen. Morphological Parsing and the Perception of Lexical Identity: A Masked Priming Study of Stem Homographs. Journal of Memory and Language 47.1 (2002): Apr

In any case, lexical recognition likely contributes significantly to speech segmentation through the contextual clues it provides, given that it is a heavily probabilistic system - based on the statistical likelihood of certain words or constituents occurring together

As this example shows, proper lexical segmentation depends on context and semantics which draws on the whole of human knowledge and experience, and would thus require advanced pattern recognition and artificial intelligence technologies to be implemented on a computer.

Automatic segmentation and alignment software is commercially available.

Speech segmentation - Phonotactic cues
For most spoken languages, the boundaries between lexical units are difficult to identify; phonotactics are one answer to this issue

The notion that speech is produced like writing, as a sequence of distinct vowels and consonants, may be a relic of alphabetic heritage for some language communities

From a decompositional perspective, in many cases, phonotactics play a part in letting speakers know where to draw word boundaries

Morphological Parsing and the Use of Segmentation Cues in Reading Finnish Compounds

Still, there are instances where phonotactics may not aid in segmentation. Words with unclear clusters or uncontrasted vowel harmony as in opinto/uudistus ('student reform') do not offer phonotactic clues as to how they are segmented.

From the perspective of the whole-word model, however, these words are thought be stored as full words, so the constituent parts wouldn't necessarily be relevant to lexical recognition.

Speech segmentation - Speech segmentation in infants and non-natives
Between 6 and 9 months, infants begin to lose the ability to discriminate between sounds not present in their native language and grow sensitive to the sound structure of their native language, with the word segmentation abilities appearing around 7.5 months.

Word Segmentation by 8-Month-Olds: When Speech Cues Count More Than Statistics

Cross-Language Differences in Cue Use for Speech Segmentation

Online charging system - Based on customer segmentation
As market competition heats up, end users can be segmented not only according to their ages, occupations and income, but also according to cultures, nationalities and behavioral patterns and so on. The operator can then accurately work out differentiated pricing policies based on customer segmentation.

Bi-level image - Image segmentation
Edge detection also often creates a binary image with some pixels assigned to edge pixels, and is also a first step in further segmentation.

Segmentation and Reassembly
'Segmentation and Reassembly' is the process used to fragment and reassemble variable length Network_packet|packets into fixed length cells so as to allow them to be transported across Asynchronous Transfer Mode networks or other cell based infrastructures. Since ATM's payload is only 48 bytes, nearly every packet from any other protocol has to be processed in this way. Thus, it is an essential process for any ATM node. It is usually handled by a dedicated chip, called the 'SAR'.

The process is conceptually simple: an incoming packet from another protocol to be transmitted across the ATM network is chopped up into segments that fit into 48-byte chunks carried as ATM cell payloads. At the far end, these chunks are fitted back together to reconstitute the original packet.

The process is analogous to the fragmentation of IP packets on reaching an interface with a Maximum Transmit Unit (MTU (networking)|MTU) size less than the packet size and the subsequent reassembly of the original packet once the fragments have reached the original packet's destination.

Since different types of data are encapsulated in different ways, the details of the segmentation process vary according to the type of data being handled. There are several different schemes, referred to as ATM Adaptation Layers (AAL). The schemes are:

*AAL1 – Constant bitrate, circuit emulation (T1, E1, etc.)

*AAL2 – Variable bitrate synchronous traffic, eous traffic, e.g. Frame Relay transport

*AAL5 – Used for most data traffic, such as IP

Department stores - Segmentation
* Upscale:Barneys New York, Bergdorf Goodman, Lord Taylor, Bloomingdale's, Dillard's Saks Fifth Avenue, Neiman Marcus, Nordstrom,

Antigenic shift - Segmentation
Antigenic shift occurs because the genome of the virus is segmented, allowing for dramatic genetic changes of type by re-assortment of its segmented RNA genome. A mnemonic to remember segmented viruses is BOAR: Bunyaviridae, Orthomyxoviridae, Arenavirus and Reoviridae.

Beginning of human personhood - Segmentation
For fourteen to twenty-one days after fertilization, an embryo may segment and form twins, triplets, etc. Some argue that an early embryo cannot be a person because If every person is an individual, one cannot be divided from oneself.

Beginning of human personhood - Segmentation
However, Fr

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