Ppt on edge detection image

- photometric aspects of image formation gray level images

,10,10,11,11,11,99 CS223, Jana Kosecka Image Features Local, meaningful, detectable parts of the image. We will look at edges and corners CS223, Jana Kosecka Image Features – Edges, Corners Look for detectable, meaningful parts of the image Edges are detected at places where the image values exhibit sharp variation Gray value column column Edge Edge CS223, Jana Kosecka Edge detection (1D) F(x) Edge= sharp variation x F ’(x) Large first derivative x/


The Segmentation Problem

) Laplacian Of Gaussian The Laplacian of Gaussian (or Mexican hat) filter uses the Gaussian for noise removal and the Laplacian for edge detection Images taken from Gonzalez & Woods, Digital Image Processing (2002) Laplacian Of Gaussian Example Images taken from Gonzalez & Woods, Digital Image Processing (2002) Edge Linking and Boundary Detection Local Processing Global Processing Graph-Theoretic Techniques Local Processing Edge Linking using gradient and gradient direction. Graph-Theoretic Techniques


Machinen Vision and Dig. Image Analysis 1 Prof. Heikki Kälviäinen CT50A6100 Lectures 8&9: Image Segmentation Professor Heikki Kälviäinen Machine Vision.

selection => feature extraction => feature vector. Challenging question: What details? Machinen Vision and Dig. Image Analysis Prof. Heikki Kälviäinen CT50A6100 4 Detection of discontinuities Point detection. Line detection. Edge detection. –Basic formulation. –Gradient operators. –Laplace operators. Corner detection. Combined detection. Machinen Vision and Dig. Image Analysis Prof. Heikki Kälviäinen CT50A6100 5 Point detection Detection of isolated points: a) Apply the mask -1 -1 -1 -1 8 -1 -1/


Chapter Three: imgproc module Image Processing Part I Xinwen Fu

. By Dr. Xinwen Fu Laplacian Operator From the explanation above, we deduce that the second derivative can be used to detect edges. Since images are “2D”, we would need to take the derivative in both dimensions. Here, the Laplacian operator comes handy. The/ The Hough Line Transform is a transform used to detect straight lines. To apply the Transform, first an edge detection pre-processing is desirable. By Dr. Xinwen Fu How does it work? A line in the image space can be expressed with two variables. In the/


Precise News Video Text Detection and Text Extraction Based on Multiple Frames Integration Advisor: Dr. Shwu-Huey Yen Student: Hsiao-Wei Chang 1.

accomplish this. (1) where Y is the intensity value. 16 17 (a Fig. 2. Four color reference frames. 18 Fig. 3. Four grayscale reference images. Step 2: Execute the edge detection. The Canny edge detector is applied on each grayscale image yielding an edge map. A simple line deletion (horizontal and vertical) is followed if a line is too long. 19 20 Fig. 4. Four Canny/


DIGITAL IMAGE PROCESSING Instructors: Dr J. Shanbehzadeh M.Yekke Zare M.Yekke Zare ( J.Shanbehzadeh M.Yekke Zare )

variance of the background and the object pixels will be:  The variance of the whole image is: 10.1- Fundamentals 10.2- Point, Line and Edge Detection 10.3- Thresholding 10.4- Region-Based Segmentation 10.5 - Segmentation Using Morphological watersheds/Thresholds The following relationships hold: The optimum thresholds can be found by : The image is then segmented by 10.1- Fundamentals 10.2- Point, Line and Edge Detection 10.3- Thresholding 10.4- Region-Based Segmentation 10.5 - Segmentation Using /


Ramakrishna Lanka Ramakrishna.lanka@mavs.uta.edu 1001126160 IMAGE ANALYSIS AND OBJECT DETECTION-RECOGNITION USING COMPUTER VISION ALGORITHMS Ramakrishna.

is the number of pixels with the intensity rk.[4] Computer Vision search algorithms and techniques Edge feature detection The Sobel operator The Prewitt operators The Robert operator Laplacian/Laplacian of Gaussian operator Canny edge detection Fig(24). Reference object image [25] Fig(25). Object in scene image [25] Edge feature detection The Sobel operator (1.5) Where Gx is the vertical filter and Gy is the horizontal/


CS 558 C OMPUTER V ISION Lecture VII: Corner and Blob Detection Slides adapted from S. Lazebnik.

finding characteristic region size that is covariant with the image transformation A CHIEVING SCALE COVARIANCE R ECALL : E DGE DETECTION f Source: S. Seitz Edge Derivative of Gaussian Edge = maximum of derivative E DGE DETECTION, T AKE 2 f Edge Second derivative of Gaussian (Laplacian) Edge = zero crossing of second derivative Source: S. Seitz F ROM EDGES TO BLOBS Edge = ripple Blob = superposition of two ripples Spatial selection: the/


Feature Detection. Image features Global features –global properties of an image, including intensity histogram, frequency domain descriptors, covariance.

Detection Image features Global features –global properties of an image, including intensity histogram, frequency domain descriptors, covariance matrix and high order statistics, etc. Local features –local regions with special properties, including edges, corners, lines, curves, regions with special properties, etc Depending on applications, various features are useful. We will focus on edges and corners in this lecture Edges Edge points are pixels at or around which the image/


Lecture 16 Image Segmentation 1.The basic concepts of segmentation 2.Point, line, edge detection 3.Thresh holding 4.Region-based segmentation 5.Segmentation.

and  = 0.0, 0.1, 1.0 and 10.0, respectively. Second column: first-derivative images and gray-level profiles. Third column : second- derivative images and gray- level profiles. 13 Steps in edge detection 1.Image smoothing for noise reduction 2.Detection of edge points. Points on an edge 3.Edge localization 14 Image gradient Gradient is a vector The magnitude of the gradient The direction of the gradient vector/


University of Kurdistan Digital Image Processing (DIP) Lecturer: Kaveh Mollazade, Ph.D. Department of Biosystems Engineering, Faculty of Agriculture,

example, the brickwork in the previous example. One way to overcome this is to smooth images prior to edge detection. Digital Image Processing – Department of Biosystems Engineering – University of Kurdistan http://agri.uok.ac.ir/k.mollazade 17 Edge detection example with smoothing Original Image Horizontal Gradient Component Vertical Gradient Component Combined Edge Image Digital Image Processing – Department of Biosystems Engineering – University of Kurdistan http://agri.uok.ac.ir/k/


Edges. Edge detection schemes can be grouped in three classes: –Gradient operators: Robert, Sobel, Prewitt, and Laplacian (3x3 and 5x5 masks) –Surface.

-maximum suppression) Noise reduction: first derivatives are susceptible to noise present on raw unprocessed image data. Canny edge detection performs convolution of the original image with a Gaussian filter to remove noise. The result is a slightly blurred version of the original image. ‒Consider a single row or column of the image and plot intensity as a function of position. If pixels are disturbed with noise/


Detection of nerves in Ultrasound Images using edge detection techniques NIRANJAN TALLAPALLY.

TALLAPALLY Problem statement Problem Description Some of the papers (Edge Detection) reviewed Canny Edge Detection Detection of images in speckle images Authentication of edges produced by zero crossings Adaptive Transform edge detection Summary Outline Image Segmentation in Ultrasound imaging Ultrasound Imaging: In Ultrasound imaging, high frequency sound waves are used to capture the images. Usually sound waves are directed towards object to be imaged. From the sound waves reflected from the object, 2/


Computer Vision : CISC 4/689 Gradients and edges Points of sharp change in an image are interesting: –change in reflectance –change in object –change in.

or 3 Computer Vision : CISC 4/689 Example: Canny Edge Detection courtesy of G. Loy gap is gone Original image Strong edges only Strong + connected weak edges Weak edges Computer Vision : CISC 4/689 Example: Canny Edge Detection (Matlab automatically set thresholds) Computer Vision : CISC 4/689 Image Pyramids Observation: Fine-grained template matching expensive over a full image –Idea: Represent image at smaller scales, allowing efficient coarse- to-fine search/


Digital Image Processing By: Eng. Mohanned Dawoud 1.

mask to detect lines of a given direction 85 Edge Detection First derivative – detect if a point is on the edge Second derivative – detect the midpoint of the edge (zero-crossing property) 86 Edge detection in noisy images Examples of a ramp edge corrupted by random/Partitioning into regions done often by using gray values of the image pixels. Two general approaches : – Region-based segmentation – Boundary estimation using edge detection 103 Region-based Approach Pixels corresponding to an object grouped /


Motion Noise Separation In Digital Video Motion Noise Separation In Digital Video E. E. A. Yfantis, Y. Terradas, P. Olson F. Image Processing, Computer.

the discrete Gaussian mask shown before. If we need another standard deviation value we can create the corresponding integer-valued kernel. 20 MOTION DETECTION (cont’d) The following images show the original background frame and the corresponding edge map. This image will be saved so we can use it every time we need it. The next step will take care of two consecutive frames/


Chapter 10.  Image segmentation subdivides an image into its constituent regions or objects.  The level of detail to which the subdivision is carried.

a number of these approaches and show that improvements in segmentation performance can be achieved by combining methods from distinct categories, such as techniques in which edge detection is combined with thresholding.  Suppose the Image R using the image segmentation process, R will be partitioned into n sub regions: R 1, R 2,…,R n.  In the segmentation process there are five possible conditions/


1 Optimal Linear Operator for Step Edge Detection Jun Shen Image Laboratory, Institute EGID, Bordeaux-3 University, France.

the theoretical analysis on optimization and performance analysis. 35 36 37 38 39 40 Isotropically Symmetrical? 41 42 43 44 F On Multi-Edge Detection, "CVGIP: Graphical Models And Image Processing", Vol.58, No.2, pp101-114, March 1996. F Multi-Edge Detection by Isotropical 2-D ISEF Cascade, “Pattern Recognition”, Vol.28, No.12, pp1871-1885, 1995. F Towards Unification of Band-Limited Differential/


October 7, 2014Computer Vision Lecture 9: Edge Detection II 1 Laplacian Filters Idea: Smooth the image, Smooth the image, compute the second derivative.

 7 Laplacian October 7, 2014Computer Vision Lecture 9: Edge Detection II 11 Laplacian Filters October 7, 2014Computer Vision Lecture 9: Edge Detection II 12 Gaussian Edge Detection As you know, one of the problems in edge detection is the noise in the input image. Noise creates lots of local intensity gradients that can trigger unwanted responses by edge detection filters. We can reduce noise through (Gaussian) smoothing, but there is a/


Brent M. Dingle, Ph.D. 2015 Game Design and Development Program Mathematics, Statistics and Computer Science University of Wisconsin - Stout Edge Detection.

– Interpolation – Warping – Morphing – Compression Today – Edge Detection Image Manipulation and Enhancement Image Analysis Image Compression Recall: Digital Image Processing Digital Image Processing (DIP) – Is computer manipulation of pictures, or images, that have been converted into numeric form – Typical operations include Image Compression Image Warping Contrast Enhancement Blur Removal Feature Extraction Pattern Recognition Image Processing Goals Digital image processing is a subclass of signal/


Image Registration Advanced DIP Project Group #3: Dave Grimm Joe Handfield Mahnaz Mohammadi Yushan Zhu.

Problem Statement Comparison of GCP selection algorithms Comparison of GCP selection algorithms Manual Manual Automated Automated Area-based Area-based Feature-based Feature-based Contour Mapping Contour Mapping Corner and Edge Detection Corner and Edge Detection Manual Registration Left: the reference image Right: the image to be registered Automated Registration Area-based algorithms Area-based algorithms A small window of points in the sensed/


Digital Image Processing Lecture 16: Segmentation: Detection of Discontinuities May 2, 2005 Prof. Charlene Tsai.

is necessary for the sum or average of all elements of the kernel matrix to be zero. Digital Image ProcessingLecture 16 28 Summary  Line detectionEdge detection based on  First derivative  Provides gradient information  2 nd derivative using zero-crossing  Indicates dark/bright side of an edge  Line detectionEdge detection based on  First derivative  Provides gradient information  2 nd derivative using zero-crossing  Indicates dark/bright side/


Course 5 Edge Detection. Image Features: local, meaningful, detectable parts of an image. edge corner texture … Edges: Edges points, or simply edges,

Course 5 Edge Detection Image Features: local, meaningful, detectable parts of an image. edge corner texture … Edges: Edges points, or simply edges, are pixels at or around which the image values undergo a sharp variation. Usually, edges are classified as : step edge, ridge edge roof edge. We will emphasize on step edge detection in our discussions. 1.Edge detection: 1)Goal: Given an image corrupted by acquisition noise, locate the edges most likely to be generated by scene elements not by/


Machine Vision Edge Detection Techniques ENT 273 Lecture 6 Hema C.R.

that produces a set of edges from an image Contour –Is a list of edges or the mathematical curve that models the list of edges. Edge Linking –Is the process of forming an ordered list of edges from an unordered list Edge Following –Is the process of searching the image to determine contours Hema ENT 273 Lecture 6 6 Edge Detection Edge detection is an operation to detect significant local changes in the/


Digital Image Processing Image Segmentation: Thresholding.

) filter uses the Gaussian for noise removal and the Laplacian for edge detection Images taken from Gonzalez & Woods, Digital Image Processing (2002) 23 of 20 Laplacian Of Gaussian Example Images taken from Gonzalez & Woods, Digital Image Processing (2002) 24 of 20 Summary In this lecture we have begun looking at segmentation, and in particular edge detection Edge detection is massively important as it is in many cases the first step/


September 26, 2013Computer Vision Lecture 8: Edge Detection II 1Gradient In the one-dimensional case, a step edge corresponds to a local peak in the first.

13 Laplacian Filters 3  3 Laplacian 5  5 Laplacian zero detection 7  7 Laplacian September 26, 2013Computer Vision Lecture 8: Edge Detection II 14 Gaussian Edge Detection As you know, one of the problems in edge detection is the noise in the input image. Noise creates lots of local intensity gradients that can trigger unwanted responses by edge detection filters. We can reduce noise through (Gaussian) smoothing, but there is/


Detection of nerves in Ultrasound Images using edge detection techniques NIRANJAN TALLAPALLY.

TALLAPALLY Problem statement Problem Description Some of the papers (Edge Detection) reviewed Canny Edge Detection Detection of images in speckle images Authentication of edges produced by zero crossings Adaptive Transform edge detection Summary Outline Image Segmentation in Ultrasound imaging Ultrasound Imaging: In Ultrasound imaging, high frequency sound waves are used to capture the images. Usually sound waves are directed towards object to be imaged. From the sound waves reflected from the object, 2/


Chapter 10: Image Segmentation The whole is equal to the sum of its parts. Euclid The whole is greater than the sum of its parts. Max Wertheimer.

fairly little noise can have such a significant impact on the two key derivatives used for edge detection in images image smoothing should be serious consideration prior to the use of derivatives in applications where noise is likely to be present. 21 Edge point to determine a point as an edge point the transition in gray level associated with the point has to be significantly stronger/


CS558 C OMPUTER V ISION Lecture IV: Image Filter and Edge Detection Slides adapted from S. Lazebnik.

and convolution Convluation and linear filter Gaussian filter, box filter, and median filter Application: Hybrid images Edge detection Image derivatives Gaussian derivative filters Canny edge detector O UTLINE Image filter and convolution Convolution and linear filter Gaussian filter, box filter, and median filter Application: Hybrid images Edge detection Image derivatives Gaussian derivative filters Canny edge detector M OTIVATION : I MAGE DENOISING How can we reduce noise in a photograph? M OVING/


INF 53001 INF 5300 - 30.3.2016 Detecting good features for tracking Anne Schistad Solberg  Finding the correspondence between two images  What are good.

J will point in the direction of the steepest ascent. Taking the derivative is prone to noise, so we normally apply smoothing first/or in combination by combining the edge detector with a Gaussian. INF 530048 Edge detection using Gaussian filters Gradient of a smoothed image: Derivative of Gaussian filter: Remember that the second derivative (Laplacian) carries information about the exact location of the/


Filters, Edge Detection and Sharpening Francesca Pizzorni Ferrarese 28/04/2010.

gaussian filter matrix of 7 rows and 7 columns, with standard deviation of 5. Smoothing / Blurring Application of the same Gaussian filter to an intensity image (take the red layer of the previous image): Edge Detection The process of edge detection attenuates high fluctuations in color, i.e. dramatic change in intensity. In the frequency domain, this process refers to the attenuation of high frequencies. Matlab/


Edge Detection Selim Aksoy Department of Computer Engineering Bilkent University

and Woods CS 484, Spring 2007©2007, Selim Aksoy20 Difference operators for 2D Adapted from Gonzales and Woods CS 484, Spring 2007©2007, Selim Aksoy21 Gaussian smoothing and edge detection We can smooth the image using a Gaussian filter and then compute the derivative. Two convolutions: one to smooth, then another one to differentiate?  Actually, no - we can use a derivative of Gaussian/


1 Image Processing For Robot Navigation Modar Ibraheem Wintersemester 2007/2008.

looking for discontinuities in gradients. Linking these edge points in some way to produce descriptions of edges in terms of lines, curves etc. 22 I.Definitions & Concepts Edges 23 Edges are caused by a variety of factors surface normal discontinuity depth discontinuity surface color discontinuity illumination discontinuity II.Edge Detection Origin of Edge 24 II.Edge Detection Images and intensity gradients The image is a function mapping coordinates to intensity f/


CIS 581 Course Project Heshan Lin

and dissolves. By analyzing the spatial distribution of entering and exiting edge pixels, we can detect and classify wipes. Edges/contours (cont.) How to define the entering and exiting edge pixels Xnin and Xn-1out? Suppose we have 2 binary images en-1 and en. The entering edge pixels Xnin are the fraction of edge pixels in en which are more than a fixed distance r from/


Image Processing A brief introduction (by Edgar Alejandro Guerrero Arroyo)

transition. f (x) helps to identify the exact position of transition. f (x) helps to identify the exact position of transition. Edge Detection Gradient and Magnitude Edge Detection But an image is a two dimensional discretized gray level function f(x,y). But an image is a two dimensional discretized gray level function f(x,y). The norm of the gradient can be aproximated to reduce/


Sliding Window Filters and Edge Detection Longin Jan Latecki Computer Graphics and Image Processing CIS 601 – Fall 2004.

.gif in www.cis.temple.edu/~latecki/CIS601-04LecturesImages Compare the results. Edge Detection What are edges in an image? Edge Detection Edge Detection Methods Edge Operators Matlab Program Performance What are edges in an image?  Edges are those places in an image that correspond to object boundaries.  Edges are pixels where image brightness changes abruptly. Brightness vs. Spatial Coordinates More About Edges  An edge is a property attached to an individual pixel and is calculated from/


Face Detection and Recognition

in complex background 30/08/2006 IPCV 2006 Budapest Agenda (Detection) Detecting faces in gray scale images Knowledge-based Feature-based Template-based Appearance-based Detecting faces in color images Detecting faces in video Performance evaluation Research direction and concluding remarks 30/08/2006 IPCV 2006 Budapest Template Matching Methods Store a template Predefined: based on edges or regions Deformable: based on facial contours (e.g., Snakes/


1 Ekstrahering og repræsentation af features MM2: Convolution and filtering of images.

blurring is minimized and edges stay sharp 41 Edge Detection Powerfull application of convolution! 42 Edge detection What are edges? Why are they interesting? How do we find them? –Prewitt –Sobel –Laplacian –Canny 43 What are edges? Edge detection Local intensity change Strong edge = the steep areas in a 3D plot (show: blobs) 44 Why are they interesting? Edges can (many times) represent the information in the image (the objects) A higher/


Edges and Binary Images

filter – can it be done with covolution 25 x 25 median filter 50 x 50 median filter Edge detection Goal: map image from 2d array of pixels to a set of curves or line segments or contours. Why? Main / noise Derivatives to locate contrast, gradient Templates, matched filters to find designated pattern. Edge detection processes the image gradient to find curves, or chains of edges. Binary image analysis useful to manipulate regions of interest Connected components Morphological operators (local operator, but/


Segmentation (2): edge detection

segmentation A large group of methods based on information about previously detected edges in the image Preprocessing step: edge detection Motivation: images resulted from edge detection cannot be used as a segmentation result Edges have to be linked into chains which correspond better with boundaries in an image Final result: detection of boundaries of objects present in the image Edge-based segmentation is not about one algorithm, but involves a large group of/


Digital image processing Chapter 8 Image analysis and pattern recognition IMAGE ANALYSIS AND PATTERN RECOGNITION Introduction Feature extraction: - spatial.

: Gaussian filter The derivative of the Gaussian The 2 nd derivative (Laplacian of Gaussian) Digital image processing Chapter 8 Image analysis and pattern recognition Edge detection by different operators – comparison: Original image Sobel edge detection LoG edge detection; sigma=5 Digital image processing Chapter 8 Image analysis and pattern recognition Roberts edge detection LoG edge detection; Sigma=10 Objects representation by their boundaries: Contour extraction: Fig. 8.8 4-connectivity; 8/


Edge Detection Enhancement Using Gibbs Sampler

of Gibbs) Edge Detection with Gibbs Original Image (512x512) Canny Edge Detection Edge Detection with Gibbs Edge Detection with Gibbs Original Image (200x200) Canny Edge Detection Edge Detection with Gibbs Edge Detection with Gibbs Original Image (2048x2048) Canny Edge Detection Edge Detection with Gibbs Edge Detection with Gibbs Original Image (800x800) Canny Edge Detection Edge Detection with Gibbs Image Enlargement, Canny Edge Detector Image Used Image Enlargement, Gibbs Enhanced Edge Detector Image Used/


ECCV 2002 Removing Shadows From Images G. D. Finlayson 1, S.D. Hordley 1 & M.S. Drew 2 1 School of Information Systems, University of East Anglia, UK 2.

thresholding step T, that is, on effectively locating the shadow edges. As we will see, our shadow edge detection is not yet perfect ECCV 2002 Shadow Edge Detection The Shadow Edge Detection consists of the following steps: 1. Edge detect a smoothed version of the original (by channel) and the invariant images 2. Threshold to keep strong edges in both images 3. Shadow Edge = Edge in Original & NOT in Invariant 4. Applying a suitable/


Content Based Image Retrieval

| for all ji – the point is within line i. Use one mask to detect lines of a given direction 10/50 Edge Detection First derivative – detect if a point is on the edge Second derivative – detect the midpoint of the edge (zero-crossing property) 11/50 Edge detection in noisy images Examples of a ramp edge corrupted by random Gaussian noise of mean 0 and  = 0.0, 0.1, 1/


1 Formation et Analyse d’Images Session 5 Daniela Hall 4 November 2004.

the texture –Reflections depends on view point and illumination 7 Contrast detection process Image processing Image analysis image intermediate image representation image description 8 Contrast detection methods Search for extremum in 1 st derivative Search for zero crossing in 2 nd derivative 9 Contrast detection Step edge Smoothed step edge First derivative Second derivative (white pos, yellow negative) 10 Image processing tools Let s(i), f(i) be two real discrete/


Machinen Vision and Dig. Image Analysis 1 Prof. Heikki Kälviäinen CT50A6100 Lectures 8&9: Image Segmentation Professor Heikki Kälviäinen Machine Vision.

. Feature selection => feature extraction => feature vector. Challenging question: What details? Machinen Vision and Dig. Image Analysis Prof. Heikki Kälviäinen CT50A6100 4 Detection of discontinuities Point detection. Line detection. Edge detection. –Basic formulation. –Gradient operators. –Laplace operators. Combined detection. Machinen Vision and Dig. Image Analysis Prof. Heikki Kälviäinen CT50A6100 5 Point detection Detection of isolated points: a) Apply the mask -1 -1 -1 -1 8 -1 -1/


Vision Review: Image Processing Course web page: www.cis.udel.edu/~cer/arv September 17, 2002.

in surface material properties) Illumination discontinuity (e.g., shadow) Edge Detection Edge Types –Step edge (ramp) –Line edge (roof) Searching for Edges: –Filter: Smooth image –Enhance: Apply numerical derivative approximation –Detect: Threshold to find strong edges –Localize/analyze: Reject spurious edges, include weak but justified edges Step edge detection First derivative edge detectors: Look for extrema –Sobel operator (Matlab: edge(I, ‘sobel’) ) –Prewitt, Roberts cross –Derivative of Gaussian/


Example: Canny Edge Detection

gradient per point Look at the gradient behavior over a small window Categories image windows based on gradient statistics Constant: Little or no brightness change Edge: Strong brightness change in single direction Flow: Parallel stripes Corner/spot: Strong brightness changes in orthogonal directions Computer Vision : CISC 4/689 Corner Detection: Analyzing Gradient Covariance Intuitively, in corner windows both Ix and Iy should be/


Lecture 2: Edge detection and resampling

does median filtering have over Gaussian filtering? Source: K. Grauman Salt & pepper noise – median filtering = 1 pixel = 2 pixels = 5 pixels 3x3 window 5x5 window 7x7 window Questions? Edge detection Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box/


Lappeenranta University of Technology (Finland)

(upper right), sum for two masks (bottom). Edge detection. Sobel (left), Prewitt (right) Edge detection. Marr edge detection (Laplacian of Gaussian). After convolution with a smoothing filter h(r) of =1.5 (left) and =5 (right). Edge detection is given in next slide. Edge detection. Marr edge detection. Zero crossing from above (presented in previous slide) image (right) and the same for left image. Edge detection. Original image (grains). Edge detection. Sobel (upper left), Prewitt (upper right), Roberts/


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