of an object. The data grid is **compressed** into a smaller set that retains the essential features. Back- propagation is **used**. Recognition on the order of approximately 70% is achieved. Detecting Skin Cancer F. Ercal, A. Chawla, W. Stoecker, and R. Moss study a **neural** **network** approach to the diagnosis of malignant melanoma. They strive to discriminate tumor **images** as malignant or benign. There are/

we have weakly biologically-inspired computing technologies (**neural** nets, genetic algorithms, developmental genetic programming, belief **networks**, support vector machines, evolvable hardware, etc/ Development is intelligence/adaptation preservation. Life, Intelligence, and the Universe **use** both evo and devo processes to emerge and persist. Individually, each/501 (c)(3) Nonprofit Disruptive STEM **Compression** in Nanospace: Holey Optical Fibers for Microlasers Above: SEM **image** of a photonic crystal fiber. Note /

**Neural** **Networks** Netta Cohen Last time Today Biologically inspired associative memories moves away from bio- realistic model Unsupervised learning Working examples and applications Pros, Cons & open questions SOM (Competitive) Nets Neuroscience applications GasNets. Robotic control Attractor **neural** nets: Other **Neural** Nets Spatial Codes Natural **neural** nets often code similar things close together. The auditory and visual cortex provide examples. **Neural**/data are **compressed** **using** spatial/ / different **images**. The/

**Neural** **Networks** Netta Cohen 2 Last time Biologically inspired associative memories moves away from bio- realistic model Unsupervised learning Working examples and applications Pros, Cons & open questions Today Attractor **neural** nets: SOM (Competitive) Nets Neuroscience applications GasNets. Robotic control Other **Neural** Nets 3 Spatial Codes Natural **neural**/volumes of data are **compressed** **using** spatial/ topological relationships/ to encode categories of different **images**. The redundancy in this encoding/

pueden estar de frente o de perfil, o en puntos intermedios. Capturing Device / **Compression** / **Image** Quality/ Resolution Challenges (4) Why gray scale **images**? Some **images** are just in grey scale and in others the colors were modified. The color /also be **used** for the feature/classifier selection. Boosting works well with unstable base learners, i.e. algorithms whose output classifier undergoes mayor changes in response to small changes in training data for example, decision trees **neural** **networks** decision stumps/

**used** to normalize each face to the same scale, orientation, and position Result: set of 20 X 20 face training samples Training the **Neural** **Network** Negative Face Examples Generate 1000 random nonface **images** and apply the preprocessing Train a **neural** **network** on these plus the face **images**/ most variation among training vectors x eigenvector with smallest eigenvalue has least variation We can **compress** the data by only **using** the top few eigenvectors corresponds to choosing a “linear subspace” represent points on a/

. Pb: because of Matlab, transforming picture into Matrix needs computations. (solution: **use** another language more picture processing-oriented) Some references -**Image** **compression** by Self-Organized kohonen Map Christophe Amerijckx, Philippe Thissen..IEE Transition on **Neural** **Networks** 1998. http://www.dice.ucl.ac.be/~verleyse/papers/ieeetnn98ca.pdf -SRAM bitmap shape recognition and sorting **Using** **Neural** **Networks**. Randall S. Collica. IEEE. http://www.ibexprocess.com/solutions/wp_SRAM.pdf -From/

**Neural** **networks** bridge this gap by modeling, on a computer, the **neural** behavior of human brains. **Neural** **networks** bridge this gap by modeling, on a computer, the **neural** behavior of human brains. 3 **Neural** **Network** Characteristics **Neural** **networks** are **useful** for pattern recognition or data classification, through a learning process. **Neural** **networks** are **useful**/ –Learning by doing –**Used** to pick out structure in the input: –Clustering –**Compression** 12 Topologies – Back- Propogated **Networks** Inputs are put through /

Goal-based Planning Goal-based Planning Rule-based Inference Engine Rule-based Inference Engine **Neural** **Network** **Neural** **Network** References References –Game Gems –AI Game Programming Wisdom 295 Game Physics 296 Introduction/**Network** Data **Compression** Must be Lossless **Compression** ? Must be Lossless **Compression** ? Zip ? Zip ? Bit, Byte, Short or Long ? Bit, Byte, Short or Long ? Fixed-point or Floating-point Fixed-point or Floating-point Run-length **Compression** Run-length **Compression** **Use** Index/ID Instead of Data **Use**/

calculations for ONE iteration. Show the weight values at the end of the first iteration? 39 The illustrated Simple Recurrent **Neural** **Network** has two neurons. All neurons have sigmoid function. The **network** ues the standard error function E = **using** the initial weights [b1=-0.5, w1=2,b2=0.5 and w2=0.5] and let the input /. See e.g. C. H. Chou, M. C. Su and E. Lai, “A New Cluster Validity Measure and Its Application to **Image** **Compression**,” Pattern Analysis and Applications, vol. 7, no. 2, pp. 205-220, 2004. (SCI)

NFIQ algorithm is an implementation of the NIST “Fingerprint **Image** Quality” algorithm. It takes an input **image** that is in ANSI/NIST or NIST IHEAD format or **compressed** **using** WSQ, baseline JPEG, or lossless JPEG. NFIQ outputs the **image** quality value for the **image** (where 1 is highest quality and 5 is lowest quality). 46 **Image** Quality (NFIQ) **Neural** **networks** offer a very powerful and very general framework for/

**Neural** **Networks** M. De Kamps, Netta Cohen 2 Attractor **networks**: Two examples Jets and Sharks **network** –Weights set by hand –Demonstrates recall Generalisation Prototypes Graceful degradation Robustness Kohonen **networks**/surface. Lattice Kohonen Nets Large volumes of data are **compressed** **using** spatial/ topological relationships within the training set. Thus / The IT employs a distributed representation to encode categories of different **images**. The redundancy in this encoding allows for graceful degradation so that/

requires a lot of introduction, and will also take me into the domain of computing within living organism (“real” **neural** **networks**) It deals with with real-time computing rather than off-line processing, although the boundary between the two is/reduction 50 patterns Entropy=9.8% **Compression** factor=40 16 patterns Entropy=5.5% Compressionr= 67 16 patterns Entropy=5.5% Compressionr= 67 Original **Image** 244 NON optimal patterns.) Entropy=5.5% **Compression**=90 Natural vision only **uses** a small number of patterns under /

al, pure learning ■ Conclusions Introduction – Super resolution ■ Goal: obtaining a high resolution (HR) **image** from a low resolution (LR) input **image** ■ Ill posed problem ■ Motivation – overcoming the inherent resolution limitations of low cost **imaging** sensors/**compressed** **images** allowing better utilization of high resolution displays Introduction – **Neural** **Networks** Old machine learning algorithm (first work - 1943) Widely **used** since 2012 (Alex net) Mostly on high-level-vision tasks (classification, detection/

**used** in the construction of **neural** **networks**. Strictly increasing function that exhibits a graceful balance between linear and nonlinear behavior. T0293 - Neuro Computing21 Figure 1.5 Graph of the sigmoid function. Figure 1.4 Sigmoid function for varying slope parameter a. The Future of **Neural** **Network** –**Neural** **Networks** Model Multilayer Perceptrons (Backpropagation) Principal-Component Analysis (PCA) Self-Organizing **Network** Model (SOM) etc. –Application Pattern Recognition **Image** **Compression** Optimization/

world. How??? Where do you start? 4/6/2017 **Neural** **Networks** NN Applications http://www-cs-faculty. stanford Character recognition **Image** **compression** Stock market prediction Traveling salesman problem Medicine, electronic noise, loan applications 4/6/2017 **Neural** **Networks** **Neural** **Networks** (ACM) Web spam detection by probability mapping graphSOMs and graph **neural** **networks** No-reference quality assessment of JPEG **images** by **using** CBP **neural** **networks** An Embedded Fingerprints Classification System based on Weightless/

**Image** Understanding Techniques Knowledge integration from both low-level (color/texture) and mid- level (people/sky/grass) features **using** Bayesian **networks** Scene classification in broad **image** categories (e.g. indoor vs outdoor), city, forest, mountain, sea, etc. High-level **image** understanding for **image** **compression** and **image**/advise them on document **imaging**, OCR, forms design, error analysis, matching algorithms, etc. R I T Rochester Institute of Technology Linear Pixel Shuffling **neural** net feature detection /

recognition **neural** nets, genetic algorithms medical **image** processing speech recognition, EKG Applied Computer Science parallel and distributed computing picture and **image** processing compilers GIS (Geographic Information Systems) **Networking** Migration/ –Internet visualisation –Multimedia Applications –**Compression** of topological and geometical data –Visualization and **compression** of 3D/4D medical data UM- FERI GeMMA Lab ASO Workshop on Natural Phenomena Visualisation **using** Unstructured Grid, Budmerice 11.5./

unsupervised learning. Examples of recognition From Krizhevsky, Sutskever, Hinton (2012) Autoencoders **Neural** **Network** for generation of latent (usually **compressed**) data/feature representation. Unsupervised training: no class labels needed. Reproduce target /. Minimize difference between input **image** and reconstructed **image**. No **image** labels **used**. Unsupervised training. Pooling layer weights are fixed. RICA = Reconstruction Independent Component Analysis **Image** recognizer Pretrained (unsupervised) autoencoder/

. This process can reduce computational cost dramatically. ISAN-DSP GROUP Face Recognition Project Feature Extraction Discrete Wavelet + Fourier Transform **Neural** **Network** 1. Possessing multi-resolution analysis capability that can eliminate unwanted variations of the facial **image** in wavelet scale-space. 2. Being able to **compress** the **image** **using** few coefficients Senior Project 2001 1. Chavis Srichan, 2. Piyapong Sripikul 3. Suranuch Sapsoe ISAN-DSP GROUP Multiresolution/

–Multimodal & multisensual information flows –Visualization –Telepresence –Augmentation, **neural** interfaces –Virtuality IMPACT - requires tremendous bandwidth, low latency, /**networks** –to fit on most standard storage devices Moving Picture Experts Group (or MPEG) is in charge of developing standards for coded representation of digital audio and video. LIDO 34 MPEG **Compression** The MPEG **compression** algorithm reduces redundant information in **images**. MPEG **compression** is asymmetric. Digital movies **compressed** **using**/

**Using** the following **network**: Can this be done? Learned parameters Note that each value is assigned to the edge from the corresponding input Reconstruction Data Reconstruction Data Hypothetical example (not actually from an autoencoder) **Neural** **network** autoencoding The hidden layer is a **compressed**/ digits with a **network** trained on different copies of “2” New test **images** from the digit class that the model was trained on **Images** from an unfamiliar digit class (the **network** tries to see every **image** as a 2)/

strongly supervised manner **using** hinge-based loss term and squared l2-norm regularization. w are the weights of the **neural** **network** O i net is the **network** output for the /**neural** **network** competes favourably against costs produced by a state-of-the-art hand-crafted feature descriptor, so we chose to compare with DAISY. Experiments Wide baseline stereo evaluation Experiments Local descriptors performance evaluation The dataset consists of 48 **images** in 6 sequences with camera viewpoint changes, blur, **compression**/

the same five **images**?these works would offer more insight into the minds of their composers. As it is, Rauschenbergs shuffle dulls the synapses. Karen Rosenberg ” Motivation: Single synapse matters 400 ext. (10/sec) 100 inh. (65/sec) Mainen & Sejnowki model Motivation: Single synapse matters 200 sec simulation (10 spikes/sec) Motivation: Single synapse matters “Synaptic efficacy” Artificial **Neural** **Networks** - synaptic efficacy reduced/

the step-by-step process of how to **use** **neural** **networks** Appreciate the wide variety of applications of **neural** **networks**; solving problem types Opening Vignette: “Predicting Gambling Referenda with **Neural** **Networks**” Decision situation Proposed solution Results Answer and discuss the case questions **Neural** **Network** Concepts **Neural** **networks** (NN): a brain metaphor for information processing **Neural** computing Artificial **neural** **network** (ANN) Many **uses** for ANN for pattern recognition, forecasting, prediction, and/

Model BP Algorithm Approxim. Model Selec. BP & Opt. CS 476: **Networks** of **Neural** Computation, CSD, UOC, 2009 Conclusions Advantages & Disadvantages MLP and BP is **used** in Cognitive and Computational Neuroscience modelling but still the algorithm does not have real neuro-physiological support The algorithm can be **used** to make encoding / decoding and **compression** systems. **Useful** for data pre-processing operations The MLP with the BP algorithm/

**used** to train a **neural** **network** Measures of OCR Accuracy n Character accuracy n Word accuracy n IDF coverage n Query coverage Improving OCR Accuracy n **Image** preprocessing –Mathematical morphology for bloom and splitting –Particularly important for degraded **images**/ Chen, 1995) n Matching Handwritten Records –(Ganzberger et al, 1994) n Headline Extraction n Document **Image** **Compression** (UMD, 1996-1998) Outline Document Structure n Characteristics: –Essential to understanding semantic relationships –Often lacking/

**network** Evaluate performance Applications of **Neural** **Networks** General Information 1.Search for a gene 2.Gene expression **network** 3.Kernel number prediction Applications of **Neural** **Network** Pattern classification Clustering Forecasting and prediction Nonlinear system modeling Speech synthesis and recognition / Function approximation / **Image** **compression**/ **neural** **network** that can effectively simulate kernel number of corn needs a wild range of data set **Neural** **network** can simulate kernel number of corn by **using** total/

3] A.S. Pandya, “Pattern Recognition with **Neural** **network** **using** C++,”, 2nd ed. vol. 3, J. New York: IEEE PRESS. [4/**Neural** **Network** Adaptation for Hardware Implementation”, Handbook of **Neural** Computation. JAN 97 [7] M.Negnevitsky, "Multi-Layer **Neural** **Networks** with Improved Learning Algorithms",Proceedings of the Digital **Imaging** Computing: Techniques and Applications (DICTA 2005) [8] A. Ahmed and NI. M. Fahmy, IEEE, Fellow"Application of Mullti-layer Neurad **Networks** tcil **Image** **Compression**/

CSC321: Introduction to **Neural** **Networks** and Machine Learning Lecture 22: Transforming autoencoders for learning the right representation of shapes Geoffrey Hinton What is the right representation of **images**? Computer vision is inverse graphics, so the higher levels should look like the representations **used** in graphics. –Graphics programs **use** matrices to represent spatial relationships. –Graphics programs do not **use** sigmoid belief nets to generate **images**. There is a lot/

of data values 4) take the square root of that value PART 5: APPLICATIONS OF **NEURAL** **NETWORK** THEORY AND OPEN PROBLEMS OPEN PROBLEMS Identifying if the **neural** **network** will converge in finite time Training the **neural** **network** to identify local versus global minimums **Neural** modularity APPLICATIONS OF **NEURAL** **NETWORK** THEORY Traveling Salesman problem **Image** **Compression** Character Recognition Optimal Control Problems PART 6: HOMEWORK OPTIMAL CONTROL PROBLEM FIND THE RMSE OF THE/

bit “0” Demodulation process **using** a Linear Vector Quantization based **Neural** **Network** PMR5406 Redes Neurais e Lógica Fuzzy SOM A histogram matrix H(u,v) is designed. u(k)=f(k) v(k)=f(k+l) A histogram matrix H(u,v) is designed. A geometric series generator was **used** to **compress** histogram peaks and reinforce other points of the **image**: Z(u,v)=(1/

bloom, character splitting, binding bend n Uncommon fonts can cause problems –If not **used** to train a **neural** **network** Measures of OCR Accuracy n Character accuracy n Word accuracy n IDF coverage n Query coverage Improving OCR Accuracy n **Image** preprocessing –Mathematical morphology for bloom and splitting –Particularly important for degraded **images** n “Voting” between several OCR engines helps –Individual systems depend on specific training/

**used** to train a **neural** **network** Improving OCR Accuracy **Image** preprocessing –Mathematical morphology for bloom and splitting –Particularly important for degraded **images** /**Useful** as a first pass in any system Easily extracted from JPEG-2 **images** –Because JPEG-2 **uses** object-based **compression** Additional Applications Handwritten Archival Manuscripts –(Manmatha, 1997) Page Classification –(Decurtins and Chen, 1995) Matching Handwritten Records –(Ganzberger et al, 1994) Headline Extraction Document **Image** **Compression**/

**network** “learns” based on problems to which answers are known (in supervised learning). The **network** can then produce answers to entirely new problems of the same type. Applications of Artificial **Neural** **Networks** speech recognition medical diagnosis **image** **compression** financial prediction Existing **Neural** **Network**/-layer **neural** **networks**) can be **used** to find protein secondard structure, but more often feed-forward multi- layer **networks** are **used**. Two frequently-**used** web sites for **neural**- **network**-based secondary/

Method **Network** for Logic Operations Adaptive Resonance Theorem Optimization Problems **Neural** **Networks** for Matrix Algebra Problems **Neural** **Networks** for **Compression** Hamming /**Image** Processing, Springer-Verlag, 2004 B.Macukow 17 Bibliography L.Rutkowski Flexible Neuro-Fuzzy Systems, Kluwer Acad, Publ., 2004 L.Rutkowski Computational Intelligence, Springer Verlag, 2008 Conf. Materials: **Neural** **Networks**/. It describes a number of **neural** **network** models which **use** supervised and unsupervised learning methods, /

of light microscopy plus selective functional imagingUse of light microscopy plus selective functional **imaging** Five step processFive step process –Bulk **imaging** into freshly cut sample –Mechanical sectioning via integrated microtome –Automated, continuous staining –Functional, fluorescent **imaging** –Data fusion, **compression** and archival storage Confocal Light Microscopy **Using** one objective lens twiceUsing one objective lens twice Point-spread function squaredPoint-spread function squared Instrument Overview/

**used** to evolve **network** weights, but sometimes **used** to evolve structures and/or learning algorithms Typical **Neural** **Network** OUTPUTS INPUTS More Complex **Neural** **Network** Evolutionary Algorithms (EAs) Applied to **Neural** **Network** Attributes **Network** connection weights **Network** topology (structure) **Network** PE transfer function **Network**/ Must normalize input patterns (?) SOFM Applications Speech processing **Image** processing Data **compression** Combinatorial optimization Robot control Sensory mapping Preprocessing SOFM Run /

**Network** 2 Project Goals –Demonstrate innovative **neural** **network** architecture for 3D object classification –Provide **network** communications for distributed client-server architectures –Visibility into all levels of processing –Handle variable **image** /**Network** 10 Future Work Improved feature extraction - oriented hysteresisImproved feature extraction - oriented hysteresis Object isolation **using** techniques developed for MPEG video compressionObject isolation **using** techniques developed for MPEG video **compression**/

**neural** **networks** Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 6-3 Learning Objectives Understand the step-by-step process of how to **use** **neural** **networks** Appreciate the wide variety of applications of **neural** **networks**;/**Image**-browsing systems Medical diagnosis Interpretation of seismic activity Speech recognition Data **compression** Environmental modeling, many more … Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 6-35 Other Popular ANN Paradigms Hopfield **Networks**/

**use** **neural** **networks** Appreciate the wide variety of applications of **neural** **networks**; solving problem types of Classification Regression Clustering Association Optimization Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 6-4 **Neural** **Network** Concepts **Neural** **networks** (NN): a brain metaphor for information processing **Neural** computing Artificial **neural** **network** (ANN) Many **uses**/ **Image**-browsing systems Medical diagnosis Interpretation of seismic activity Speech recognition Data **compression** /

Understand the step-by-step process of how to **use** **neural** **networks** Appreciate the wide variety of applications of **neural** **networks**; solving problem types of Classification Regression Clustering Association Optimization/**Image**-browsing systems Medical diagnosis Speech recognition Data **compression** Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 6-29 Applications Types of ANN Classification Feedforward **networks** (MLP), radial basis function, and probabilistic NN Regression Feedforward **networks**/

(Theory tells us that a **neural** **network** with at least 1 hidden layer can represent any function) Vast number of ANN types exist oioi w ij w jk xkxk hjhj Backpropagation ANNs Most widely **used** type of **network** Feedforward Supervised (learns mapping from one data space to another **using** examples) Error propagated backwards Versatile. **Used** for data modelling, classification, forecasting, data and **image** **compression** and pattern recognition. BP/

segmentation in CRM **Image** **compression**: Color quantization Bioinformatics: Learning motifs Reinforcement Learning Topics: Policies: what actions should an agent take in a particular situation Utility estimation: how good is a state (**used** by policy) No/.utoronto.ca/~delve/ Resources: Journals Journal of Machine Learning Research www.jmlr.org IEEE Transactions on **Neural** **Networks** IEEE Transactions on Pattern Analysis and Machine Intelligence Annals of Statistics Journal of the American Statistical Association /

algorithm When it’s better to **use** reducing of dimension, and when – quantifying of the input information? Reducing the dim Number of training patterns # of operations: quantifying number of syn weights of 1 layer ANN with d inputs & m output neurons **Compression** coef: **Compression** coef (b – capacity data) # of operations: Complexity: With the same **compression** coef: JPEG example **Image** is divided on to 8x8 pixels/

system assist doctor? - Objectives 1.Designated user interface with support of ultrasonic **image** **compression** No pre-**image** processing is needed Reduce storage space Facilitate the diagnosis process 2.Multi-severity level/**Neural** **Network** A direct continuation of the work on Bayes classifiers, which relies on Parzen windows classifiers. Setting: 3) Probabilistic **Neural** **Network** It learns to approximate the PDF of the training examples. The input features are normalized by standard score. Commonly **used** in **image**/

, OCR ANN-Intro (Jan 2010) 7 of 29 ANN Applications Clustering/Categorization Data mining, data **compression** ANN-Intro (Jan 2010) 8 of 29 ANN Applications Function Approximation Noisy arbitrary function needs to be/**use** of the feature extractors Second **uses** the **image** pixels directly ANN-Intro (Jan 2010) 29 of 29 References A. K. Jain, J.Mao, K.Mohiuddin, “ANN a Tutorial”, IEEE Computer, 1996 March, pp 31- 44 (Figures and Tables taken from this reference) B. Yegnanarayana, Artificial **Neural** **Networks**/

), Rafael C. Gonzalez, Richard E. Woods. Digital **Image** Processing **using** Matlab – Other books: – **Image** processing toolbox 14 Outline Introduction Digital **Image** Fundamentals Intensity Transformations and Spatial Filtering Filtering in the Frequency Domain **Image** Restoration and Reconstruction Color **Image** Processing Wavelets and Multi resolution Processing **Image** **Compression** Morphological Operation Object representation Object recognition 15 Introduction An **image** may be defined as: A two-dimensional function, f/

Models Phylogenetic Trees Electrical Grids Pipeline Flows Distribution **Networks** Biosphere/Geosphere **Neural** **Networks** Crystallography Tomographic Reconstruction MRI **Imaging** Diffraction Inversion Problems Signal Processing Condensed Matter / quality results State of the art tools **used** by embedded systems designers RC platforms for rapid prototyping Simple migration, development to deployment with full library support Design Example JPEG2000 **Image** **Compression** Algorithm 30 NCSA/OSC Reconfigurable Systems Summer/

. - Computational power/cost Some problems are so complex that they require expensive specially designed hardware. - Lack of standardization The **use** of artificial **neural** **networks** is recent; alternate naming conventions and multiple equally viable approaches occur. Current Areas of Application - Neurology & Neurobiology - Economics: stock market prediction - **Image** **compression** - NP-complete problems - EBAI – Studying Eclipsing Binaries with Artificial Intelligence Future Work - Brain-Computer Interface (BCI/

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