Computer Vision and Fuzzy-Neural Systems

Dr. Arun D. Kulkarni is Professor of Computer Science at The University of Texas at Tyler, Tyler, Texas. His research interests include computer vision, fuzzy-neural systems, data mining, image processing, and artificial intelligence. He has authored a book and published more than 50 referred papers. His awards include the 1984 Fulbright Fellowship award and the 1997 NASA/ASSE Summer Faculty Fellowship. Dr. Kulkarni obtained his Ph.D. from the Indian Institute of Technology, Bombay, and was a post-doctoral fellow at Virginia Tech.

(NOTE: Each chapter begins with an Introduction and concludes with a Summary, References, and Exercises.)

Preface.


1. Introduction.


Computer Vision. Neural Network Models. Fuzzy Logic Techniques. Fuzzy Neural Systems. Outline.



2. Computer Vision Fundamentals.


Human Vision System. Perception. Input-Output Devices. Camera Models. Sampling And Quantization. Preprocessing Techniques. Image Transforms. Feature Extraction And Recognition.



3. Fuzzy Logic Fundamentals.


Fuzzy Sets And Membership Functions. Logical Operations And If-Then Rules. Fuzzy Inference System. Defuzzification. Fuzzy Set Representation With A Cube. Hedges. Fuzzy Systems As Function Approximators. Extraction Of Rules From Sample Data Points. Fuzzy Basis Functions. Design And Implementation Of A Fuzzy Inference System.



4. Neural Network Fundamentals.


Neuron Representation. Perception. Linear Networks. Single-Layer Networks With Nonlinear Transfer Functions. Backpropagation. Kohonen Feature Maps. Competitive Learning. Hopfield Networks. Counterpropagation Network.



5. Preprocessing.


Gray-Level Histogram. Point Operations. Filtering Techniques. Noise Removal Techniques. Mathematical Morphology. Edge Detection Techniques. Neural Network Models For Brightness Perception And Boundary Detection. Image Restoration. Geometric Corrections And Registration. Interpolation.



6. Feature Extraction.


Segmentation And Shape Descriptors. Moment Invariants. Feature Extraction Using Orthogonal Transforms. Neural Network Models For Ft Domain Feature Extraction. Neural Network Model For Wht Domain Feature Extraction. Invariant Feature Extraction Using Adaline. Texture Features. Neural Network Models For Texture Analysis.



7. Supervised Classifiers.


Discriminant Functions. Minimum Distance Classifiers. Bayes Classifier. Tree Classifiers. Neural Network Models For Classification. Fuzzy Neural Network Models.



8. Unsupervised Classifiers.


Conventional Clustering Techniques. Self-Organizing Networks. Fuzzy C-Means Clustering. Fuzzy Neural Network Models For Clustering.



9. Associative Memories.


Discrete Autocorrelator. Discrete Bidirectional Associative Memory. Bidirectional Associative Memories With Multiple Input-Output Patterns. Optimal Associative Memory. Selective Reflex Memory. Temporal Associative Memory. Counterpropagation Networks As Associative Memory. Fuzzy Associative Memory. Computer Vision Applications.



10. Applications.


Remote Sensing. Medical Image Processing. Image Data Compression. Data Mining And Computer Vision. Biometric Applications. Character Recognition. Knowledge-Based Pattern Recognition. Stereo Vision.



Index.


About The Author.


About The CD.