Computer Vision and Fuzzy-neural Systems
For courses in computer vision, pattern recognition, or image processing at the senior undergraduate level or first year graduate level.This essential resource brings together the field's latest research and applications, presenting the field's first comprehensive tutorial and reference to apply fuzzy-neural systems to computer vision applications, such as remote sensing, medical image analysis, data compression, fingerprint analysis, and character recognition. Reflects most recent trends in computer vision and provides algorithms with practical examples.
What people are saying - Write a review
We haven't found any reviews in the usual places.
COMPUTER VISION FUNDAMENTALS
FUZZY LOGIC FUNDAMENTALS
9 other sections not shown
Other editions - View all
algorithm applications associative memory backpropagation backpropagation learning cluster centers coefficients competitive learning computer vision computer vision system Consider corresponding data compression data set defined defuzzification detection digital image edge Equation feature extraction feature space feature vector feed-forward feed-forward network filtering fuzzifier fuzzy inference system fuzzy logic fuzzy neural network fuzzy rules fuzzy set fuzzy systems Gabor filters given gray value Grossberg histogram Hopfield network IEEE Transactions Image Processing implement input features input image input layer input pattern input sample input vector Kohonen Kulkarni layer L2 learning algorithm linear mapping mean-squared error membership functions methods neural network models neuron node noise number of units obtained operators original image output layer output vector pattern recognition perceptron model pixel point spread function Radon transform region segmentation self-organizing shown in Figure SOFTWARE spatial Step techniques texture three-layer tion training samples two-dimensional units in layer weight matrix weight vector weights between layers