It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image.

Soft Computing and Image Analysis : Features, Relevance and Hybridization.- 1. Preprocessing and Feature Extraction.- Image Filtering Using Evolutionary Neural Fuzzy Systems.- Edge Extraction Using Fuzzy Reasoning.- Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithm.- Adaptive Clustering for Effiicient Segmentation and Vector Quantization of Images.- On Fuzzy Thresholding of Remotely Sensed Images.- Image Compression Using Pixel Neural Networks.- Genetic Algorithm and Fuzzy Reasoning for Digital Image Compression Using Triangular Plane Patches.- Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization.- Soft Computing and Image Analysis.- Fuzzy Interpretation of Image Data.- 2. Classification.- New Pattern Recognition Tools Based on Fuzzy Logic for Image Understanding.- Adaptive, Evolving, Hybrid Connectionist Systems for Image Pattern Recognition.- Neuro-Fuzzy Computing: Structure, Performance Measure and Applications.- Knowledge Reuse Mechanisms for Categorizing Related Image Sets.- Symbolic Data Analysis for Image Processing.- 3. Applications.- The Use of Artificial Neural Networks for Automatic Target Recognition.- Hybrid Systems for Facial Analysis and Processing Tasks.- Handwritten Digit Recognition Using Soft Computing Tools.- Neural Systems for Motion Analysis : Single Neuron and Network Approaches.- Motion Estimation and Compensation with Neural Fuzzy Systems.- About the Editors.