Titlebook: Soft Computing for Image Processing; Sankar K. Pal,Ashish Ghosh,Malay K. Kundu Book 2000 Springer-Verlag Berlin Heidelberg 2000 algorithm.

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Edge Extraction Using Fuzzy Reasoninging, detection, and tracing. Images are filtered by applying fuzzy reasoning based on local pixel characteristics to control the degree of Gaussian smoothing. Filtered images are then subjected to a simple edge detection algorithm which evaluates the edge fuzzy membership value for each pixel, based

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Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithmsing tasks using the compressed form of images, which can be accessed directly from digital library. The present chapter is focused on two algorithms. In the first algorithm a fractal based image compression technique using genetic algorithms has been suggested. In particular, the genetic algorithm

CROW

Adaptive Clustering for Efficient Segmentation and Vector Quantization of Imagesefficient technique of clustering is a natural choice for image segmentation. Recognition of similar patterns embedded in image data is the basis of clustering subregions in an image. Efforts to develop algorithms for adaptive and less computationally com-plex classification of data have led to impl

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On Fuzzy Thresholding of Remotely Sensed Imagestrated on remotely sensed images. A new quantitative index for image segmentation using the concept of homogeneity within regions is defined. Results are compared with those of probabilistic thresholding, and fuzzy c-means and hard c-means cluster-ing algorithms, both in terms of index value (quanti

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Image Compression Using Pixel Neural Networksxel has few input links, e.g. less than 10~.iV. The limit state (if exists) defines a 2D pattern, i.e. an image. Several sufficient conditions for the deterministic and stochastic convergence of PNN network are given. For LOPNN — a special subclass of PNN, necessary and sufficient condition for the

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Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantizati decomposition and vector quantization. In digital mammograms, important diagnostic features such as the microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels. These image features can be preserved by a compression scheme emp

AGATE

Fuzzy Interpretation of Image Datain the image brightness and also in the location of a pixel. We consider the cases where the observed image data or entities computed from it are inherently fuzzy. Based on this idea, we have considered shape detection methods and representation of edges using fuzzy set theory. In shape detection me

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Adaptive, Evolving, Hybrid Connectionist Systems for Image Pattern Recognitiondynamically evolving fuzzy neural networks that are neural architectures to realise connectionist learning, fuzzy logic inference, and case-based reasoning. The methodology and the architecture are applied on two sets of real data - one of satellite image data, and the other of fruit image data. The

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Archipelago

Knowledge Reuse Mechanisms for Categorizing Related Image Setsin a new classification task. Knowledge reuse helps in constructing better generalizing classifiers given few training examples and for evaluating images for search in an image database. In particular, we discuss a knowledge reuse framework in which a . improves the performance of the . using inform