Hybrid Soft Computing for Multilevel Image and Data by Sourav De, Visit Amazon's Siddhartha Bhattacharyya Page,

By Sourav De, Visit Amazon's Siddhartha Bhattacharyya Page, search results, Learn about Author Central, Siddhartha Bhattacharyya, , Susanta Chakraborty, Paramartha Dutta

This booklet explains effective recommendations for segmenting the depth degrees of alternative kinds of multilevel photos. The authors current hybrid tender computing thoughts, that have benefits over traditional gentle computing recommendations as they contain facts heterogeneity into the clustering/segmentation procedures.

This is an invaluable advent and reference for researchers and graduate scholars of machine technology and electronics engineering, relatively within the domain names of snapshot processing and computational intelligence.

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An algorithm has been proposed in [134, 206] to segment an image based on fuzzy connectedness using dynamic weights (DyW). Fuzzy connectedness is measured on the basis of the linear combination of an object-feature and homogeneity component using fixed weights. Fuzzy set theory and fuzzy logic can incorporate the colour and spatial uncertainty and guide the segmentation process [207]. Fuzzy rules have been applied for the region dissimilarity function and the total merging process in [208, 209].

3. number of generations is greater than some predefined threshold. GAs find applications in the field of image processing, data clustering [82], path finding [83], project management, portfolio management [84] etc. 5 Classical Differential Evolution Basically, differential evolution (DE) [85] is a well-known optimisation technique in the evolutionary computation family. This algorithm is a variant of genetic algorithms. It also applies biological operations, like crossover, mutation and selection operation on a population.

X n,G − X r,G ) where, i, m, n, r are mutually different integers and randomly selected within the range [1, N P]. The scaling factor, AF, is applied as a positive mutation constant for the amplification of the difference vector [85, 86]. − → 1 D , . . , ti,G } is generated with the help In the crossover, the trial vector T i,G = {ti,G − → − → of each pair of target vector, X i,G and the same indexed mutant vector A i,G . 10) where, j = 1, 2, . . , D and jrand is the randomly chosen integer in the range [1, D].

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