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 e-book explains effective suggestions for segmenting the depth degrees of alternative kinds of multilevel pictures. The authors current hybrid gentle computing ideas, that have benefits over traditional smooth computing options as they contain facts heterogeneity into the clustering/segmentation procedures.

This is an invaluable creation and reference for researchers and graduate scholars of laptop technological know-how and electronics engineering, quite within the domain names of photo processing and computational intelligence.

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Fuzzy rules have been applied for the region dissimilarity function and the total merging process in [208, 209]. Other fuzzy homogeneity based colour image segmentation methods have been presented in the literature [193, 210]. Yang et al. [211] proposed an eigen-based fuzzy Cmeans (FCM) clustering algorithm to segment colour images. In this method, the eigenspace is formed by dividing the selected colour pixels of the image. colour image segmentation is achieved by combining the eigenspace transform and the FCM method.

Incorporating the spatial constraints to segmentation algorithms [200, 202] and imposing different features or dissimilarity index that is insensitive to intensity variations in the objective function of FCM [203, 204] are two different classifications of the modified FCM. Rezaee et al. [128, 205] introduced an unsupervised image segmentation technique. The segmentation process is the combination of pyramidal image segmentation with the fuzzy c-means clustering algorithm. The root labelling technique is applied to separate each layer of the pyramid into a number of regions.

Jiang et al. [179] presented a literature review on computer-aided diagnosis, medical image segmentation, edge detection, etc. by different neural network architectures. In this article, different techniques as well as different examples of neural network architectures are illustrated for medical image segmentation. A new version of the self-organising map (SOM) network, named moving average SOM (MA-SOM), is applied to segment the medical images [180]. SOFM has also been used to segment medical images through identification of regions of interest (ROI) [159, 160].

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