Download Hybrid Soft Computing for Image Segmentation by Siddhartha Bhattacharyya PDF
By Siddhartha Bhattacharyya
The ebook indicates hybrids deriving from 3 major ways: fuzzy structures, essentially used for dealing with real-life difficulties that contain uncertainty synthetic neural networks, often utilized for desktop cognition, studying, and popularity and evolutionary computation, commonly used for seek, exploration, effective exploitation of contextual info, and optimization.
The contributed chapters speak about either the strengths and the weaknesses of the methods, and the booklet might be worthwhile for researchers and graduate scholars within the domain names of photo processing and computational intelligence.
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Extra resources for Hybrid Soft Computing for Image Segmentation
Example text
X N ] where X i = [xi1 , xi2 , . . , xiD ] should be randomly generated first. Each particle X i is comprised of a set of gray values, which stands for a candidate solution of the required threshold values. The size of the population is N (can be set by users), and dimension of each particle is D, which is also the number of desired thresholds: D = m. In the iteration process, the fitness value of each particle is calculated from the Kapur’s entropy or Otsu criterion using Eqs. (4)–(5) and (8)–(9), respectively.
93(1), 139–153 (2013) 15. : A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013) 16. : Cuckoo search and firefly algorithm: overview and analysis. Stud. Comput. Intell. 516, 1–26 (2013) 17. : Swarm intelligence algorithms for multilevel image thresholding. In: Intelligent Systems in Technical and Medical Diagnostics, pp. 301–311. Springer, Berlin (2014) 18. : Image thresholding segmentation based on a novel beta differential evolution approach.
In Sect. 4, we briefly reviewed the hybrid NAs based multilevel thresholding. 1 Swarm Intelligence-Based Optimization Algorithms Based Multilevel Thresholding SIAs are mainly proposed by mimicking the foraging or hunting behaviors of different species like ants, bees, birds, cuckoos, bats, wolf, and so on [45]. Well-known SIAs include ACO [29], ABC [31], PSO [46], cuckoo search algorithm (CS) [47], Bat Algorithm (BA) [48], and Gray Wolf Optimizer (GWO) [49], etc. In these algorithms, one special particle/individual with the best performance is usually chosen 28 G.