By Siddhartha Bhattacharyya
The booklet indicates hybrids deriving from 3 major ways: fuzzy platforms, basically used for dealing with real-life difficulties that contain uncertainty man made neural networks, often utilized for computing device cognition, studying, and popularity and evolutionary computation, in most cases used for seek, exploration, effective exploitation of contextual info, and optimization.
The contributed chapters talk about either the strengths and the weaknesses of the techniques, and the ebook might be necessary for researchers and graduate scholars within the domain names of picture processing and computational intelligence.
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Additional info for Hybrid Soft Computing for Image Segmentation
5, experiments are conducted over two standard test images and two real satellite images to evaluate the effectiveness of the GSA-GA. Finally, the chapter is concluded in Sect. 6. 2 Formulation of Multilevel Thresholding Problem Optimal thresholding methods search for the optimum thresholds by minimizing or maximizing an objective function. Parameters in the utilized objective function are made up by the selected thresholds in each iteration. Specially, the purpose of multilevel thresholding is to classify the Num pixels of an image into K classes using a set of thresholds Th = (td1 , td2 , .
To start the GSA-GA for multilevel thresholding, initial population X = [X 1 , X 2 , . . , X i , . . , 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.
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 . Well-known SIAs include ACO , ABC , PSO , cuckoo search algorithm (CS) , Bat Algorithm (BA) , and Gray Wolf Optimizer (GWO) , etc. In these algorithms, one special particle/individual with the best performance is usually chosen 28 G. Sun et al.