A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

被引：27

作者：

Pare, Shreya ^{[1
]}

Kumar, Anil ^{[1
]}

Bajaj, Varun ^{[1
]}

Singh, Girish Kumar ^{[2
]}

机构：

[1] PDPM Indian Inst Informat Technol Design & Mfg, Jabalpur 482005, MP, India

[2] Indian Inst Technol Roorkee, Uttarakhand 247667, India

来源：

IEEE-CAA JOURNAL OF AUTOMATICA SINICA | 2019年 / 6卷 / 06期

关键词：

Color image segmentation; Kapur's entropy; multilevel thresholding; Otsu method; swarm based optimization algorithms; Tsalli's entropy; SATELLITE IMAGE SEGMENTATION; MINIMUM CROSS-ENTROPY; NATURE-INSPIRED OPTIMIZATION; SEARCH ALGORITHM; 2D HISTOGRAM; SELECTION; EVOLUTIONARY; KAPURS; OTSU;

D O I：

10.1109/JAS.2017.7510697

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions: Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.

引用

页码：1471 / 1486

页数：16

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