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Information Journal Paper

Title

OPTIMAL MULTILEVEL IMAGE THRESHOLDING USING THE TEACHING-LEARNING-BASED OPTIMIZATION

Pages

  51-62

Abstract

 Image thresholding is a popular method for IMAGE SEGMENTATION. HISTOGRAM is used for IMAGE SEGMENTATION in image thresholding. In this paper, a MULTILEVEL IMAGE THRESHOLDING is proposed based on TEACHING-learning-based optimization (TLBO). TLBO is a new population-based metaheuristic inspired by learners and teacher in a classroom. The optimal thresholds are found by maximizing Kapur’s (entropy criterion) thresholding function. The performance of TLBO is explained by considering five images. In addition, the performance is compared with three well known population-based metaheuristics: particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE).Results show that TLBO presents the better performance in terms of fitness value, peak signal to noise ratio (PSNR), Structural-Similarity index (SSIM), and stability.

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    APA: Copy

    MOUSAVIRAD, SEYED JALALEDDIN, & EBRAHIMPOUR KOMLEH, HOSSEIN. (2016). OPTIMAL MULTILEVEL IMAGE THRESHOLDING USING THE TEACHING-LEARNING-BASED OPTIMIZATION. MACHINE VISION AND IMAGE PROCESSING, 2(2 ), 51-62. SID. https://sid.ir/paper/265706/en

    Vancouver: Copy

    MOUSAVIRAD SEYED JALALEDDIN, EBRAHIMPOUR KOMLEH HOSSEIN. OPTIMAL MULTILEVEL IMAGE THRESHOLDING USING THE TEACHING-LEARNING-BASED OPTIMIZATION. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2016;2(2 ):51-62. Available from: https://sid.ir/paper/265706/en

    IEEE: Copy

    SEYED JALALEDDIN MOUSAVIRAD, and HOSSEIN EBRAHIMPOUR KOMLEH, “OPTIMAL MULTILEVEL IMAGE THRESHOLDING USING THE TEACHING-LEARNING-BASED OPTIMIZATION,” MACHINE VISION AND IMAGE PROCESSING, vol. 2, no. 2 , pp. 51–62, 2016, [Online]. Available: https://sid.ir/paper/265706/en

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