2012 ©
Journal Publication
Research Title Multilevel thresholding selection based on chaotic multi-verse optimization for image segmentation 
Date of Distribution 21 November 2016 
     Title of the Conference 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016 
     Organiser Department of computer Science, Faculty of Science, Khon Kaen University, Thailand 
     Conference Place Pullman Khon Kaen Raja Orchid Hotel, Khon Kaen, Thailand 
     Province/State Khon Kaen 
     Conference Date 13 July 2016 
     To 15 July 2016 
Proceeding Paper
     Volume 2016 
     Page 1 - 6 
     Abstract Multilevel thresholding is the most important method for image processing. Conventional multilevel thresholding methods have proven to be efficient in bi-level thresholding; however, when extended to multilevel thresholding, they prove to be computationally more costly, as they comprehensively search the optimal thresholds for the objective function. This paper presents a chaotic multi-verse optimizer (CMVO) algorithm using Kapur's objective function in order to determine the optimal multilevel thresholds for image segmentation. The proposed CMVO algorithm was applied to various standard test images, and evaluated by peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The CMVO algorithm efficiently and accurately searched multilevel thresholds and reduced the required computational times. 
577020024-5 Mr. TANACHAPONG WANGCHAMHAN [Main Author]
Science Doctoral Degree

Peer Review Status มีผู้ประเมินอิสระ 
Level of Conference นานาชาติ 
Type of Proceeding Full paper 
Type of Presentation Oral 
Part of thesis true 
Presentation awarding false 
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