The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization

碩士 === 國立屏東商業技術學院 === 資訊工程系(原資訊科技系) === 97 === Several methods has so far been proposed in evolutionary computation, that include the particle swarm optimization (PSO), the ant colony optimization (ACO), the genetic algorithm (GA), the simulated annealing (SA). This thesis applies a new evolutionary...

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Main Authors:	Ting-Wei Jiang, 江庭維
Other Authors:	Ming-Huwi Horng
Format:	Others
Language:	en_US
Published:	2009
Online Access:	http://ndltd.ncl.edu.tw/handle/09451531197747393876

id	ndltd-TW-097NPC05392006
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spelling	ndltd-TW-097NPC053920062016-05-04T04:31:30Z http://ndltd.ncl.edu.tw/handle/09451531197747393876 The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization 蜜蜂配對最佳法於影像分割及向量量化之應用 Ting-Wei Jiang 江庭維碩士國立屏東商業技術學院資訊工程系(原資訊科技系) 97 Several methods has so far been proposed in evolutionary computation, that include the particle swarm optimization (PSO), the ant colony optimization (ACO), the genetic algorithm (GA), the simulated annealing (SA). This thesis applies a new evolutionary computation method, the honey bee mating optimization algorithm (HBMO), for the application in multi-level thresholds selection and vector quantization for image compression. The results of HBMO are compared with those of other widely-used algorithm. The experimental results reveal that the application of HBMO is feasible and efficient in the research areas of image processing. Furthermore, it is high potential to further design algorithm in other applications of image processing. Ming-Huwi Horng 洪明輝 2009 學位論文 ; thesis 137 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立屏東商業技術學院 === 資訊工程系(原資訊科技系) === 97 === Several methods has so far been proposed in evolutionary computation, that include the particle swarm optimization (PSO), the ant colony optimization (ACO), the genetic algorithm (GA), the simulated annealing (SA). This thesis applies a new evolutionary computation method, the honey bee mating optimization algorithm (HBMO), for the application in multi-level thresholds selection and vector quantization for image compression. The results of HBMO are compared with those of other widely-used algorithm. The experimental results reveal that the application of HBMO is feasible and efficient in the research areas of image processing. Furthermore, it is high potential to further design algorithm in other applications of image processing.
author2	Ming-Huwi Horng
author_facet	Ming-Huwi Horng Ting-Wei Jiang 江庭維
author	Ting-Wei Jiang 江庭維
spellingShingle	Ting-Wei Jiang 江庭維 The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization
author_sort	Ting-Wei Jiang
title	The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization
title_short	The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization
title_full	The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization
title_fullStr	The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization
title_full_unstemmed	The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization
title_sort	application of image thresholding and vector quantization using honey bee mating optimization
publishDate	2009
url	http://ndltd.ncl.edu.tw/handle/09451531197747393876
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The Application of Image Thresholding and Vector Quantization Using Honey Bee Mating Optimization

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