Genetic Algorithm For Segmentation On a Linked Pyramid

• Main Authors: Luan-Jiau Chiu, 邱鸞嬌
• Other Authors: Shu-yuan, Chen
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/21050100717971090455

碩士 === 元智大學 === 電資與資訊工程研究所 === 86 === In this study, we propose a genetic algorithm for image segmentation on a linked regular pyramid with connectivity preserving. The hierarchy of pyramid is placed into a GA processing for evolution. Since the genetic algorithms always provide global optimal solutions and pyramid algorithms support fast parallel processing for multiresolution image analysis, near-optimal multiresolution segmentation results can be obtained. Some attributes of irregular pyramid, such as clustering and connectivity preserving relinking, are also involved in our regular hierarchy to construct a simple and powerful computational structure. Since GA evolution makes the parent values and child-parent links computation reach near-optimal result, a more efficient pyramid data can be obtained. Consequently, these pyramid data can be used to produce the multiresolution segmentation results. Moreover, some novel ideas are also proposed in this study. First, the chromosome sizes dependent on level sizes are used to facilitate the GA evolution on each level. Then, an adaptable candidate parent window is used to obtain different segmented results. In general, small window leads to compact connected segment, while large window leads to fine (sometimes scattered) connected segment. Experimental results prove the effectiveness, robustness and feasibility of our algorithm.