FITTING-IMPROVED SNAKE WITH ROBUST POLYGON APPROXIMATION, ADAPTATION AND REFINEMENT

• Main Authors: Chun-Chai Chang, 張峻嘉
• Other Authors: Din-Yuen Chan
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/05219691808546500489

碩士 === 國立嘉義大學 === 資訊工程學系研究所 === 96 === In this paper, we propose a fitting-improved adaptive (FIA) snake (FIA-snake) adapted to various images’ segmentations in the noisy environment. The FIA-snake performs three stages: fast evenly-distributed initialization (object-area marking), FIA evolution, which leads all the parameters of snake model to be automatically adaptive to the image in problem, and directional compensation evolution (DCE). The integrity of these three stages can make one part greatly elevate the effectiveness its subsequent one. Firstly, FIA-snake applies an estimation method with uncertainty minimization to compute the possible ranges of gradient magnitudes of object boundary for the snake initialization. The FIA evolution will adapt the weights of the snake force components according to their contributions in edge fitness, and simultaneously renormalize external and internal forces. After the first snake convergence, DCE identifies the unqualified snake fragments by block-based texture analysis and then repair them toward the object border by the modified internal force called directional compensation force (DCF). The simulation results demonstrate that FIA-snake can improve the performance of snake very much, and outperform Gradient Vector Flow (GVF) in noisy images.