Using Local Invariant in Occluded Object Recognition by Hopfield Neural Network

• Main Authors: Chih-Hung Tzeng, 曾智宏
• Other Authors: Innchyn Her
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/61679627653397484929

碩士 === 國立中山大學 === 機械與機電工程學系研究所 === 91 === In our research, we proposed a novel invariant in 2-D image contour recognition based on Hopfield-Tank neural network. At first, we searched the feature points, the position of feature points where are included high curvature and corner on the contour. We used polygonal approximation to describe the image contour. There have two patterns we set, one is model pattern another is test pattern. The Hopfield-Tank network was employed to perform feature matching. In our results show that we can overcome the test pattern which consists of translation, rotation, scaling transformation and no matter single or occlusion pattern.