Evolutionary computation based optimization of image Zernike moments shape feature vector |
| |
Authors: | Maofu Liu Hujun Hu Ming Zhong Yanxiang He Fazhi He |
| |
Institution: | [1]College of Computer Science and Technology, WuhanUniversity of Science and Technology, Wuhan 430081, Hubei, China [2]School of Computer, Wuhan University, Wuhan 430072,Hubei, China |
| |
Abstract: | The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high
dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements
making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should
be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary
computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility
of the optimization algorithm.
Biography: LIU Maofu (1977–), male, Associate professor, Ph.D., research direction: image mining, natural language processing. |
| |
Keywords: | Zernike moment image Zernike moments shape feature vector image reconstruction evolutionary computation |
本文献已被 维普 SpringerLink 等数据库收录! |
|