Multiscale Geometric Feature Eztraction and Selection Algorithms of Similar Objects
To recognize objects with similar shapes, a scheme for feature extraction and selection based on Multiscale transformation is proposed in this paper. Multiscale Geometric Analysis is characterized with directionality and anisotropy, and the subbands in different decomposed scales could present different classification capabilities. The scheme applies timefrequency-localized feature algorithm as well as probability information measurement to choose the decomposing scale and directional subband in order to maximize similarity between objects in the same class while minimize similarity of objects in different classes. To some extent, the algorithm proposed has resolved the random selection problems of decomposing scale, direction number and directional sub-bands in Multiscale transforms. The experimental results have verified the effectiveness of the algorithm.
Multiscale Geometric Transform feature eztraction probability information measurements contourlet transform similar target
Xue Mei Xiaomin Gu Jinguo Lin Li Wu
College of Automation and Electrical Engineering, Nanjing University of Technology Nanjing, China
国际会议
2010 International Conference on Image Analysis and Signal Processing(2010 图像分析与信号处理国际会议 IASP 10)
厦门
英文
399-402
2010-04-12（万方平台首次上网日期，不代表论文的发表时间）