Object detection based on 2D canonical correlation analysis

Guofeng Zhang; Weida Zhou; Weihua Ren

doi:10.1117/12.750452

15 November 2007 Object detection based on 2D canonical correlation analysis

Guofeng Zhang, Weida Zhou, Weihua Ren

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67882P (2007) https://doi.org/10.1117/12.750452
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

A novel approach combining 2DCCA, edge detector, and corner detector for object detection is proposed in this paper. The detection system consists of two stages. In the first stage, edge and corner information is obtained by edge detector and corner detector. By setting range for the number of edge pixel and corner in the scanning window, a large number of non-object windows are rejected. In the second stage, the classifier trained by 2DCCA is combined with slide window method so that further non-object windows are rejected. For the case that one object is simultaneously contained in several windows, the algorithm of determining the best position of object is designed. Compared with related approaches, our method has advantage of obtaining higher precision under the similar recall. The performance of the proposed approach is illustrated by experimental results.

Citation Download Citation

Guofeng Zhang, Weida Zhou, and Weihua Ren "Object detection based on 2D canonical correlation analysis", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67882P (15 November 2007); https://doi.org/10.1117/12.750452

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