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Proceedings Paper

Object detection based on improved color and scale invariant features
Author(s): Mengyang Chen; Peng Fan
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Paper Abstract

A novel object detection method which combines color and scale invariant features is presented in this paper. The detection system mainly adopts the widely used framework of SIFT (Scale Invariant Feature Transform), which consists of both a keypoint detector and descriptor. Although SIFT has some impressive advantages, it is not only computationally expensive, but also vulnerable to color images. To overcome these drawbacks, we employ the local color kernel histograms and Haar Wavelet Responses to enhance the descriptor's distinctiveness and computational efficiency. Extensive experimental evaluations show that the method has better robustness and lower computation costs.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749516 (30 October 2009); doi: 10.1117/12.832493
Show Author Affiliations
Mengyang Chen, Beijing Univ. of Posts and Telecommunications (China)
Peng Fan, Beijing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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