A learning-based spatial processing method for the detection of point targets

Authors:
Zhijun Liu;Xubang Shen;Hongshi Sang
Affiliations:
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

Citing 3
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Abstract

In this paper, we present an efficient learning-based method for the detection of point targets in images. In the scheme, the probabilistic visual learning (PVL) technique is used for modeling the appearance of point targets and constructing a saliency measure function. Based on this function and the feature vector extracted at each pixel position and a target saliency map is formed by lexicographically scanning the input image. We treat such saliency map as a spatially filtered result of input image. Experimental results show that the proposed algorithm outperforms other filter-based methods.