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Optical Engineering • Open Access

Object detection using voting spaces trained by few samples
Author(s): Pei Xu; Mao Ye; Xue Li; Lishen Pei; Pengwei Jiao

Paper Abstract

A method to detect generic objects by training with a few image samples is proposed. A new feature, namely locally adaptive steering (LAS), is proposed to represent local principal gradient orientation information. A voting space is then constructed in terms of cells that represent query image coordinates and ranges of feature values at corresponding pixel positions. Cell sizes are trained in voting spaces to estimate the tolerance of object appearance at each pixel location. After that, two detection steps are adopted to locate instances of object class in a given target image. At the first step, patches of objects are recognized by densely voting in voting spaces. Then, the refined hypotheses step is carried out to accurately locate multiple instances of object class. The new approach is training the voting spaces based on a few samples of the object. Our approach is more efficient than traditional template matching approaches. Compared with the state-of-the-art approaches, our experiments confirm that the proposed method has a better performance in both efficiency and effectiveness.

Paper Details

Date Published: 16 September 2013
PDF: 13 pages
Opt. Eng. 52(9) 093105 doi: 10.1117/1.OE.52.9.093105
Published in: Optical Engineering Volume 52, Issue 9
Show Author Affiliations
Pei Xu, Univ. of Electronic Science and Technology of China (China)
Mao Ye, Univ. of Electronic Science and Technology of China (China)
Xue Li, The Univ. of Queensland (Australia)
Lishen Pei, Univ. of Electronic Science and Technology of China (China)
Pengwei Jiao, Univ. of Electronic Science and Technology of China (China)

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