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Human hand detection using robust local descriptors

  • Beihang University
  • Nokia

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

To date, human hand detection in images remains a challenging task due to the variable lighting conditions, hand appearances and background noise. In this paper, we present an effective strategy based on feature fusion for detecting hands with cluttered surroundings. To form the fusions, we propose three novel noise invariant features, namely: 1) NCHOG (Noise Compensated Histogram of Oriented Gradients), 2) NCLBP (Noise Compensated Local Binary Patterns), and 3) HPCP (Histograms of Pairs of Circumference Pixels). We show the superior performance of the NCHOG and the NCLBP descriptors over their existing traditional counterparts, i.e., HOG and LBP. Merging our novel features with existing features in different permutations, and applying Partial Least Squares (PLS) based feature weighting, yields excellent detection results on our own dataset of hand images with variegated and complex backgrounds.

源语言英语
主期刊名Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
DOI
出版状态已出版 - 2013
活动2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013 - San Jose, CA, 美国
期限: 15 7月 201319 7月 2013

出版系列

姓名Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013

会议

会议2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
国家/地区美国
San Jose, CA
时期15/07/1319/07/13

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