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  • 标题:A Local Feature Descriptor Invariant to Complex Illumination Changes
  • 作者:Luo Yong ; Chen Yuanzhi
  • 期刊名称:TELKOMNIKA (Telecommunication Computing Electronics and Control)
  • 印刷版ISSN:2302-9293
  • 出版年度:2016
  • 卷号:14
  • 期号:2A
  • 页码:289-297
  • DOI:10.12928/telkomnika.v14i2A.4329
  • 语种:English
  • 出版社:Universitas Ahmad Dahlan
  • 摘要:In this paper, we propose a novel and robust local image descriptor to resolve the problem of complex illumination variations on low-level feature matching. Many classic local feature descriptors are invariant to linear illumination or monotonous intensity shift, but cannot handle more complex nonlinear illumination changes, which often occur due to the different time of exposure, the viewing direction, different types of light-surface interactions and other varying brightness changes. The presented descriptor that extracts the histograms of oriented gradients and Uniform Symmetric-Local Binary Pattern (US-LBP) features for each feature point neighborhood in the scale space. Extensive experiments show that the proposed descriptor outperforms many state-of-the-art descriptors such as SIFT, ORB, SURF and FREAK under the problem of local image matching, especially demonstrating the effectiveness of our method under complex illumination changes.
  • 其他摘要:In this paper, we propose a novel and robust local image descriptor to resolve the problem of complex illumination variations on low-level feature matching. Many classic local feature descriptors are invariant to linear illumination or monotonous intensity shift, but cannot handle more complex nonlinear illumination changes, which often occur due to the different time of exposure, the viewing direction, different types of light-surface interactions and other varying brightness changes. The presented descriptor that extracts the histograms of oriented gradients and Uniform Symmetric-Local Binary Pattern (US-LBP) features for each feature point neighborhood in the scale space. Extensive experiments show that the proposed descriptor outperforms many state-of-the-art descriptors such as SIFT, ORB, SURF and FREAK under the problem of local image matching, especially demonstrating the effectiveness of our method under complex illumination changes.
  • 关键词:Harris corner detector;Illumination invariance;Histograms of oriented gradients;Uniform symmetric-Local Binary Pattern
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