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9. A Local Feature Descriptor Invariant to Complex Illumination Changes

Author

Luo Yong and Chen Yuan-zhi

Subjects
Local binary patterns, business.industry, 05 social sciences, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Corner detection, FREAK, Scale-invariant feature transform, Pattern recognition, Binary pattern, Scale space, Computer Science::Computer Vision and Pattern Recognition, Histogram, Computer Science::Multimedia, 0502 economics and business, 050211 marketing, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, Invariant (mathematics), business, 050203 business & management, Mathematics
Abstract

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.

Published
2016

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