A Gradient Extension of Center Symmetric Local Binary Patterns for Robust RGB-NIR Image Matching

Scene acquisition using RGB and Near Infra-Red (NIR) filters generates useful visual information about scene contents. But it induces significant intensity and textural changes between RGB and NIR images of the same scene. It becomes a challenging problem to perform interest point based image matching under such intensity and textural changes. To cope with this problem, a novel method for the description of interest points is proposed. The method proposed is based on Center Symmetric-Local Binary Patterns (CS-LBP) which extracts distinct image features from intensity and gradient magnitude maps of the image patches centered at interest points. Those features are then used in the SIFT algorithm to compute robust descriptors against intensity and textural changes. The experimental results show that the method proposed improves the descriptor matching between RGB and NIR images and achieves better image matching results than CS-LBP and SIFT based methods for the description of interest points.

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