Object recognition using rotation invariant local binary pattern of significant bit planes

Abstract— The binary feature descriptors such as binary robust independent elementary features (BRIEF), oriented rotated binary robust independent elementary features (ORB), and fast retina keypoint (FREAK) usually perform binarisation on the intensity comparisons, thus they lo se some useful information. In this study, the authors propose an effective binary image descriptor which is called significant bit-planes-based local binary pattern for visual recognition. First, the authors divide an image into several sub regions according to the intensity orders to incorporate the spatial information . Then the authors extract the higher bit planes for all the sub regions and sort the adjacent neighbour bits based on the corresponding intensity orders, which make the descriptor invariant to rotation. In order to further improve the discriminative ability , the authors sample the multi-scale neighbours and average the adjacent pixels and extract the feature descriptor from the higher bit planes. Since the authors directly perform operation on the significant bit planes without quantisation, the authors decrease the information loss to some extent. < final year projects >