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

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

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

The binary feature descriptors such as binary robust independent elementary features (BRIEF), Oriented rotated binary robust independent elementary features (ORB), and fast retin a key point (FREAK) usually perform binarisation on the intensity comparisons, thus they lose some useful information. In this study, the authors propose an effective bi nary i mage descriptor which is called significant bi t-planes based local binary pattern for visual recognition. First, the authors divide an image into several sub regions according t o t he in ten s i t y orders to incorporate the spatial information . Then the authors extract the higher bit planes for all the sub region s an d sort the adjacent neighbour bits based o n t he corresponding intensity orders, which make the descriptor invariant to rotation. In order to further improve the discriminative ability , the authors sample the multi-s ca le neighbours and average the adjacent pixels and extract the feature descript or from the higher bit planes. Since the author s directly perform operation on the significant bit p lanes without quantisation, the authors decrease the information loss To some extent. The descriptor has demonstrated a better performance over the state-of-the-art binary descriptors as well as scale invariant feature transform on two recognition benchmarks (i.e. Kentucky and ETHZ) and PASCAL 2007 for image classification.


 


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  • Model: PROJ7490
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Friday 28 July, 2017.

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