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A Low Complexity Interest Point Detector
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DOI：10.1109/LSP.2014.2354237.png)
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Abstract
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Interest point detection is a fundamental approach to feature extraction in computer vision tasks. To handle the scale invariance, interest points usually work on the scale-space representation of an image. In this letter, we propose a novel block-wise scale-space representation to significantly reduce the computational complexity of an interest point detector. Laplacian of Gaussian (LoG) filtering is applied to implement the block-wise scale-space representation. Extensive comparison experiments have shown the block-wise scale-space representation enables the efficient and effective implementation of an interest point detector in terms of memory and time complexity reduction, as well as promising performance in visual search.
Keywords:
Block-wise scale-space representation
interest point detector
Laplacian of Gaussian
scale-space
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