A Low Complexity Interest Point Detector

Citation:

Chen, Jie; *Duan L-Y; GF; CJ; KAHTC ;. A Low Complexity Interest Point Detector. IEEE Signal Processing Letters. 2015;22(2):172-176.

摘要:

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.

SCI被引用次数:7.