@article {12735,
	title = {A low complexity interest point detector},
	journal = {IEEE Signal Processing Letters},
	volume = {22},
	year = {2014},
	month = {2014/2},
	pages = {172-176},
	chapter = {172},
	abstract = {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 = {Interest Point Detectors; Laplacian of Gaussian; Scale-space; Scale-space representation},
	isbn = {1070-9908},
	doi = {10.1109/LSP.2014.2354237},
	url = {http://www.engineeringvillage2.org/search/results/fulltext.url?docID=cpx_M74829d86148f5ff1a0dM7e8410178163125},
	author = {Chen, Jie;  *Duan, Ling-Yu;  Gao, Feng;  Cai, Jianfei;  Kot, Alex C.;  Huang, Tiejun}
}
