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	Infrared small target detection algorithm based on entropy weighted multiscale local contrast 会议论文 OAI收割 ELECTR NETWORK, 2022-11-24 作者: Wei, Jingbo; Chen, Rongli; Zhang, Ximing; Zhao, Hui \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2023/06/19 infrared small target multiscale local contrast entropy operator adaptive threshold
	Edge detection using matched filter 会议论文 OAI收割 27th Chinese Control and Decision Conference, CCDC 2015, Qingdao, China, May 23-25, 2015 作者: Luo HB(罗海波); Jiao AB(焦安波); Xu LY(许凌云); Shao CY(邵春艳) 收藏 \| 浏览/下载：29/0 \| 提交时间：2015/11/18 edge detection matched filter local adaptive threshold real-time application noise suppression
	Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China Meng B.; Zhu M.; Han G.; Wu Z. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently we first use Sobel algorithm to extract the profile of the object. Then we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.