中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共4条，第1-4条

帮助

条数/页：排序方式：
	Analysis on local optimum existence form of K-means-type 会议论文 OAI收割 5th International Conference on Intelligent Control and Information Processing, ICICIP 2014, Singapore, Singapore, December 10-12, 2014 作者: Zhang CN(张承宁); Xia QH(夏庆华); Zhao, Fei; Zou YY(邹媛媛) 收藏 \| 浏览/下载：50/0 \| 提交时间：2015/07/05 clustering K-means local optimum
	Analysis on Local Optimum Existence Form of K-means-type 会议论文 OAI收割 DEC 10-12, 2014 作者: Xia Qinhua; Zou Yuanyuan; Zhao Fei; Zhang Chengning; Zhang, CN \| 收藏 \| 浏览/下载：13/0 \| 提交时间：2018/01/12 Clustering K-means Local Optimum
	An adaptive image contrast enhancement based on differential evolution (EI CONFERENCE) 会议论文 OAI收割 2010 3rd International Congress on Image and Signal Processing, CISP 2010, October 16, 2010 - October 18, 2010, Yantai, China Yang Q. 收藏 \| 浏览/下载：16/0 \| 提交时间：2013/03/25 Tubbs proposed a regularized incomplete beta function to represent some nonlinear transform functions most commonly used in image contrast enhancement. But how to define the coefficients of the beta function adaptively is still a problem. Applying the differential evolution in image contrast enhancement we utilize the global quickly search ability of the differential evolution algorithm adaptive mutation search at last searches the optimal values of beta function and gets an adaptive contrast enhanced image. To avoid trapping into local optimum a chaotic differential evolution algorithm is proposed. Experimental results show that the proposed algorithm can find the global optimal in few iterations and save largely computational time and complexity. 2010 IEEE.
	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：24/0 \| 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.