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A Robust Optimum Thresholding Method Based on Local Intensity Mapping and Class Uncertainty Theory 会议论文
中国澳门
作者:  Yuntao Wang;  Guoyuan Liang;  Sheng Huang;  Can Wang;  Xinyu Wu
收藏  |  浏览/下载：16/0  |  提交时间：2018/02/02
An optimization method for improving the accuracy of centroid computation based on Shack-Hartmann wavefront sensor 会议论文
作者:  Xiaoyu Zhang [1,2,3], Caixia Wang[1,2]
收藏  |  浏览/下载：17/0  |  提交时间：2018/12/20
Game theoretic approach to global climate control 会议论文
Jinming, Du; Long, Wang
收藏  |  浏览/下载：3/0  |  提交时间：2017/12/03
Optimum threshold selection method of centroid computation for Gaussian spot 会议论文
Proceedings of SPIE - The International Society for Optical Engineering, 2015
作者:  Li, Xuxu;  Li, Xinyang;  Wang, Caixia
收藏  |  浏览/下载：15/0  |  提交时间：2016/11/24
Hierarchical artificial bee colony optimizer for multilevel threshold image segmentation 会议论文
Las Vegas, NV, USA, July 21-24, 2014
作者:  Hu KY(胡琨元);  Chen HN(陈瀚宁);  He MW(何茂伟)
收藏  |  浏览/下载：16/0  |  提交时间：2019/08/03
Discussion on Debris Flow Drainage Canal Damage Type and Reasons 会议论文
第35届国际水力学大会, Chengdu China, 2013-9-15
作者:  Tao Wang;  Jiangang Chen;  Wei Zhong;  Xiaoqing Chen
收藏  |  浏览/下载：16/0  |  提交时间：2014/05/15
De-noising based on wavelet analysis and Bayesian estimation for low-dose X-ray CT 会议论文
作者:  Ye, Fang;  Yabin, Zhou;  Dongwei, Ge;  Zhan, Zhou
收藏  |  浏览/下载：2/0  |  提交时间：2019/12/18
Optimum Soft Threshold Technique for Fractal Signals Denoising 会议论文
3rd International Conference on Pervasive Computing and Applications, OCT 06-08, 2008
作者:  Zhao, Yongjian;  Gong, Peng;  Wang, Hongrun
收藏  |  浏览/下载：3/0  |  提交时间：2019/12/31
Waveform Estimation of Fractal Signals Using Optimum Soft Threshold Technique 会议论文
Pacific/Asia Workshop on Computational Intelligence and Industrial Application, DEC 19-20, 2008
作者:  Zhao, Yongjian;  Wang, Hongrun
收藏  |  浏览/下载：1/0  |  提交时间：2019/12/31
Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文
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：21/0  |  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.  

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