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	A simple and fast moving object segmentation based on H.264 compressed domain information (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Computational and Information Sciences, ICCIS 2012, August 17, 2012 - August 19, 2012, Chongqing, China 作者: Chen X.; Chen X.; Chen X.; Sun L. 收藏 \| 浏览/下载：22/0 \| 提交时间：2013/03/25 The paper presents a simple and fast approach for moving object segmentation based on H.264 compressed domain information for the application of indoor video surveillance with static camera. Due to the characteristics of indoor video surveillance the proposed method of segmentation avoids complicated background model like Gaussian Mixture background model. On the contrary it chooses some simple information like the type of Macroblock etc.. Experimental results of several specific H.264 compressed video sequences demonstrate the good segmentation quality of the proposed approach. 2012 IEEE.
	智能视频监控系统中的运动目标检测技术研究学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2010 常晓夫收藏 \| 浏览/下载：70/0 \| 提交时间：2015/09/02 智能视频监控运动目标检测多种类视觉特征混合高斯定量分析 smart video surveillance moving object detection multi-category visual features mixture of Gaussian model quantitative analysis
	CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang J.-L.; Wang J.-L.; Li D.-Y.; Wang Y.-P. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 In the procedure of computed radiography imaging we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail the author think that the major two noises are Gaussian white noise and Poisson noise. Then the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images a multiscale image filtering algorithm which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM according to scale size and achieve the experiments as well as the comparison with other denoising methods were presented at last.