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	A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence 期刊论文 OAI收割 International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 6, 页码: 855-866 作者: Zun-Ran Wang; Chen-Guang Yang; Shi-Lu Dai \| 收藏 \| 浏览/下载：67/0 \| 提交时间：2021/02/22 3D point cloud compression motion estimation signatures of histograms orientation 3D point cloud matching predicted frame and intra frame.
	Efficient Vehicle Detection and Orientation Estimation by Confusing Subsets Categorization 会议论文 OAI收割中国四川成都, 2016-12 作者: Li FM(李非墨); Lan XS(兰晓松); Li SX(李书晓); Zhu CF(朱承飞); Chang HX(常红星) \| 收藏 \| 浏览/下载：36/0 \| 提交时间：2017/05/18 High Resolution Aerial Image Vehicle Detection Orientation Estimation
	A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation 期刊论文 OAI收割 COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2016, 卷号: 54, 期号: in press, 页码: 35-47 作者: Ye, Chuyang; Prince, Jerry L. \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2016/10/13 Dti Fiber Orientation Estimation Volumetric Tract Segmentation
	A Fast Orientation Estimation Approach of Natural Images 期刊论文 OAI收割 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 卷号: 46, 期号: 11, 页码: 1589-1597 作者: Cao, Zhiqiang; Liu, Xilong; Gu, Nong; Nahavandi, Saeid; Xu, De \| 收藏 \| 浏览/下载：35/0 \| 提交时间：2016/10/19 Biological Simple Cell Differential Field Natural Image Orientation Estimation
	Estimation of fiber orientations using neighborhood information 期刊论文 OAI收割 Medical Image Analysis, 2016, 期号: 32, 页码: 243-256 作者: Ye, Chuyang; Zhuo, Jiachen; Gullapalli, Rao; Prince, Jerry \| 收藏 \| 浏览/下载：34/0 \| 提交时间：2016/10/13 Diffusion Mri Fiber Orientation Estimation Neighborhood Information
	A Low-Cost Implementation of a 360 degrees Vision Distributed Aperture System 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2015, 卷号: 25, 期号: 2, 页码: 225-238 作者: Peng, Xiaoming; Bennamoun, Mohammed; Wang, Qingbo; Ma, Qian; Xu, Zhiyong 收藏 \| 浏览/下载：18/0 \| 提交时间：2015/07/10 Bundle adjustment camera orientation estimation distributed aperture systems (DASs) GPU programming image registration image stitching real-time virtual view synthesis
	Adaptive deformation estimation of moving target by weight image analysis (EI CONFERENCE) 会议论文 OAI收割 2010 2nd International Conference on Future Computer and Communication, ICFCC 2010, May 21, 2010 - May 24, 2010, Wuhan, China Bai X.-G.; Dai M. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 An algorithm based on weight image analysis is proposed for adaptive deformation estimation of moving target in mean-shift tracking method. At the first we get the weight image from the target candidate region. Then we analyze the differences between the object and background. According to that the area estimation of the target can be converted into the image segmentation task. To realize the adaptive segmentation and estimation we define the threshold as the maximum variance between object and background. Combining the estimated area and covariance matrix we can estimate the width height and orientation of the object. The experimental results on three representative video sequences validate its robustness to the deformable estimation of the targets. 2010 IEEE.
	Integrated intensity, orientation code and spatial information for robust tracking (EI CONFERENCE) 会议论文 OAI收割 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, May 23, 2007 - May 25, 2007, Harbin, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 real-time tracking is an important topic in computer vision. Conventional single cue algorithms typically fail outside limited tracking conditions. Integration of multimodal visual cues with complementary failure modes allows tracking to continue despite losing individual cues. In this paper we combine intensity orientation codes and special information to form a new intensity-orientation codes-special (IOS) feature to represent the target. The intensity feature is not affected by the shape variance of object and has good stability. Orientation codes matching is robust for searching object in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc The spatial locations of the pixels are used which allow us to take into account the spatial information which is lost in traditional histogram. Histograms of intensity orientation codes and spatial information are employed for represent the target Mean shift algorithm is a nonparametric density estimation method. The fast and optimal mode matching can be achieved by this method. In order to reduce the compute time we use the mean shift procedure to reach the target localization. Experiment results show that the new method can successfully cope with clutter partial occlusions illumination change and target variations such as scale and rotation. The computational complexity is very low. If the size of the target is 3628 pixels it only needs 12ms to complete the method. 2007 IEEE.