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	Dynamical stochastic resonance for nonuniform illumination image enhancement 期刊论文 OAI收割 IET Image Processing, 2018, 卷号: 12, 期号: 12, 页码: 2147-2152 作者: Zhang, Yongbin; Liu, Hongjun; Huang, Nan; Wang, Zhaolu \| 收藏 \| 浏览/下载：52/0 \| 提交时间：2018/12/18 Image Enhancement Image Fusion Stochastic Processes Iterative Methods Differential Equations Brightness Dynamical Stochastic Resonance Nonuniform Illumination Image Enhancement Dark Tones Low-contrast Image Enhancement Nonlinear Iteration Monostable Langevin Equation Iteration Parameters Intensity Distribution Visibility Balance Naturalness Balance Illumination Compensation Component Computational Time No-reference Perceptual Quality Assessment Lightness Order Error Low-computational Complexity
	Affine object recognition and affine parameters estimation based on covariant matrix (EI CONFERENCE) 会议论文 OAI收割 2008 International Symposium on Information Science and Engineering, ISISE 2008, December 20, 2008 - December 22, 2008, Shanghai, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：133/0 \| 提交时间：2013/03/25 A new method of affine object recognition and affine parameters estimation is presented. For a real-time image and a group of templates in addition firstly on the basis of correct recognition we segment the object regions in them and compute their covariant matrices. Secondly it can estimate affine parameters exactly normalize the ellipse regions defined by covariant matrices to circle regions to get rotational invariants and the estimated error is within 3%. 2008 IEEE. and compute the similarity function value between rotational invariants of real-time image and every template respectively. Then compare the values with threshold set in advance if more than one value is larger than threshold take the corresponding templates as candidates and compute affine matrix between real-time image and every candidate. Finally transform the realtime image with every affine matrix and match the result with corresponding candidate by classical matching methods. Experimental results show that the presented method is robust to illumination with low computational complexity and it can realize recognition of different affine objects
	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.