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	A Robust Deep Affinity Network for Multiple Ship Tracking 期刊论文 OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 卷号: 70, 页码: 20 作者: Zhang, Wen; He, Xujie; Li, Wanyi; Zhang, Zhi; Luo, Yongkang \| 收藏 \| 浏览/下载：30/0 \| 提交时间：2021/11/02 Complex marine scenes joint global region modeling (JGRM) module marine surveillance motion-matching optimization (MMO) module multiple ship tracking (MST)
	The ParallelEye Dataset: A Large Collection of Virtual Images for Traffic Vision Research 期刊论文 OAI收割 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 6, 页码: 2072-2084 作者: Li, Xuan; Wang, Kunfeng; Tian, Yonglin; Yan, Lan; Deng, Fang \| 收藏 \| 浏览/下载：61/0 \| 提交时间：2019/07/11 Traffic vision complex environments parallel vision artificial scenes ParallelEye virtual dataset
	Video Synopsis in Complex Situations 期刊论文 OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 卷号: 27, 期号: 8, 页码: 3798-3812 作者: Li, Xuelong; Wang, Zhigang; Lu, Xiaoqiang; Lu, XQ (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, Xian 710119, Shaanxi, Peoples R China. \| 收藏 \| 浏览/下载：43/0 \| 提交时间：2018/05/14 Video Synopsis Surveillance Video Complex Scenes Crowded Scenes
	Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives 期刊论文 OAI收割 ARTIFICIAL INTELLIGENCE REVIEW, 2017, 卷号: 48, 期号: 3, 页码: 299-329 作者: Wang, Kunfeng; Gou, Chao; Zheng, Nanning; Rehg, James M.; Wang, Fei-Yue \| 收藏 \| 浏览/下载：41/0 \| 提交时间：2018/01/05 Visual Perception Complex Scenes Parallel Vision Acp Methodology Computer Graphics Image Synthesis
	复杂场景中行为识别的关键技术及方法研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院大学, 2014 作者: 周文收藏 \| 浏览/下载：99/0 \| 提交时间：2015/09/02 复杂场景视角局部特征多示例学习马尔科夫模型行为识别姿态估计 complex scenes viewpoints local features multiple-instance learning Markov model action recognition pose estimation
	Contrast-dependent OFF-dominance in cat primary visual cortex facilitates discrimination of stimuli with natural contrast statistics 期刊论文 OAI收割 EUROPEAN JOURNAL OF NEUROSCIENCE, 2014, 卷号: 39, 期号: 12, 页码: 2060-2070 Liu, KF; Yao, HS 收藏 \| 浏览/下载：32/0 \| 提交时间：2014/07/30 RETINAL GANGLION-CELLS RECEPTIVE-FIELD SIZE MACAQUE V1 NEURONS STRIATE CORTEX RETINOTOPIC ORGANIZATION SPATIAL-FREQUENCY LOCAL CONTRAST COMPLEX CELLS SCENES IMAGES
	复杂场景多目标跟踪中的遮挡算法与应用研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2012 作者: 丁欢收藏 \| 浏览/下载：115/0 \| 提交时间：2015/09/02 复杂场景多目标跟踪遮挡分割粒子滤波嵌入式 complex scenes multi-target tracking occlusion segmentation particle filter embedded system
	Scene matching based on directional keylines and polar transform (EI CONFERENCE) 会议论文 OAI收割 2010 IEEE 10th International Conference on Signal Processing, ICSP2010, October 24, 2010 - October 28, 2010, Beijing, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 Scene matching under complex background is a priority and difficulty in the field of computer vision it has the characteristics of rotation and scaling invariance commonly used in matching real-time collected images and photos for navigation. Scene matching techniques are faced with complex natural scenes anti-light and anti-slight-distortion the image distortion exist applicable for complex scene matching. The project has a new idea: combining the keylines with the vectors description based on polar image translation such as light and utilize the rotation-scale-invariance vectors to describe the extracted keylines change of gray levels this method includes three steps: keylines extraction perspective description and matching. Preliminary experiments show that this keylines-based scene matching algorithm is applicable for image matching under complex background. 2010 IEEE. scaling and other differences which cause matching difficult. This paper aims to find a scene matching algorithm
	A segment detection method based on improved Hough transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Yao Z.-J. 收藏 \| 浏览/下载：25/0 \| 提交时间：2013/03/25 Hough transform is recognized as a powerful tool in shape analysis which gives good results even in the presence of noise and the disconnection of edge. However 3. applying the standard Hough transform equation to every point of the input image edge 4. according to the local threshold 6. merging the segments whose extreme points are near. Experiment results show the approach not only can recognize regular geometric object but also can extract the segment feature of real targets in complex environment. So the proposed method can be used in the target detection of complicated scenes traditional Hough transform can only detect the lines 2. quantizing the parameter space and extracting a group of maximums according to the global threshold eliminating spurious peaks which are caused by the spreading effects and will improve the precision of tracking. cannot give the endpoints and length of the line segments and it is vulnerable to the quantization errors. Based on the analysis of its limitations Hough transform has been improved in order to detect line segment feature of targets. The algorithm aims to avoid the loss of spatial information as well as to eliminate the spurious peaks and fix on the line segments endpoints accurately 5. fixing on the endpoints of the segments according to the dynamic clustering rule which can expediently be used for the description and classification of regular objects. The method consists of 6 steps: 1. setting up the image parameter and line-segment spaces