中国科学院机构知识库网格系统: 检索

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	Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360 degrees Omnidirectional Image 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 13 作者: Cong, Runmin; Huang, Ke; Lei, Jianjun; Zhao, Yao; Huang, Qingming \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2023/07/12 360? omnidirectional image cube-unfolding (CU) dynamic weighting filtration and refinement (FR) salient object detection (SOD)
	IRDCLNet: Instance Segmentation of Ship Images Based on Interference Reduction and Dynamic Contour Learning in Foggy Scenes 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 9, 页码: 6029-6043 作者: Sun, Yuxin; Su, Li; Luo, Yongkang; Meng, Hao; Zhang, Zhi \| 收藏 \| 浏览/下载：51/0 \| 提交时间：2022/11/14 Marine vehicles Image segmentation Meteorology Feature extraction Interference Object detection Visualization Foggy scene ship instance segmentation interference reduction module dynamic contour learning
	Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos 会议论文 OAI收割 Honolulu, Hawaii, 20170721-20170726 作者: Du, Yang; Yuan, Chunfeng; Li, Bing; Hu, Weiming; Maybank, Stephen \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2018/01/04 Dynamic Object Detection Self-organizing Map Deep Network
	基于视觉的动态场景下目标跟踪方法研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2012 作者: 尹春霞收藏 \| 浏览/下载：42/0 \| 提交时间：2015/09/02 目标跟踪动态场景 Mean-shift 粒子滤波特征检测 object tracking dynamic scenes Mean-shift particle feature detection
	Spatio-temporal context for codebook-based dynamic background subtraction 期刊论文 OAI收割 AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2010, 卷号: 64, 期号: 8, 页码: 739-747 作者: Wu, Mingjun; Peng, Xianrong 收藏 \| 浏览/下载：16/0 \| 提交时间：2015/09/21 Dynamic background subtraction Object detection Codebook Spatio-temporal context Markov random field
	复杂场景下的运动目标检测学位论文 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, 2009 作者: 刘舟收藏 \| 浏览/下载：69/0 \| 提交时间：2015/09/02 运动目标检测背景建模阴影去除动态背景遗弃物检测 moving object detection background modeling shadow removal dynamic background left-luggage detection
	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：30/0 \| 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