机构

• 自动化研究所 [2]
• 长春光学精密机械与物... [1]
• 西安光学精密机械研究... [1]
• 软件研究所 [1]

采集方式

• OAI收割 [5]

内容类型

• 期刊论文 [3]
• 会议论文 [2]

发表日期

• 2016 [3]
• 2015 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共5条，第1-5条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Fine-structured object segmentation via neighborhood propagation 期刊论文  OAI收割 PATTERN RECOGNITION, 2016, 卷号: 60, 期号: null, 页码: 130-144 作者:  Gong, Yongchao;  Xiang, Shiming;  Pan, Chunhong   |  收藏  |  浏览/下载：26/0  |  提交时间：2016/12/26 Fine-structured Object Segmentation  Label Propagation  Affinity Graph  Local And nonLocal Neighborhood  Region Cost   A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation 期刊论文  OAI收割 spectroscopy and spectral analysis, 2016, 卷号: 36, 期号: 9, 页码: 2919-2924 作者:  Wang Cai-ling;  Wang Hong-wei;  Hu Bing-liang;  Wen Jia;  Xu Jun 收藏  |  浏览/下载：37/0  |  提交时间：2016/10/10 Hyperspectral image processing  Sparse representation  Neighborhood clustering  Neighborhood segmentation  Minimum reconstruction error   A dynamic niching clustering algorithm based on individual-connectedness and its application to color image segmentation 期刊论文  OAI收割 PATTERN RECOGNITION, 2016, 卷号: 60, 页码: 334-347 Chang, DX; Zhao, Y; Liu, L; Zheng, CW   |  收藏  |  浏览/下载：32/0  |  提交时间：2016/12/09 Clustering  Genetic algorithms  Niching  Connected individual  k-distance neighborhood  Image segmentation   FINE-STRUCTURED OBJECT SEGMENTATION VIA LOCAL AND NONLOCAL NEIGHBORHOOD PROPAGATION 会议论文  OAI收割 Québec City, Canada, 2015-9-27 ~ 2015-9-30 作者:  Gong, Yongchao;  Xiang, Shiming;  Wang, Lingfeng;  Pan, Chunhong   |  收藏  |  浏览/下载：18/0  |  提交时间：2017/05/09 Fine-structured (Fs) Object Segmentation  Affinity Graph  Local And nonLocal Neighborhood System  Region Cost  Closed-form Solution   Research on tracking approach to low-flying weak small target near the sea (EI CONFERENCE) 会议论文  OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者:  Xue X.-C. 收藏  |  浏览/下载：33/0  |  提交时间：2013/03/25 Automatic target detection is very difficult in complicate background of sea and sky because of the clutter caused by waves and clouds nearby the sea-level line. In this paper  in view of the low-flying target near the sea is always above the sea-level line  we can first locate the sea-level line  and neglect the image data beneath the sea-level line. Thus the noise under the sea-level line can be suppressed  and the executive time of target segmentation is also much reduced. A new method is proposed  which first uses neighborhood averaging method to suppress background and enhance targets so as to increase SNR  and then uses the multi-point multi-layer vertical Sobel operator combined with linear least squares fitting to locate the sea-level line  lastly uses the centroid tracking algorithm to detect and track the target. In the experiment  high frame rate and high-resolution digital CCD camera and high performance DSP are applied. Experimental results show that this method can efficiently locate the sea-level line on various conditions of lower contrast  and eliminate the negative impact of the clutter caused by waves and clouds  and capture and track target real-timely and accurately.