机构

• 长春光学精密机械与物... [1]
• 自动化研究所 [1]
• 沈阳自动化研究所 [1]
• 软件研究所 [1]

采集方式

• OAI收割 [4]

内容类型

• 会议论文 [2]
• 学位论文 [1]
• 期刊论文 [1]

发表日期

• 2014 [1]
• 2010 [1]
• 2009 [1]
• 2008 [1]

学科主题

• Computer S... [1]

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 An improved multi-scale autoconvolution transform 会议论文  OAI收割 Beijing, China, May 13-15, 2014 作者:  Shao CY(邵春艳);  Ding QH(丁庆海);  Luo HB(罗海波)   |  收藏  |  浏览/下载：18/0  |  提交时间：2014/12/29 Affine Invariant Feature  Multi-scale Autoconvolution  N-domain Vectors Included Angle Map  N-domain Vectors  Angle Map   基于参数化求和不变量与特征重整的形状匹配 期刊论文  OAI收割 中国图象图形学报, 2010, 卷号: 15, 期号: 1, 页码: 122-128 吕玉增; 彭启民; 黎湘   |  收藏  |  浏览/下载：28/0  |  提交时间：2011/05/23 形状匹配  仿射不变量  特征重整  动态规划shape matching  affine invariant  feature regulating  dynamic programming   图像特征检测及应用 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:  戴志军 收藏  |  浏览/下载：69/0  |  提交时间：2015/09/02 特征检测  仿射不变  边缘检测  聚焦区域  角点检测  图像质量评价  特征匹配  feature detection  affine invariant  edge detection  focused region  corner detection  image quality assessment  feature matching   Mean shift tracking combining SIFT (EI CONFERENCE) 会议论文  OAI收割 2008 9th International Conference on Signal Processing, ICSP 2008, October 26, 2008 - October 29, 2008, Beijing, China 作者:  Xue C. 收藏  |  浏览/下载：67/0  |  提交时间：2013/03/25 A novel visual tracking algorithm to cope with occlusion and scale variation is proposed. This method combines mean shift and SIFT algorithm to track object. SIFT algorithm is invariant to rotation  translation and scale variation. But it is a timeconsuming algorithm. The wasting time is related to image size. So the proposed algorithm first adopts mean shift to initially locate object position  then SIFT operator is used to detect features in object area and model area  lastly  the proposed method matches features in these two areas and calculates the relationship between them using affine transform. According to affine transform parameters  the state of object can be adjusted in time. In order to reduce process time  an improved feature matching algorithm is proposed in this paper. Experiments show that the proposed algorithm deals with occlusion successfully and can adjust object size in time. 2008 IEEE.