中国科学院沈阳自动化研究所机构知识库
Advanced  
SIA OpenIR  > 光电信息技术研究室  > 会议论文
题名: An improved multi-scale autoconvolution transform
作者: Shao CY(邵春燕) ; Ding QH(丁庆海) ; Luo HB(罗海波)
作者部门: 光电信息技术研究室
会议名称: International Symposium on Optoelectronic Technology and Application 2014
会议日期: May 13-15, 2014
会议地点: Beijing, China
会议录: Proc. Of SPIE 9301, International Symposium on Optoelectronic Technology and Application
会议录出版者: SPIE
会议录出版地: Bellingham, WA
出版日期: 2014
页码: 1-7
收录类别: CPCI(ISTP) ; EI
ISSN号: 0277-786X
关键词: affine invariant feature ; Multi-Scale Autoconvolution ; N-domain vectors included angle map ; N-domain vectors ; Angle map
摘要: Affine invariant feature computing method is an important part of statistical pattern recognition due to the robustness, repeatability, distinguishability and wildly applicability of affine invariant feature. Multi-Scale Autoconvolution (MSA) is a transformation proposed by Esa Rathu which can get complete affine invariant feature. Rathu proved that the linear relationship of any four non-colinear points is affine invariant. The transform is based on a probabilistic interpretation of the image function. The performance of MSA transform is better on image occlusion and noise, but it is sensitive to illumination variation. Aim at this problem, an improved MSA transform is proposed in this paper by computing the map of included angle between N-domain vectors. The proposed method is based on the probabilistic interpretation of N-domain vectors included angle map. N-domain vectors included angle map is built through computing the vectors included angle where the vectors are composed of the image point and its N-domain image points. This is due to that the linear relationship of included angles between vectors composed of any four non-colinear points is an affine invariance. This paper proves the method can be derived in mathematical aspect. The transform values can be used as descriptors for affine invariant pattern classification. The main contribution of this paper is applying the N-domain vectors included angle map while taking the N-domain vector included angle as the probability of the pixel. This computing method adapts the illumination variation better than taking the gray value of the pixel as the probability. We illustrate the performance of improved MSA transform in various object classification tasks. As shown by a comparison with the original MSA transform based descriptors and affine invariant moments, the proposed method appears to be better to cope with illumination variation, image occlusion and image noise.
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/15335
Appears in Collections:光电信息技术研究室_会议论文

Files in This Item: Download All
File Name/ File Size Content Type Version Access License
An improved Multi-Scale Autoconvolution transform.pdf(688KB)----开放获取View Download
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Shao CY(邵春燕)]'s Articles
[Ding QH(丁庆海)]'s Articles
[Luo HB(罗海波)]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Shao CY(邵春燕)]‘s Articles
[Ding QH(丁庆海)]‘s Articles
[Luo HB(罗海波)]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
文件名: An improved Multi-Scale Autoconvolution transform.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2016  中国科学院沈阳自动化研究所 - Feedback
Powered by CSpace