An improved multi-scale autoconvolution transform
文献类型：会议论文
| 作者 | Shao CY(邵春艳) ; Ding QH(丁庆海); Luo HB(罗海波)
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| 出版日期 | 2014 |
| 会议日期 | May 13-15, 2014 |
| 会议地点 | Beijing, China |
| 关键词 | Affine Invariant Feature Multi-scale Autoconvolution N-domain Vectors Included Angle Map N-domain Vectors Angle Map |
| 页码 | 1-7 |
| 英文摘要 | 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 |
| 会议录 | Proc. Of SPIE 9301, International Symposium on Optoelectronic Technology and Application
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| 会议录出版者 | SPIE |
| 会议录出版地 | Bellingham, WA |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| WOS记录号 | WOS:000349327100071 |
| 源URL | [http://ir.sia.cn/handle/173321/15335]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 作者单位 | 1.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang, China 2.Research Institute of General Development and Demonstration of Equipment, Equipment of Air Force, Bejing, China 3.Key Lab of Image Understanding and Computer Vision, Liaoning province, Shenyang, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 5.University of Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Shao CY,Ding QH,Luo HB. An improved multi-scale autoconvolution transform[C]. 见:. Beijing, China. May 13-15, 2014. |
入库方式： OAI收割
来源：沈阳自动化研究所
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