A low-complexity deformation invariant descriptor

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, we propose a descriptor which is invariant to general deformations (only intensity locations change but not their value) by using Hilbert scanning. In our method, an image is converted to a 1-D sequence through Hilbert scanning at first. Then, we embed this sequence as a 1-D curve in the 2-D space. Because Hilbert scanning preserves the coherence in a 2-D image, it is easily to understand that the area under the curve is invariant to intensity location changes, naturally. Hence, we use some areas for an interest point as a deformation invariant descriptor. This descriptor can be computed in the 2-D space efficiently than other approaches where an image is embedded in the 3-D space or the dimensions of descriptors are very large. The experimental results show that our descriptor is low-complexity and superior to other approaches on interest point matching in deformation images.

本文言語English
ホスト出版物のタイトルProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
ページ227-230
ページ数4
DOI
出版ステータスPublished - 2006 12 1
イベント18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
継続期間: 2006 8 202006 8 24

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
2
ISSN(印刷版)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period06/8/2006/8/24

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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