Robust detection of skewed symmetries

Dinggang Shen, Horace H S Ip, Eam Khwang Teoh

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)

Abstract

Affine-invariant feature vector, which captures local and semi-local features, was used in the detection of skewed symmetries. Here, the problem of symmetry axes detection has been formulated as a line detection problem, with known orientations within a local similarity matrix computed for a shape. Moreover, our technique allows all the local reflection-symmetries within an object to be detected. Experiments on detecting skewed symmetries of self-symmetric objects and generalized objects, under noises and occlusions, have demonstrated the effectiveness of this method.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages1010-1013
Number of pages4
Volume15
Edition3
Publication statusPublished - 2000
Externally publishedYes

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Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

Cite this

Shen, D., Ip, H. H. S., & Teoh, E. K. (2000). Robust detection of skewed symmetries. In Proceedings - International Conference on Pattern Recognition (3 ed., Vol. 15, pp. 1010-1013)

Robust detection of skewed symmetries. / Shen, Dinggang; Ip, Horace H S; Teoh, Eam Khwang.

Proceedings - International Conference on Pattern Recognition. Vol. 15 3. ed. 2000. p. 1010-1013.

Research output: Chapter in Book/Report/Conference proceedingChapter

Shen, D, Ip, HHS & Teoh, EK 2000, Robust detection of skewed symmetries. in Proceedings - International Conference on Pattern Recognition. 3 edn, vol. 15, pp. 1010-1013.
Shen D, Ip HHS, Teoh EK. Robust detection of skewed symmetries. In Proceedings - International Conference on Pattern Recognition. 3 ed. Vol. 15. 2000. p. 1010-1013
Shen, Dinggang ; Ip, Horace H S ; Teoh, Eam Khwang. / Robust detection of skewed symmetries. Proceedings - International Conference on Pattern Recognition. Vol. 15 3. ed. 2000. pp. 1010-1013
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