Efficient keypoint detection and description using filter kernel decomposition in scale space

Ryo Okutani, Kenjiro Sugimoto, Sei Ichiro Kamata

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Keypoint detection and description in a continuous scale space achieves better robustness to scale change than those in a discretized scale space. State-of-the-art methods first decompose a continuous scale space into M + 1 component images weighted by M-order polynomials of scale σ, and then reconstruct the value at an arbitrary point in the scale space by a linear combination of the component images. However, these methods create the mismatch that, if σ is large, common filter kernels such as Gaussian converge to zero; but the polynomials of σ diverge. This paper presents a more efficient approximation that suppresses this mismatch by normalizing the weighting functions. Experiments show that the proposed method achieves higher performance tradeoff than state-of-the-art methods: it reduces the number of component images and total running time by 20-40% without a sacrifice of stability in keypoints detection.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
出版社IEEE Computer Society
ページ31-35
ページ数5
ISBN(電子版)9781467399616
DOI
出版ステータスPublished - 2016 8 3
イベント23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
継続期間: 2016 9 252016 9 28

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
2016-August
ISSN(印刷版)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
国/地域United States
CityPhoenix
Period16/9/2516/9/28

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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