Efficient keypoint detection and description via polynomial regression of scale space

Ryo Okutani, Kenjiro Sugimoto, Seiichiro Kamata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Keypoint detection and description using approximate continuous scale space are more efficient techniques than typical discretized scale space for achieving more robust feature matching. However, this state-of-the-art method requires high computational complexity to approximately reconstruct, or decompress, the value at an arbitrary point in scale space. Specifically, it has O(M2) computational complexity where M is an approximation order. This paper presents an efficient scale space approach that provides decompression operation with O(M) complexity without a loss of accuracy. As a result of the fact that the proposed method has much fewer variables to be solved, the least-square solution can be obtained through normal equation. This is easier to solve than the existing method which employs Karhunen-Loeve expansion and generalized eigenvalue problem. Experiments revealed that the proposed method performs as expected from the theoretical analysis.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1357-1361
ページ数5
2016-May
ISBN(電子版)9781479999880
DOI
出版ステータスPublished - 2016 5 18
イベント41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
継続期間: 2016 3 202016 3 25

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
国/地域China
CityShanghai
Period16/3/2016/3/25

ASJC Scopus subject areas

  • 信号処理
  • ソフトウェア
  • 電子工学および電気工学

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