Keypoint Detection Using Normalized Higher-Order Scale Space Derivatives 


Vol. 42,  No. 1, pp. 93-96, Jan.  2015


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  Abstract

The SIFT method is well-known for robustness against various image transformations, and is widely used for image retrieval and matching. The SIFT method extracts keypoints using scale space analysis, which is different from conventional keypoint detection methods that depend only on the image space. The SIFT method has also been extended to use higher-order scale space derivatives for increasing the number of keypoints detected. Such detection of additional keypoints detected was shown to provide performance gain in image retrieval experiments. Herein, a sigma based normalization method for keypoint detection is introduced using higher-order scale space derivatives.


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  Cite this article

[IEEE Style]

J. Park and U. Park, "Keypoint Detection Using Normalized Higher-Order Scale Space Derivatives," Journal of KIISE, JOK, vol. 42, no. 1, pp. 93-96, 2015. DOI: .


[ACM Style]

Jongseung Park and Unsang Park. 2015. Keypoint Detection Using Normalized Higher-Order Scale Space Derivatives. Journal of KIISE, JOK, 42, 1, (2015), 93-96. DOI: .


[KCI Style]

박종승, 박운상, "스케일 공간 고차 미분의 정규화를 통한 특징점 검출 기법," 한국정보과학회 논문지, 제42권, 제1호, 93~96쪽, 2015. DOI: .


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