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스케일 공간 고차 미분의 정규화를 통한 특징점 검출 기법
박종승(Jongseung Park),박운상(Unsang Park) Korean Institute of Information Scientists and Eng 2015 정보과학회논문지 Vol.42 No.1
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.
박종승(Jongseung Park),박운상(Unsang Park) Korean Institute of Information Scientists and Eng 2015 정보과학회논문지 Vol.42 No.6
Requirement of effective image handling methods such as image retrieval has been increasing with the rising production and consumption of multimedia data. In this paper, a method of constructing more effective descriptor is proposed for robust keypoint based image retrieval. The proposed method uses information embedded in the first order and second order derivative images, in addition to the scale space image, for the descriptor construction. The performance of multi-image descriptor is evaluated in terms of the similarities in keypoints with a public domain image database that contains various image transformations. The proposed descriptor shows significant improvement in keypoint matching with minor increase of the length.