期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 45, 期 4, 页码 4694-4712出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2022.3201185
关键词
Interest point detection; Feature extraction; Image edge detection; Data mining; Taxonomy; Sun; Detectors; image feature information extraction; taxonomy; performance evaluation; development trend on image feature information extraction
In this paper, a comprehensive review is conducted on the application of image feature information extraction techniques in interest point detection. A taxonomy is proposed to systematically introduce the existing methods and different types of image feature information extraction techniques are discussed. The unresolved issues and undiscussed methods are identified, popular datasets and evaluation standards are provided, and the performances of fifteen state-of-the-art approaches are evaluated and discussed. Future research directions on image feature information extraction techniques for interest point detection are also elaborated.
Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for fifteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.
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