Open AccessInterest point detection method based on multi‐scale Gabor filters
Author(s) -
Zhao Zhuo,
Li Bing,
Chen Lei,
Xin Meiting,
Gao Fei,
Zhao Qiang
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5824
Subject(s) - artificial intelligence , region of interest , edge detection , robustness (evolution) , pixel , computer vision , canny edge detector , point of interest , computer science , detector , mathematics , pattern recognition (psychology) , point (geometry) , interest point detection , image (mathematics) , image processing , geometry , telecommunications , biochemistry , chemistry , gene
In this study, a novel interest point detection algorithm which combines image intensity variation and edge contour information is proposed. Firstly, the Canny edge contour detector is used to extract the edge map. Secondly, the imaginary parts of multi‐scale Gabor filters are applied to smooth input image and then the normalised information entropies at various scales can be acquired. Finally, multiplication of different normalised information entropies will be served as a new measure for interest point that can also be used for interest point detection. This method has two advantages: on the one hand, detection accuracy is greatly improved because combination information is adopted to extract interest points including contour shape information, grey variation of edge pixels and their neighbours; on the other hand, non‐interest points can be well inhabited due to multi‐scale product in detector is served as an interest point measure. Also, desirable noise robustness and time efficiency are validated through experiments. Compared with four other state‐of‐art methods, the proposed method shows excellent performance in terms of geometric transformations and localisation accuracy of repeated interest points.