Journal
CONNECTION SCIENCE
Volume 32, Issue 3, Pages 223-238Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/09540091.2019.1674246
Keywords
Feature detection method; point-symmetric structural features; point pair algorithm; local extrema; hierarchical clustering
Funding
- Chunhui Project Foundation of the Education Department of China [Z2014050]
- Research Fund of Sichuan Science and Technology Project [2018JY0083]
- Key Projects of the Sichuan Provincial Education Department of China [17ZA0360]
- Research Foundation of the Education Department of Sichuan province [17TD0034]
Ask authors/readers for more resources
Conventional feature detection algorithms are largely based on clustered two-dimensional (2D) blocks of information. However, corners located at the centre of gradually greying blocks of information cannot be extracted using these algorithms. The edge feature points described by the algorithms are often affected by background changes, leading to significant differences in the descriptors for the same feature. These issues are detrimental to subsequent matching processes. Therefore, we propose a new feature detection method that will provide more useful corner information for subsequent tracking and detection processes, particularly for edge features. The edge information of corners is used to search for points that satisfy the requirements for inner greyscale consistency. The points are then used to construct point-symmetric structures. The zeroth-order inner greyscale data, first-order gradient orientation differences, and angular directions of the point-symmetric connections of the structures are considered structural attributes, which help search for feature points. Similar feature points are then clustered using a hierarchical clustering algorithm, followed by extracting the feature points from point pair features of the same type. It was experimentally demonstrated that the proposed point-symmetric structural features would help increase the number of valid feature points that can be extracted from an image.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available