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Discrete Curvature Representations for Noise Robust Image Corner Detection

Zhang, W.; Sun, C.; Breckon, T.P.; Alshammari, N.

Discrete Curvature Representations for Noise Robust Image Corner Detection Thumbnail


W. Zhang

C. Sun

N. Alshammari


Image corner detection is very important in the fields of image analysis and computer vision. Curvature calculation techniques are used in many contour-based corner detectors. We identify that existing calculation of curvature is sensitive to local variation and noise in the discrete domain and does not perform well when corners are closely located. In this paper, discrete curvature representations of single and double corner models are investigated and obtained. A number of model properties have been discovered which help us detect corners on contours. It is shown that the proposed method has a high corner resolution (the ability to accurately detect neighbouring corners) and a corresponding corner resolution constant is also derived. Meanwhile, this method is less sensitive to any local variations and noise on the contour; and false corner detection is less likely to occur. The proposed detector is compared with seven state-of-the-art detectors. Three test images with ground truths are used to assess the detection capability and localization accuracy of these methods in noise-free and cases with different noise levels. Twenty-four images with various scenes without ground truths are used to evaluate their repeatability under affine transformation, JPEG compression, and noise degradations. The experimental results show that our proposed detector attains a better overall performance.


Zhang, W., Sun, C., Breckon, T., & Alshammari, N. (2019). Discrete Curvature Representations for Noise Robust Image Corner Detection. IEEE Transactions on Image Processing, 28(9), 4444-4459.

Journal Article Type Article
Acceptance Date Apr 8, 2019
Online Publication Date Apr 17, 2019
Publication Date Sep 30, 2019
Deposit Date Apr 24, 2019
Publicly Available Date Apr 24, 2019
Journal IEEE Transactions on Image Processing
Print ISSN 1057-7149
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 28
Issue 9
Pages 4444-4459
Publisher URL


Accepted Journal Article (8.9 Mb)

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