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Discrete curvature representations for noise robust image corner detection.

Zhang, W. and Sun, C. and Breckon, T.P. and Alshammari, N. (2019) 'Discrete curvature representations for noise robust image corner detection.', IEEE Transactions on Image Processing, 28 (9). pp. 4444-4459.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:08 April 2019
Date deposited:24 April 2019
Date of first online publication:17 April 2019
Date first made open access:24 April 2019

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