Abstract
Corner detection is important in image analysis and understanding, but most existing corner detectors are sensitive to image quality, lens radial distortion, and illumination. In this paper, we propose a corner detector for robust corner detection in continuous space. We use the open string theory to construct the continuous representation of an image. Defining a corner as the intersection of two or more curve edges or straight line edges, we design a corner response function for corner determination. In detail, for each integer point, we construct multiple grayscale-parallelograms by any two directed line segments of that point, and the corner response function is based on these grayscale-parallelograms. Finally, a point with a high response value is detected as a corner. Experimental results on conventional images, wide-angle images, and fisheye images show that the proposed method obtains state-of-the-art performance on conventional images and achieves superior performance on wide-angle images and fisheye images, even under weak lighting and low-quality conditions.
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Acknowledgements
This work was supported by the Doctoral Fund of Guangxi University of Science and Technology (No. 20Z39) and the Project for Enhancing Young and Middle-aged Teacher’s Research Basis Ability in Colleges of Guangxi (No. 2024KY0358).
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Wei, X., Dong, Y., Liu, Q. et al. Robust corner detection in continuous space. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03362-x
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DOI: https://doi.org/10.1007/s00371-024-03362-x