What is it about?

Corners, a highly important local features of images and corner finding, play important a crucial role in computer vision and image processing, such as object tracking and vehicle detection. We present a new measure of corner sharpness termed as Point-to-Centroid Distance (PCD), and then examine its behaviors, which display beneficial characteristics that help distinguish corners from non-corners. Based on PCD behaviors, we propose a novel corner detector. Extensive experimental results demonstrate that the PCD technique is effective and simultaneously efficient for corner detection

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Why is it important?

We propose a novel corner detector based on the Point-to-Centroid Distance (PCD) technique to enhance the performance of contour-based corner detectors.We exploit the characteristics of the PCD of contour to develop the corner response function. Since PCD utilizes a large neighborhood without using any derivatives, it is less sensitive to local variations and noise of the curve, which are weaknesses faced by CSS-based corner detectors. Furthermore, we adopt relative distance instead of absolute distance to make PCD more robust to the uniform scaling transform.


Writing this article was a great pleasure as it has co-authors with whom I have had long standing collaborations. This article also lead to feature detection groups contacting me and ultimately to a greater involvement in corner detection research.

Corner detection using the point-to-centroid distance technique Shizheng Zhang
Zhengzhou University of Light Industry

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This page is a summary of: Corner detection using the point-to-centroid distance technique, IET Image Processing, August 2020, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-ipr.2020.0164.
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