An Efficient Single Chord-based Accumulation Technique (SCA) to Detect More Reliable Corners
This work addresses corner detection for computer vision applications, presenting an incremental improvement over the CPDA method.
The paper tackles the problem of corner detection in computer vision by proposing a Single Chord-based Accumulation (SCA) technique that reduces localization error and increases average repeatability compared to the CPDA detector, which often misses corners and is computationally costly.
Corner detection is a vital operation in numerous computer vision applications. The Chord-to-Point Distance Accumulation (CPDA) detector is recognized as the contour-based corner detector producing the lowest localization error while localizing corners in an image. However, in our experiment part, we demonstrate that CPDA detector often misses some potential corners. Moreover, the detection algorithm of CPDA is computationally costly. In this paper, We focus on reducing localization error as well as increasing average repeatability. The preprocessing and refinements steps of proposed process are similar to CPDA. Our experimental results will show the effectiveness and robustness of proposed process over CPDA.