Robust Image Corner Detection Through Curvature Scale Space
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Abstract—This paper describes a novel method for image corner detection based on the curvature scale-space (CSS) representation. The first step is to extract edges from the original image using a Canny detector. The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS and tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors. An improvement to Canny edge detector's response to 45° and 135° edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zero-crossings of image edge contours) in addition to the traditional corners.
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Index Terms:
Low-level processing, feature extraction, corner detection, multiscale analysis, curvature scale space, Canny edge-detector.
Citation:
Farzin Mokhtarian, Riku Suomela, "Robust Image Corner Detection Through Curvature Scale Space," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1376-1381, Dec. 1998, doi:10.1109/34.735812
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