Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
Robust Harris-Laplace Detector by Scale Multiplication
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Corner detection based on gradient correlation matrices of planar curves
Pattern Recognition
Chinese calligraphy specific style rendering system
Proceedings of the 10th annual joint conference on Digital libraries
An effective method of estimating scale-invariant interest region for representing corner features
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the ldquocurvaturerdquo itself by its ldquodefinitionrdquo is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.