Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
On the convergence of planar curves under smoothing
IEEE Transactions on Image Processing
Curvature product corner detection in direct curvature scale space
International Journal of Computational Vision and Robotics
Multiscale Corner Detection in Planar Shapes
Journal of Mathematical Imaging and Vision
Accurate junction detection and characterization in line-drawing images
Pattern Recognition
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
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There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.