An Improved Curvature Scale-Space Corner Detector and a Robust Corner Matching Approach for Transformed Image Identification

Mohammad Awrangjeb, Guojun Lu
2008 IEEE Transactions on Image Processing  
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
more » ... rner 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. Index Terms-corner detection, corner matching, curvature scale-space, affine-length, transformed image identification. EDICS: OTH-MAPP Multimedia applications, SRE-OTHR Other, and OTH-OTHR Other. Guojun Lu is currently a Professor at Gippsland School of Information Technology, Monash University. He has held positions at
doi:10.1109/tip.2008.2006441 fatcat:opyi4nlctrdi3knz7gzhe3vm4a