Computational geometry: an introduction
Computational geometry: an introduction
Maximum-Likelihood Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Translation, scaling and rotation invariant spot matching using delaunay triangulation
ACS'08 Proceedings of the 8th conference on Applied computer scince
Gel image matching based on local homography constraints
CIMMACS'09 Proceedings of the 8th WSEAS International Conference on Computational intelligence, man-machine systems and cybernetics
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This paper presents a novel algorithm for image matching, i.e. comparing them in order to obtain the similarity measure. We extract geometric features of images by binarizing them and calculating a set of convex layers. These serve as unique features for consequent image comparison and similarity measurement. Our approach has linear computational complexity, and copes with rotated, translated, and scaled images, while it is also rather robust up to moderate signal-to-noise ratios.