Modelling and identification of characteristic intensity variations
Image and Vision Computing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
BAS: a perceptual shape descriptor based on the beam angle statistics
Pattern Recognition Letters
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
Fourier Preprocessing for Hand Print Character Recognition
IEEE Transactions on Computers
Angle Detection on Digital Curves
IEEE Transactions on Computers
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Pattern recognition with computer systems presents a great challenge to researchers in computer science. In some cases patterns can be differentiated by their silhouette and recognized through the contour around its shape. In this study, shape is deliberately examined in terms of its flexibility, a measure defined to be the extent to which curves could be deformed. The proposed flexible object recognition (FOR) method widely employs flexibility to find corners, to form segments, to match segment pairs, and eventually to calculate the dissimilarity of contour pairs. Further analysis on classification with such dissimilarities shows rather high accuracy rates across various application domains, which may provide an evidence of the robustness for the proposed FOR method.