Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Object detection by global contour shape
Pattern Recognition
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Image ordering by cellular genetic algorithms with TSP and ICA
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Spatio-temporal descriptor using 3D curvature scale space
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Partition-induced connections and operators for pattern analysis
Pattern Recognition
Shape recognition, with applications to a passive assistant
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Object detection with feature stability over scale space
Journal of Visual Communication and Image Representation
Transportation Distances on the Circle
Journal of Mathematical Imaging and Vision
Visual pathways for detection of landmark points
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
The monogenic curvature scale-space
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
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A multiscale, morphological method for the purpose of shape-based object recognition is presented. A connected operator similar to the morphological hat-transform is defined, and two scale-space representations are built, using the curvature function as the underlying one-dimensional signal. Each peak and valley of the curvature is extracted and described by its maximum and average heights and by its extent and represents an entry in the top or bottom hat-transform scale spaces. We demonstrate object recognition based on hat-transform scale spaces for three large data sets, a set of diatom contours, the set of silhouettes from the MPEG-7 database and the set of two-dimensional views of three-dimensional objects from the COIL-20 database. Our approach outperforms other methods for which comparative results exist.