Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
FCRC '96/WACG '96 Selected papers from the Workshop on Applied Computational Geormetry, Towards Geometric Engineering
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Deformable Objects with Flexible Shape Priors
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Matching Hierarchies of Deformable Shapes
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Searching the web with mobile images for location recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We propose an improved shape matching algorithm that extends the work of Felzenszwalb [3]. In this approach, we use triangular meshes to represent deformable objects and use dynamic programming to find the optimal mapping from the source image to the target image which minimizes a new energy function. Our energy function includes a new cost term that takes into account the center of mass of an image. This term is invariant to translation, rotation, and uniform scaling. We also improve the dynamic programming method proposed in [3] using the center of mass of an image. Experimental results on the Brown dataset show a 7.8% higher recognition rate when compared with Felzenszwalb's algorithm.