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Thomas B. Sebastian, Philip N. Klein, Benjamin B. Kimia, "Recognition of Shapes by Editing Their Shock Graphs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 550571, May, 2004.  
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@article{ 10.1109/TPAMI.2004.1273924, author = {Thomas B. Sebastian and Philip N. Klein and Benjamin B. Kimia}, title = {Recognition of Shapes by Editing Their Shock Graphs}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {26}, number = {5}, issn = {01628828}, year = {2004}, pages = {550571}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.1273924}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Recognition of Shapes by Editing Their Shock Graphs IS  5 SN  01628828 SP550 EP571 EPD  550571 A1  Thomas B. Sebastian, A1  Philip N. Klein, A1  Benjamin B. Kimia, PY  2004 KW  Shape deformation KW  shock graphs KW  graph matching KW  edit distance KW  shape matching KW  object recognition KW  dynamic programming. VL  26 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very highdimensional, three steps are taken to make the search practical: 1) define an equivalence class for shapes based on shockgraph topology, 2) define an equivalence class for deformation paths based on shockgraph transitions, and 3) avoid complexityincreasing deformation paths by moving toward shockgraph degeneracy. Despite these steps, which tremendously reduce the search requirement, there still remain numerous deformation paths to consider. To that end, we employ an editdistance algorithm for shock graphs that finds the optimal deformation path in polynomial time. The proposed approach gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations. The recognition rates on two distinct databases of 99 and 216 shapes each indicate highly successful within category matches (100 percent in top three matches), which render the framework potentially usable in a range of shapebased recognition applications.
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