Skeleton Branch Distances for Shape Recognition
Boluk, S. Arda
Demirci, Muhammed Fatih
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In this paper, we presented a novel shape recognition framework based on medial axis graph. After extracting medial axis graph of a shape, we constructed a multi-dimensional distribution by calculating distances between each node and all of the branch nodes in the graph. Then similarity rates between these distributions is found by using a transportation-based distance function. At last, we compared our results with results of similar works conducted in the past.