Support Vector Shape: A Classifier-Based Shape Representation
We introduce a novel implicit representation for 2D and 3D shapes based on Support Vector Machine (SVM) theory. Each shape is represented by an analytic decision function obtained by training SVM, with a Radial Basis Function (RBF) kernel so that the interior shape points are given higher values. This empowers support vector shape (SVS) with multifold advantages. First, the representation uses a sparse subset of feature points determined by the support vectors, which significantly improves the...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|01_Nguyen_Support_Vector_Shape:_A_2012.pdf||1.68 MB||Adobe PDF||Request a copy|
|02_Nguyen_Support_Vector_Shape:_A_2012.pdf||2.77 MB||Adobe PDF||Request a copy|
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