Information point set registration for shape recognition
This paper proposes a way of enhancing shape recognition through point set registration. Firstly, a modified version of shape context (SC) is developed, which is invariant to rigid transformation and flipping. With the point correspondence obtained by the modified SC, an affine transformation based on the maximum correntropy criterion (MCC) is performed on the query shape. This point set registration could be further refined by non-rigid morphing with the minimization of Cauchy-Schwarz divergence (DCS). Not only does this information theoretical learning (ITL) approach renders excellent registration result, but a new shape similarity measure can also be derived from the registration.