Generalised relaxed Radon transform (GR2T) for robust inference
Pattern Recognition
Bayesian shape from silhouettes
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Bayesian 3D shape from silhouettes
Digital Signal Processing
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We present a statistical framework to merge the information from silhouettes segmented in multiple view images to infer the 3D shape of an object. The approach is generalising the robust but discrete modelling of the visual hull by using the concept of averaged likelihoods. One resulting advantage of our framework is that the objective function is continuous and therefore an iterative gradient ascent algorithm can be defined to efficiently search the space. Moreover this results in a method which is less memory demanding and one that is very suitable to a parallel processing architecture. Experimental results shows that this approach is efficient for getting a robust initial guess to the 3D shape of an object in view