A BAYESIAN APPROACH TO 3D OBJECT RECOGNITION USING LINEAR COMBINATION OF 2D VIEWS
Vasileios Zografos, Bernard F. Buxton · 2008
In this work, we introduce a Bayesian approach for pose-invariant recognition of the images of 3d objects modelled by a small number of stored 2d intensity images taken from nearby but otherwise arbitrary viewpoints. A linear combination of views approach is used to combine images from two viewpoints of a 3d object and synthesise novel views of that object. Recognition is performed by matching a target, scene image to such a synthesised, novel view using an optimisation algorithm, constrained by construction of Bayes prior distributions on the linear combination. We have experimented with both a direct search and an evolutionary optimisation method on a real-image, public database. The Bayes priors effectively regularised the posterior distribution so that all algorithms were able to find good solutions close to the optimum. Further exploration of the parameter space has been carried out using Markov-Chain Monte-Carlo sampling. 1