We present a new scheme for the estimation of Markov random field line process parameters which uses geometric CAD models of the objects in the scene. The models are used to generate synthetic images of the objects from random view points. The edge maps computed from the synthesized images are used as training samples to estimate the line process parameters using a least squares method. We show that this parameter estimation method is useful for detecting edges in range as well as intensity edges. The main contributions of the paper are: 1) use of CAD models to obtain true edge labels which are otherwise not available; and 2) use of canonical Markov random field representation to reduce the number of parameters.

A scheme for the estimation of the Markov random field (MRF) line process parameters that uses geometric CAD models of the objects in the scene is presented. The models are used to generate synthetic images of the objects from random viewpoints. The edge maps computed from the synthesized images are used as training samples to estimate the line process parameters using a least squares method. It is shown that this parameter estimation method is useful for detecting edges in range as well as intensity images.>

Consideration is given to the application of Markov random field (MRF) models to the problem of edge labeling in range images. The authors propose a segmentation algorithm which handles both jump and crease edges. The jump and crease edge likelihoods at each edge site are computed using special local operators. These likelihoods are then combined in a Bayesian framework with a MRF prior distribution on the edge labels to derive the a posterior distribution of labels. An approximation to the maximum a posteriori estimate is used to obtain the edge labelings. The edge-based segmentation has been integrated with a region-based segmentation scheme resulting in a robust surface segmentation method.>

The authors empirically compare three algorithms for segmenting simple, noisy images: simulated annealing (SA), iterated conditional modes (ICM), and maximizer of the posterior marginals (MPM). All use Markov random field (MRF) models to include prior contextual information. The comparison is based on artificial binary images which are degraded by Gaussian noise. Robustness is tested with correlated noise and with object and background textured. The ICM algorithm is evaluated when the degradation and model parameters must be estimated, in both supervised and unsupervised modes and on two real images. The results are assessed by visual inspection and through a numerical criterion. It is concluded that contextual information from MRF models improves segmentation when the number of categories and the degradation model are known and that parameters can be effectively estimated. None of the three algorithms is consistently best, but the ICM algorithm is the most robust. The energy of the a posteriori distribution is not always minimized at the best segmentation.>