A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
The theory and practice of Bayesian image labeling
International Journal of Computer Vision
BONSAI: 3D Object Recognition Using Constrained Search
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Markov random field contextual models in computer vision
Markov random field contextual models in computer vision
Close-Form Solution and Parameter Selection for Convex Minimization-Based Edge-Preserving Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
Automated estimation of the parameters of Gibbs priors to be used in binary tomography
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
Error Analysis in Homography Estimation by First Order Approximation Tools: A General Technique
Journal of Mathematical Imaging and Vision
Markov random field approach to region extraction using Tabu Search
Journal of Visual Communication and Image Representation
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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: i) use of CAD models to obtain true edge labels which are otherwise not available, and ii) use of canonical MRF representation to reduce the number of parameters.