Determination of the identity, position and orientation of the topmost object in a pile
Computer Vision, Graphics, and Image Processing
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization by simulated annealing
Readings in computer vision: issues, problems, principles, and paradigms
Readings in computer vision: issues, problems, principles, and paradigms
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in range images based on scan line approximation
Computer Vision and Image Understanding
Markov random field modeling in image analysis
Markov random field modeling in image analysis
The Complex EGI: A New Representation for 3-D Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Markov random field modeled range image segmentation
Pattern Recognition Letters
Range image segmentation based on randomized Hough transform
Pattern Recognition Letters
Roof-edge preserving image smoothing based on MRFs
IEEE Transactions on Image Processing
Range image segmentation using surface selection criterion
IEEE Transactions on Image Processing
A Distributed and Collective Approach for Curved Object-Based Range Image Segmentation
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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In this paper we present and evaluate a new Bayesian method for range image segmentation. The method proceeds in two stages. First, an initial segmentation is produced by a randomized region growing technique. The produced segmentation is considered as a degraded version of the ideal segmentation, which should be then refined. In the second stage, pixels not labeled in the first stage are labeled by using a Bayesian estimation based on some prior assumptions on the regions of the image. The image priors are modeled by a new Markov Random Field (MRF). model. Contrary to most of the authors in range image segmentation, who use only surface smoothness MRF models, our MRF takes into account also the smoothness of region boundaries. Tests performed with real images from the ABW database show a good potential of the proposed method for significantly improving the segmentation results.