Three-dimensional computer vision
Three-dimensional computer vision
The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Representing stereo data with the Delaunay triangulation
Artificial Intelligence
Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Images to Surfaces: A Computational Study of the Human Early Visual System
From Images to Surfaces: A Computational Study of the Human Early Visual System
Recognition of Lung Nodules from X-ray CT Images Using 3D Markov Random Field Models
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Fusion of Range and Visual Data for the Extraction of Scene Structure Information
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Occlusion Robust Tracking Utilizing Spatio-Temporal Markov Random Field Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface Sculpting with Stochastic Deformable 3D Surfaces
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Recognition of lung nodule shadows from chest X-ray CT images using 3D Markov random field models
Systems and Computers in Japan
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In this paper, we propose a method for reconstructing the surfaces of objects from stereo data. The proposed method quantitatively defines not only the fitness of the stereo data to surfaces but also the connectivity and smoothness of the surfaces in the framework of a three-dimensional (3-D) Markov Random Field (MRF) model. The surface reconstruction is accomplished by searching for the most possible MRF's state. Experimental results are shown for artificial and actual stereo data.