Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum likelihood unsupervised textured image segmentation
CVGIP: Graphical Models and Image Processing
Texture segmentation based on a hierarchical Markov random field model
Signal Processing
Interactive Model-Based Vehicle Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Tracking of Position and Velocity With Kalman Snakes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Image Segmentation Using Markov Random Field Models
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Unsupervised Multispectral Image Segmentation Using Generalized Gaussian Noise Model
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Occlusion Robust Tracking Utilizing Spatio-Temporal Markov Random Field Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
Joint random field model for all-weather moving vehicle detection
IEEE Transactions on Image Processing
Learning spatio-temporal dependency of local patches for complex motion segmentation
Computer Vision and Image Understanding
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There have been many successful researches on image segmentations that employ Markov Random Field model. However, most of them were interested in two-dimensional MRF, or spatial MRF, and very few researches are interested in three-dimensional MRF model. Generally, 'three-dimensional' have two meaning, that are spatially three-dimensional and spatio-temporal. In this paper, we especially are interested in segmentations of spatio-temporal images which appears to be equivalent to tracking problem of moving objects such as vehicles etc. For that purpose, by extending usual two-dimensional MRF, we defined a dedicated three-dimensional MRF which we defined as Spatio-Temporal MRF model(S-T MRF). This S-T MRF models a tracking problem by determining labels of groups of pixels by referring to their texture and labeling correlations along the temporal axis as well as the x-y image axes. Although vehicles severely occlude each other in general traffic images, segmentation boundaries of vehicle regions will be determined precisely by this S-T MRF optimizing such boundaries through spatio-temporal images. Consequently, it was proved that the algorithm has performed 95% success of tracking in middle-angle image at an intersection and 91% success in low-angle and front-view images at a highway junction.