Video Segmentation by MAP Labeling of Watershed Segments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
Statistical model for intensity differences of corresponding points between stereo image pairs
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
An algorithm for motion parameter direct estimate
EURASIP Journal on Applied Signal Processing
Nonrigid motion estimation from a sequence of degraded images
Mathematical and Computer Modelling: An International Journal
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Motion estimation belongs to key techniques in image sequence processing. Segmentation of the motion fields such that, ideally, each independently moving object uniquely corresponds to one region, is one of the essential elements in object-based image processing. This paper is concerned with unsupervised simultaneous estimation of dense motion fields and their segmentations. It is based on a stochastic model relating image intensities to motion information. Based on the analysis of natural images, a region-based model of motion-compensated prediction error is proposed. In each region the error is modeled by a white stationary generalized Gaussian random process. The motion field and its segmentation are themselves modeled by a compound Gibbs/Markov random field accounting for statistical bindings in spatial direction and along the direction of motion trajectories. The a posteriori distribution of the motion field for a given image sequence is formulated as an objective function, such that its maximization results in the MAP estimate. A deterministic multiscale relaxation technique with regular structure is employed for optimization of the objective function. Simulation results are in a good agreement with human perception for both the motion fields and their segmentations