Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
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
Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical motion detection
Investigations of multigrid algorithms for the estimation of optical flow fieldsin image sequences
Computer Vision, Graphics, and Image Processing
Comparison of stochastic and deterministic solution methods in Bayesian estimation of 2D motion
Image and Vision Computing - Special issue on the first ECCV 1990
Relaxing the Brightness Constancy Assumption in Computing Optical Flow
Relaxing the Brightness Constancy Assumption in Computing Optical Flow
Measuring visual motion from image sequences
Measuring visual motion from image sequences
A Fast Scalable Algorithm for Discontinuous Optical Flow Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Image Motion Estimation Algorithm Based on the EM Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting Discontinuities in Optical Flow
International Journal of Computer Vision
Robust Optical Flow Computation Based on Least-Median-of-Squares Regression
International Journal of Computer Vision
Video Segmentation by MAP Labeling of Watershed Segments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Occlusion and Dense Motion Fields in a Bidirectional Bayesian Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
International Journal of Computer Vision
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time Region-Based Motion Segmentation Using Adaptive Thresholding and K-Means Clustering
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Robust estimation of the optical flow based on VQ--BF
Biologically inspired robot behavior engineering
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating Piecewise-Smooth Optical Flow with Global Matching and Graduated Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
MRF-MAP-MFT visual object segmentation based on motion boundary field
Pattern Recognition Letters
Practical, Unified, Motion and Missing Data Treatment in Degraded Video
Journal of Mathematical Imaging and Vision
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Using Fourier local magnitude in adaptive smoothness constraints in motion estimation
Pattern Recognition Letters
Stereo image coder based on the MRF model for disparity compensation
EURASIP Journal on Applied Signal Processing
Least-square prediction for backward adaptive video coding
EURASIP Journal on Applied Signal Processing
An algorithm for motion parameter direct estimate
EURASIP Journal on Applied Signal Processing
Efficient MRF deformation model for non-rigid image matching
Computer Vision and Image Understanding
Pathological motion detection for robust missing data treatment
EURASIP Journal on Advances in Signal Processing
Optical-flow based on an edge-avoidance procedure
Computer Vision and Image Understanding
Visual algorithms for post production
ACM SIGGRAPH 2009 Courses
Motion estimation of deformable objects with motion inertia information
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
Block-based motion field segmentation for video coding
Journal of Visual Communication and Image Representation
Bayesian approaches to motion-based image and video segmentation
IWCM'04 Proceedings of the 1st international conference on Complex motion
Motion estimation and motion-compensated filtering of video signals
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Recursive displacement estimation and restoration of noisy-blurred image sequences
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Planar motion estimation algorithm for region based coding
CONTROL'05 Proceedings of the 2005 WSEAS international conference on Dynamical systems and control
Human motion tracking based on markov random field and hopfield neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Markovian energy-based computer vision algorithms on graphics hardware
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Dense optic flow with a bayesian occlusion model
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Velocity tuned filters for motion estimation and segmentation in digital image sequences
Mathematical and Computer Modelling: An International Journal
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A stochastic approach to the estimation of 2D motion vector fields from time-varying images is presented. The formulation involves the specification of a deterministic structural model along with stochastic observation and motion field models. Two motion models are proposed: a globally smooth model based on vector Markov random fields and a piecewise smooth model derived from coupled vector-binary Markov random fields. Two estimation criteria are studied. In the maximum a posteriori probability (MAP) estimation, the a posteriori probability of motion given data is maximized, whereas in the minimum expected cost (MEC) estimation, the expectation of a certain cost function is minimized. Both algorithms generate sample fields by means of stochastic relaxation implemented via the Gibbs sampler. Two versions are developed: one for a discrete state space and the other for a continuous state space. The MAP estimation is incorporated into a hierarchical environment to deal efficiently with large displacements.