Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Computation of component image velocity from local phase information
International Journal of Computer Vision
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation and qualitative dynamic scene analysis from an image sequence
International Journal of Computer Vision
Computing occluding and transparent motions
International Journal of Computer Vision
Multiscale minimization of global energy functions in some visual recovery problems
CVGIP: Image Understanding
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation and analysis of image motion: a synopsis of current problems and methods
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct incremental model-based image motion segmentation for video analysis
Signal Processing - Video segmentation for content-based processing manipulation
Image Sequence Analysis via Partial Differential Equations
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Computing Optical Flow with Physical Models of Brightness Variation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense Estimation of Fluid Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Parametric Motion Activity Analysis for Statistical Retrieval with Partial Query
Journal of Mathematical Imaging and Vision
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporally Adaptive Estimation and Segmenation of OF-Fields
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Tracking and Characterization of Highly Deformable Cloud Structures
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Layered 4D Representation and Voting for Grouping from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Grouping Principle and Four Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Textured Motion: Particle, Wave and Sketch
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Variational Space-Time Motion Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Layered Motion Segmentation and Depth Ordering by Tracking Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Multiple motion segmentation with level sets
IEEE Transactions on Image Processing
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In this paper, several important issues related to visual motion analysis are addressed with a focus on the type of motion information to be estimated and the way contextual information is expressed and exploited. Assumptions (i.e., data models) must be formulated to relate the observed image intensities with motion, and other constraints (i.e., motion models) must be added to solve problems like motion segmentation, optical flow computation, or motion recognition. The motion models are supposed to capture known, expected or learned properties of the motion field, and this implies to somehow introduce spatial coherence or more generally contextual information. The latter can be formalized in a probabilistic way with local conditional densities as in Markov models. It can also rely on predefined spatial supports (e.g., blocks or pre-segmented regions). The classic mathematical expressions associated with the visual motion information are of two types. Some are continuous variables to represent velocity vectors or parametric motion models. The other are discrete variables or symbolic labels to code motion detection output (binary labels) or motion segmentation output (numbers of the motion regions or layers). We introduce new models, called mixed-state auto-models, whose variables belong to a domain formed by the union of discrete and continuous values, and which include local spatial contextual information. We describe how such models can be specified and exploited in the motion recognition problem. Finally, we present a new way of investigating the motion detection problem with spatial coherence being associated to a perceptual grouping principle.