Qualitative recognition of motion using temporal texture
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
Mixed-State Auto-Models and Motion Texture Modeling
Journal of Mathematical Imaging and Vision
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Global parametric image alignment via high-order approximation
Computer Vision and Image Understanding
FPGA-based System for Real-Time Video Texture Analysis
Journal of Signal Processing Systems
Detecting motion patterns via direction maps with application to surveillance
Computer Vision and Image Understanding
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
Periodicity estimation of Dynamic Textures
International Journal of Information and Communication Technology
Statistical motion information extraction and representation for semantic video analysis
IEEE Transactions on Circuits and Systems for Video Technology
Robust temporal activity templates using higher order statistics
IEEE Transactions on Image Processing
Motion characterization from co-occurrence vector descriptor
Pattern Recognition Letters
Real-time monitoring of water quality using temporal trajectory of live fish
Expert Systems with Applications: An International Journal
Detecting regions of dynamic texture
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Action recognition based on human movement characteristics
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Variational region-based segmentation using multiple texture statistics
IEEE Transactions on Image Processing
Dynamic texture recognition using volume local binary patterns
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
International Journal of Computer Vision
Dynamic texture analysis and classification using deterministic partially self-avoiding walks
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Shift-Invariant dynamic texture recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
2D motion description and contextual motion analysis: issues and new models
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
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Abstract--A new approach for motion characterization in image sequences is presented. It relies on the probabilistic modeling of temporal and scale cooccurrence distributions of local motion-related measurements directly computed over image sequences. Temporal multiscale Gibbs models allow us to handle both spatial and temporal aspects of image motion content within a unified statistical framework. Since this modeling mainly involves the scalar product between cooccurrence values and Gibbs potentials, we can formulate and address several fundamental issues: model estimation according to the ML criterion (hence, model training and learning) and motion classification. We have conducted motion recognition experiments over a large set of real image sequences comprising various motion types such as temporal texture samples, human motion examples, and rigid motion situations.