Qualitative recognition of motion using temporal texture
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
Robot Vision
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction of Temporal Texture Based on Spatiotemporal Motion Trajectory
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Dynamic Texture Recognition by Spatio-Temporal Multiresolution Histograms
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Optical Flow Estimation and Segmentation of Multiple Moving Dynamic Textures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Subspace Approach to Texture Modelling by Using Gaussian Mixtures
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Dynamic texture recognition using normal flow and texture regularity
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Synergizing spatial and temporal texture
IEEE Transactions on Image Processing
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
Fast dynamic texture detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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
Scale-space texture description on SIFT-like textons
Computer Vision and Image Understanding
Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks
Expert Systems with Applications: An International Journal
Digital Signal Processing
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We address the problem of dynamic texture (DT) classification using optical flow features. Optical flow based approaches dominate among the currently available DT classification methods. The features used by these approaches often describe local image distortions in terms of such quantities as curl or divergence. Both normal and complete flows have been considered, with normal flow (NF) being used more frequently. However, precise meaning and applicability of normal and complete flow features have never been analysed properly. We provide a principled analysis of local image distortions and their relation to optical flow. Then we present the results of a comprehensive DT classification study that compares the performances of different flow features for a NF algorithm and four different complete flow algorithms. The efficiencies of two flow confidence measures are also studied.