Qualitative recognition of motion using temporal texture
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Image velocity estimation from trajectory surface in spatiotemporal space
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
Detecting regions of dynamic texture
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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
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
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
A comparison on textured motion classification
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks
Expert Systems with Applications: An International Journal
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A novel framework and method are proposed to extract local features of a certain kind of naturally occurring, nonrigid motion pattern, referred to as temporal texture. To catch both the spatial and temporal features of this complex pattern, we focus on the surfaces of motion trajectories in spatiotemporal space derived from multiple frames of an image sequence, and represent the surfaces as a set of tangent planes of the surfaces. From the distribution of the tangent planes in local regions in time and space, spatial and temporal texture features are computed. The features considered here include spatial arrangement of dominant contours, uniformity of velocity components, and trajectory run length. Experimental results show that the newly defined features have the capability of quantifying the features of complex motion patterns such as weather radar images.