Dense Estimation of Fluid Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
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
Mixed-State Auto-Models and Motion Texture Modeling
Journal of Mathematical Imaging and Vision
Real-time detection of steam in video images
Pattern Recognition
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smoke detection in video surveillance: the use of ViSOR (video surveillance on-line repository)
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic texture as foreground and background
Machine Vision and Applications - Special Issue on Dynamic Textures in Video
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
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Dynamic textures can be considered to be spatio-temporally varying visual patterns in image sequences with certain temporal regularity. We propose a novel and efficient approach to explore the violation of the brightness constancy assumption, as an indication of presence of dynamic texture, using simple optical flow techniques. We assume that dynamic texture regions are those that have poor spatio-temporal optical flow coherence. Further, we propose a second approach that uses robust global parametric motion estimators that effectively and efficiently detect motion outliers, and which we exploit as powerful cues to localize dynamic textures. Experimental and comparative studies on a range of synthetic and real-world dynamic texture sequences show the feasibility of the proposed approaches, with results which are competitive to or better than recent state-of-art approaches and significantly faster.