Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Qualitative recognition of motion using temporal texture
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Robot Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
International Journal of Computer Vision
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Multiscale Segmentation by Combining Motion and Intensity Cues
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Piecewise-Smooth Dense Optical Flow via Level Sets
International Journal of Computer Vision
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
Variational motion segmentation with level sets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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
Vortex and source particles for fluid motion estimation
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
IEEE Transactions on Image Processing
Synergizing spatial and temporal texture
IEEE Transactions on Image Processing
Motion-based object segmentation and estimation using the MDL principle
IEEE Transactions on Image Processing
Dynamic texture detection, segmentation and analysis
Proceedings of the 6th ACM international conference on Image and video retrieval
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
Detection of multiple dynamic textures using feature space mapping
IEEE Transactions on Circuits and Systems for Video Technology
DynTex: A comprehensive database of dynamic textures
Pattern Recognition Letters
Target extraction from the military infrared image with complex texture background
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Digital Signal Processing
Robust Object Detection in Military Infrared Image
International Journal of Advanced Pervasive and Ubiquitous Computing
| Hi-index | 0.00 |
Motion estimation is usually based on the brightness constancy assumption. This assumption holds well for rigid objects with a Lambertian surface, but it is less appropriate for fluid and gaseous materials. For these materials a variant of this assumption, which we call the brightness conservation assumption should be employed. Under this assumption an object's brightness can diffuse to its neighborhood. We propose a method for detecting regions of dynamic texture in image sequences. Segmentation into regions of static and dynamic texture is achieved by using a level set scheme. The level set function separates the images into areas obeying brightness constancy and those which obey brightness conservation. Experimental results on challenging image sequences demonstrate the success of the segmentation scheme and validate the model.