Gibbs Random Fields, Cooccurrences, and Texture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter estimation in hidden fuzzy Markov random fields and image segmentation
Graphical Models and Image Processing
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background Subtraction Using Markov Thresholds
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An a contrario Decision Framework for Region-Based Motion Detection
International Journal of Computer Vision
International Journal of Computer Vision
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Framework for Feature Selection for Background Subtraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Semantic Event Detection using Conditional Random Fields
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixed-State Auto-Models and Motion Texture Modeling
Journal of Mathematical Imaging and Vision
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Triplet Markov Fields for the Classification of Complex Structure Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background Subtraction on Distributions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning for Optical Flow Using Stochastic Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
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In this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction. An accurate estimation of the background is only possible if we locate the moving objects. Meanwhile, a correct motion detection is achieved if we have a good available background model. The key of our joint approach is to define a single random process that can take two types of values, instead of defining two different processes, one symbolic (motion detection) and one numeric (background intensity estimation). It thus allows to exploit the (spatio-temporal) interaction between a decision (motion detection) and an estimation (intensity reconstruction) problem. Consequently, the meaning of solving both tasks jointly, is to obtain a single optimal estimate of such a process. The intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so-called mixed-state statistical framework, which is extended here to account for symbolic states and conditional random fields. Experiments on real sequences and comparisons with existing motion detection methods support our proposal. Further implications for video sequence inpainting will be also discussed.