A motion texture is an instantaneous motion map extracted from a dynamic texture. We observe that such motion maps exhibit values of two types: a discrete component at zero (absence of motion) and continuous motion values. We thus develop a mixed-state Markov random field model to represent motion textures. The core of our approach is to show that motion information is powerful enough to classify and segment dynamic textures if it is properly modeled regarding its specific nature and the local interactions involved. A parsimonious set of 11 parameters constitutes the descriptive feature of a motion texture. The motivation of the proposed formulation runs toward the analysis of dynamic video contents, and we tackle two related problems. First, we present a method for recognition and classification of motion textures, by means of the Kullback--Leibler distance between mixed-state statistical models. Second, we define a two-frame motion texture maximum a posteriori (MAP)-based segmentation method applicable to motion textures with deforming boundaries. We also investigate a new issue, the space-time dynamic texture segmentation, by combining the spatial segmentation and the recognition methods. Numerous experimental results are reported for those three problems which demonstrate the efficiency and accuracy of the proposed two-frame approach.

The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ¿mixed state¿ refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results.

Dynamic textures are time-varying visual patterns that exhibit certain spatio-temporal stationarity properties and are displayed mostly by natural scene elements. In this paper, we present new statistical models for the characterization of motion in this type of sequences. First we observe that motion measurements present values of two types: a discrete component at zero expressing the absence of motion and a continuous distribution for the rest of the motion values. Thus, we define random variables with mixed-states and propose to model a sequence of motion maps as a Markov chain, where the transition densities are mixed-state probability densities. Based on this approach, we propose a method for dynamic texture segmentation in real sequences showing the efficiency of the proposal in dynamic content analysis applications.

The aim of this work is to model the apparent motion in image sequences depicting natural dynamic scenes. We adopt the mixed-state Markov Random Fields (MRF) models recently introduced to represent so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit mixed-state nature: a discrete component related to the absence of motion and a continuous part for measurements different from zero. We propose several significative extensions to this model. We define an original motion texture segmentation method which does not assume conditional independence of the observations for each texture and normalizing factors are properly handled. Results on real examples demonstrate the accuracy and efficiency of our method.

The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos.