Dynamic textures are sequences of images that exhibit some form of temporal stationarity, such as waves, steam, and foliage. We pose the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented. Since the space is non-linear, a distance between models must be defined. We examine three different distances in the space of autoregressive models and assess their power. 1.

Abstract. Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals and point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.

We present a framework for the classification of visual processes that are best modeled with spatio-temporal autoregressive models. The new framework combines the modeling power of a family of models known as dynamic textures and the generalization guarantees, for classification, of the support vector machine classifier. This combination is achieved by the derivation of a new probabilistic kernel based on the Kullback-Leibler divergence (KL) between Gauss-Markov processes. In particular, we derive the KL-kernel for dynamic textures in both 1) the image space, which describes both the motion and appearance components of the spatio-temporal process, and 2) the hidden state space, which describes the temporal component alone. Together, the two kernels cover a large variety of video classification problems, including the cases where classes can differ in both appearance and motion and the cases where appearance is similar for all classes and only motion is discriminant. Experimental evaluation on two databases shows that the new classifier achieves superior performance over existing solutions. 1.

We propose to model the traffic flow in a video using a holistic generative model that does not require segmentation or tracking. In particular, we adopt the dynamic texture model, an auto-regressive stochastic process, which encodes the appearance and the underlying motion separately into two probability distributions. With this representation, retrieval of similar video sequences and classification of traffic congestion can be performed using the Kullback-Leibler divergence and the Martin distance. Experimental results show good retrieval and classification performance, with robustness to environmental conditions such as variable lighting and shadows.

Interior sampling and exterior sampling (enclosure) signal-subspace-based imaging methodologies for extended scatterers derived in previous work are reformulated and reinterpreted in terms of the concepts of angles and distances between subspaces. The insight gained from this reformulation naturally paves the way for a broader, more encompassing inversion methodology based on a cross-coherence matrix associated to the singular vectors of the scattering or response matrix and the singular vectors intrinsic to a given, hypothesized support region for the scatterers (under a known background Green function associated to a known embedding medium where the scatterers reside). A number of new imaging functionals based on that cross-coherence matrix emerge, being of particular interest imaging functionals based on information-theoretic concepts applied to an interpretation of the entries in that matrix as probability amplitudes. The resulting approach is based on entropy minimization, and it has the enormous advantage of not requiring for its implementation the estimation of a cutoff in the singular value spectrum separating signal versus noise subspaces, which is a common computational difficulty in both imaging and shape reconstruction contexts. The theoretical and computational concepts developed in the paper are illustrated for electromagnetic scattering examples in twodimensional space. Both imaging and shape reconstruction contexts are considered, and in the shape reconstruction context it is also shown how to combine the signal subspace approach with the level set method. I.

Abstract. A novel approach to the problem of locating and recognizing anatomical structures of interest in ultrasound (US) video is proposed. While addressing this challenge may be beneficial to US examinations in general, it is particularly useful in situations where portable US probes are used by less experienced personnel. The proposed solution is based on the hypothesis that, rather than their appearance in a single image, anatomical structures are most distinctively characterized by the variation of their appearance as the transducer moves. By drawing on recent advances in the non-linear modeling of video appearance and motion, using an extension of dynamic textures, successful location and recognition is demonstrated on two phantoms. We further analyze computational demands and preliminarily explore insensitivity to anatomic variations. 1