In this paper, we present a new technique for separating different types of periodic motions in a video sequence. We consider different motions those that have different periodic patterns with one or many fundamental frequencies. We select the temporal Fourier transform for each pixel to be the representation space for a sequence of images. The classification is performed using nonnegative matrix factorization (NNMF) over the power spectra data set. The paper we present can be applied on a wide range of applications for video sequences analysis, such as: background subtraction on nonstatic backgrounds framework, object segmentation and classification. We point out the fact that no registration technique is applied in the method that we introduce. Nevertheless, this method can be used as a cooperative tool for the existing techniques based on camera motion models (motion segmentation, layer classification, tracking of moving objects, etc).

In this paper, we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence. Given a set of time correlated frames, we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution. To this end, we present a generative model which involves jointly the representation and the evolution of appearance. Applying Linear Dynamical System theory to this problem, we discuss how the temporal information is encoded yielding a manner of grouping the iconic representations of the video sequence in terms of invariance. The formulation of this problem is driven in terms of a probabilistic approach, which affords a measure of perceptual similarity taking both learned appearance and time evolution models into account.

This paper is about two topics. First, given a video sequence, we introduce a Bayesian framework where background and moving objects are segmented into layers. The model that describes the different layer evolutions in a sequence of images uses the results of a multi-frame optical flow estimation (MFOFE); we present a new technique based on the fact that each pixel in the frame of reference produces a trajectory in the mosaic absolute coordinate system. The second topic is video summarization. A general moisaicing method is presented for describing the background and the trajectories of moving objects in a sequence of frames. Combining layer segmentation and mosaicing, we show different manners of encoding and visualizing temporal information, where the key point is the selection of a certain object in the images as reference in the evolution.

In this paper we introduce a statistic snake that learns and tracks image features by means of statistic learning techniques. Using probabilistic principal component analysis a feature description is obtained from a training set of object profiles. In our approach a sound statistical model is introduced to define a likelihood estimate of the grey-level local image profiles together with their local orientation. This likelihood estimate allows to define a probabilistic potential field of the snake where the elastic curve deforms to maximise the overall probability of detecting learned image features. To improve the convergence of snake deformation, we enhance the likelihood map by a physics-based model simulating a dipole-dipole interaction. A new extended local coherent interaction is introduced defined in terms of extended structure tensor of the image to give priority to parallel coherence vectors.

We introduce chromatic aberration as a source of visual information that can be useful for autofocus and depth estimation. A color video camera equipped with a lens with chromatic aberration has been used to take images of both step and occlusion edges at several distances from the camera. The defocus measures obtained in the three different RGB color channels of each image are different. We suggest the way this information can be exploited in order to design an autofocus sensor, and also how depth information can be derived.

We address the object recognition problem in a probabilistic framework to detect and describe object appearance through image features organized by means of active contour models. We consider the formulation of saliency in terms of visual similarity embedded in the probabilistic principal component analysis framework. A likelihood of object structure detection is obtained using the relation between the visual field and the internal object representation. Deformable models are employed introducing a computational methodology for a perceptual organisation of image features as an abstract understanding of the integration between structure and constraints of the visual information-processing problem. A specific application of the integrated approach for vessels segmentation in angiography is considered and the results are encouraging.

We introduce a new deformable model, called eigensnake, for segmentation of elongated structures in a probabilistic framework. Instead of snake attraction by specific image features extracted independently of the snake, our eigensnake learns an optimal object description and searches for such image feature in the target image. This is achieved applying principal component analysis on image responses of a bank of Gaussian derivative filters. Therefore, attraction by eigensnakes is defined in terms of classification of image features. The potential energy for the snake is defined in terms of likelihood in the feature space and incorporated into a new energy minimising scheme. Hence, the snake deforms to minimise the mahalanobis distance in the feature space. A real application of segmenting and tracking coronary vessels in angiography is considered and the results are very encouraging.