We propose a generative model for social networks, both undirected and directed, that takes into account two fundamental characteristics of the user: background (specifically, the real world groups to which the user belongs); and behavior (namely, the ways in which the user engages in surfing activity and occasionally adds links to other users encountered this way). Our experiments show that ne...

Hidden Markov Models (HMMs) are increasingly being used in computer vision for applications such as: gesture analysis, action recognition from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a stationarity assumption on the model parameters). But, realworld applications are often non-stationary in nature. T...

Moving object detection is a relevant step for many computer vision applications, and specifically for real-time color video surveillance systems, where processing time is a challenging issue. We adopt a dual background approach for detecting moving objects and discriminating those that have stopped, based on a neural model capable of learning from past experience and efficiently detecting such...

Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for stereo sequences that improves the reliability and sensitivity of segmentation in the presence of object clutter. The dynamic version of the method, called gated background adaptation, can reliably learn background sta...

We examine the fundamental problem of background modeling which is to model the background scenes in video sequences and segment the moving objects from the background. A novel approach is proposed based on the Restricted Boltzmann Machine (RBM) while exploiting the temporal nature of the problem. In particular, we augment the standard RBM to take a window of sequential video frames as input an...

Video compression takes a very important part in multimedia communication. New applications require constrained resources in terms of power, computational complexity, memory and robustness. Distributed video coding (DVC) provides a solution to achieve such constraints. However, DVC has a low coding efficiency, the overall performance is lower than the H.264/AVC [1] codec. In low-motion scenes s...

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Background modeling and subtraction using subspaces is attractive in real-time computer vision applications due to its low computational cost. However, the application of this method is mostly limited to the gray-scale images since the integration of multi-channel data is not straightforward; it involves much higher dimensional space and causes additional difficulty to manage data in general. W...

Background modeling algorithms are commonly used in camera setups for foreground object detection. Typically, these algorithms need adjustment of their parameters towards achieving optimal performance in different scenarios and/or lighting conditions. This is a tedious process requiring considerable effort by expert users. In this work we propose a novel, fully automatic method for the tuning o...

A method for locating apples was developed to process real-time video image sequences captured with an over-the-row harvester. The concepts of background modeling in RGB color were used, which is a novel approach to the apple segmentation problem. In background modeling, the distributions of background colors are approximated from real data. The algorithm developed for this task, Global Mixture...