Integrated region- and pixel-based approach to background modelling

In this paper a new probabilistic method for background modelling is proposed, aimed at the application in video surveillance tasks using a monitoring static camera. Recently, methods employing Time-Adaptive, Per Pixel, Mixture of Gaussians (TAPPMOG) modelling have become popular due to their intrinsic appealing properties. Nevertheless, they are not able per se to monitor global changes in the scene, because they model the background as a set of independent pixel processes. In this paper, we propose to integrate this kind of pixel-based information with higher level region-based information, that permits to manage also sudden changes of the background. These pixel- and regionbased modules are naturally and effectively embedded in a probabilistic Bayesian framework called particle filtering, that allows a multi-object tracking. Experimental comparison with a classic pixel-based approach reveals that the proposed method is really effective in recovering from situations of sudden global illumination changes of the background, as well as limited non-uniform changes of the scene illumination.

EM Training of Hidden Markov Models for Shape Recognition Using Cyclic Strings

Shape descriptions and the corresponding matching techniques must be robust to noise and invariant to transformations for their use in recognition tasks. Most transformations are relatively easy to handle when contours are represented by strings. However, starting point invariance is difficult to achieve. One interesting possibility is the use of cyclic strings, which are strings with no starting and final points. Here we present the use of Hidden Markov Models for modelling cyclic strings and their training using Expectation Maximization. Experimental results show that our proposal outperforms other methods in the literature

Unsupervised activity recognition for autonomous water drones

We propose an automatic system aimed at discovering relevant activities for aquatic drones employed in water monitoring applications. The methodology exploits unsupervised time series segmentation to pursue two main goals: i) to support on-line decision making of drones and operators, ii) to support off-line analysis of large datasets collected by drones. The main novelty of our approach consists of its unsupervised nature, which enables to analyze unlabeled data. We investigate different variants of the proposed approach and validate them using an annotated dataset having labels for activity \u201cupstream/downstream navigation\u201d. Obtained results are encouraging in terms of clustering purity and silhouette which reach values greater than 0.94 and 0.20, respectively, in the best models

Recognizing People's Faces: from Human to Machine Vision

Recognizing people's face

Biclustering of expression microarray data with topic models

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Measuring health inequality among children in developing countries: does the choice of the indicator of economic status matter?

Background Currently, poor-rich inequalities in health in developing countries receive a lot of attention from both researchers and policy makers. Since measuring economic status in developing countries is often problematic, different indicators of wealth are used in different studies. Until now, there is a lack of evidence on the extent to which the use of different measures of economic status affects the observed magnitude of health inequalities. Methods This paper provides this empirical evidence for 10 developing countries, using the Demographic and Health Surveys data-set. We compared the World Bank asset index to three alternative wealth indices, all based on household assets. Under-5 mortality and measles immunisation coverage were the health outcomes studied. Poor-rich inequalities in under-5 mortality and measles immunisation coverage were measured using the Relative Index of Inequality. Results Comparing the World Bank index to the alternative indices, we found that (1) the relative position of households in the national wealth hierarchy varied to an important extent with the asset index used, (2) observed poor-rich inequalities in under-5 mortality and immunisation coverage often changed, in some cases to an important extent, and that (3) the size and direction of this change varied per country, index, and health indicator. Conclusion Researchers and policy makers should be aware that the choice of the measure of economic status influences the observed magnitude of health inequalities, and that differences in health inequalities between countries or time periods, may be an artefact of different wealth measures used

Combining free energy score spaces with information theoretic kernels: Application to scene classification

Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vectorial data (e.g., support vector machines) are learned discriminatively. A generative embed-ding is a mapping from the object space into a fixed dimensional score space, induced by a generative model, usually learned from data. The fixed dimensionality of these generative score spaces makes them adequate for discriminative learning of classifiers, thus bringing together the best of the discriminative and generative paradigms. In particular, it was recently shown that this hybrid ap-proach outperforms a classifier obtained directly for the generative model upon which the score space was built. Using a generative embedding involves two steps: (i) defining and learning the generative model and using it to build the embed-ding; (ii) discriminatively learning a (maybe kernel) classifier on the adopted score space. The literature on generative embeddings is es-sentially focused on step (i), usually using some standard off-the-shelf tool for step (ii). In this paper, we adopt a different approach, by focusing also on the discriminative learning step. In particular, we combine two very recent and top performing tools in each of the steps: (i) the free energy score space; (ii) non-extensive information theoretic kernels. In this paper, we apply this methodology in scene recognition. Experimental results on two benchmark datasets shows that our approach yields state-of-the-art performance. Index Terms — Scene categorization, generative embeddings, score spaces, information theoretic kernels

2D Shape Recognition Using Information Theoretic Kernels

In this paper, a novel approach for contour-based 2D shape recognition is proposed, using a recently intro-duced class of information theoretic kernels. This kind of kernels, based on a non-extensive generalization of the classical Shannon information theory, are defined on probability measures. In the proposed approach, chain code representations are first extracted from the contours; then n-gram statistics are computed and used as input to the information theoretic kernels. We tested different versions of such kernels, using support vector machine and nearest neighbor classifiers. An experi-mental evaluation on the chicken pieces dataset shows that the proposed approach outperforms the current state-of-the-art methods. 1

Mortality Measurement Matters: Improving Data Collection and Estimation Methods for Child and Adult Mortality

Colin Mathers and Ties Boerma discuss three research articles in PLoS Medicine that address the measurement and analysis of child and adult mortality data collected through death registration, censuses, and household surveys

Model based dynamics analysis in live cell microtubule images

Background: The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. Results: In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. Conclusion: Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior