Denoising 3D microscopy images of cell nuclei using shape priors on an anisotropic grid

This paper presents a new multiscale method to denoise three-dimensional images of cell nuclei. The speci- ficity of this method is its awareness of the noise distribution and object shapes. It combines a multiscale representation called Isotropic Undecimated Wavelet Transform (IUWT) with a nonlinear transform, a statistical test and a variational method, to retrieve spherical shapes in the image. Beyond extending an existing 2D approach to a 3D problem, our algorithm takes the sampling grid dimensions into account. We compare our method to the two algorithms from which it is derived on a representative image analysis task, and show that it is superior to both of them. It brings a slight improvement in the signal-to-noise ratio and a significant improvement in cell detection

A Particle Filtering Approach for Tracking an Unknown Number of Objects with Dynamic Relations

In recent years there has been a growing interest on particle filters for solving tracking problems, thanks to their applicability to problems with continuous, non-linear and non-Gaussian state spaces, which makes them more suited than hidden Markov models, Kalman filters and their derivations, in many real world tasks. Applications include video surveillance, sensor fusion, tracking positions and behaviors of moving objects, situation assessment in civil and bellic scenarios, econometric and clinical data series analysis. In many environments it is possible to recognize classes of similar entities, like pedestrians or vehicles in a video surveillance system, or commodities in econometric. In this paper, a relational particle filter for tracking an unknown number of objects is presented which exploits possible interactions between objects to improve the quality of filtering. We will see that taking into account relations between objects will ease the tracking of objects in presence of occlusions and discontinuities in object dynamics. Experimental results on a benchmark data set are presented. \ua9 2012 Springer Science+Business Media Dordrecht

MULTIPLE OBJECT TRACKING WITH RELATIONS

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Simultaneous Tracking and Activity Recognition

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[WORK DOCUMENT] Towards interactive causal relation discovery driven by an ontology

In complex domains, users need to be able to find causal relations between the different attributes that compose them. Our work offers a way to help users to uncover such relations by combining the representativity of ontologies and the flexibility of probabilistic relational models, and provides them with an interactive and iterative process in order to validate or modify the obtained results

Bayesian Vote Elicitation for Group Recommendations

International audienceElicitation of preferences is a critical task in modern application of voting protocols such as group recommender systems. This paper introduces a Bayesian elicitation paradigm for social choice. The system maintains a probability distribution over the preferences (rankings) of the voters. At each step the system asks the question to one of the voters, and the distribution is conditioned on the response. We consider strategies to pick the next question based on value of information, conditional entropy, and a mix of these two notions. We develop this idea focusing on scoring rules and compare different elicitation strategies in the case of Borda rule

Towards Interactive Causal Relation Discovery Driven by an Ontology

International audienceDiscovering causal relations in a knowledge base represents nowadays a challenging issue, as it gives a brand new way of understanding complex domains. In this paper, we present a method to combine an ontology with a probabilistic rela-tional model (PRM), in order to help a user to check his/her assumption on causal relations between data and to discover new relationships. This assumption is important as it guides the PRM construction and provide a learning under causal constraints