Publications related to Anomaly detection

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Jean-Philippe Thiran, Meritxell Bach Cuadra, Xavier Bresson, Nawal Houhou, Anca-Ramona Ciurte, Alessia Pica

Segmenting ultrasound images is a challenging problem where standard unsupervised segmentation methods such as the well-known Chan-Vese method fail. We propose in this paper an efficient segmentation method for this class of images. Our proposed algorithm ...

2011

Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model

Jean-Marc Odobez, Rémi Emonet, Jagannadan Varadarajan

In this paper, we present an unsupervised method for mining activities in videos. From unlabeled video sequences of a scene, our method can automatically recover what are the recurrent temporal activity patterns (or motifs) and when they occur. Using non p ...

2011

Distributed sensor failure detection in sensor networks

Pascal Frossard, Nikolaos Thomos, Tamara Tosic

We investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from neighboring sensor measurements. Defective sensors are represented by non-zero values in ...

2011

Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hesam Sagha

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices according to a recognition goal at runtime that just happen to be available instead of pre-defining them at the design time of the system. Whenever a ...

2011

Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hamidreza Bayati

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors can affect the data distribution between two different experimental sessions including sensor displacemen ...

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FlowBoost - Appearance Learning from Sparsely Annotated Video

François Fleuret, Karim Ali, David Hasler

We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists in iteratively improving an appearance based model built with a Boosting pro ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Are Sparse Representations Really Relevant for Image Classification?

Vincent Lepetit, Roberto Rigamonti, Matthew Brown

Recent years have seen an increasing interest in sparse representations for image classiﬁcation and object recognition, probably motivated by evidence from the analysis of the primate visual cortex. It is still unclear, however, whether or not sparsity hel ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Sensor array optimization for sources separation and detection in the at-worst determined case

Hervé Lissek, Xavier Falourd, Patrick Marmaroli

Most of the number of sources estimation techniques use the well-known signal-subspace approach in which the number of dominant sources is deduced regarding the multiplicity of the lowest eigenvalues of the correlation matrix. In the at-worst determined ca ...

2011

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

Jean-Philippe Thiran, Meritxell Bach Cuadra, Subrahmanyam Venkata Ravi Mohana Sai Gorthi

This paper presents 3-D brain tissue classification schemes using three recent promising energy minimization methods for Markov random fields: graph cuts, loopy belief propagation and tree-reweighted message passing. The classification is performed using t ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

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Anomaly detection

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model

Information Processing and Structure of Dynamical Networks

Distributed sensor failure detection in sensor networks

Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition

FlowBoost - Appearance Learning from Sparsely Annotated Video

Are Sparse Representations Really Relevant for Image Classification?

Sensor array optimization for sources separation and detection in the at-worst determined case

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

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Chat with Graph Search

Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model

Information Processing and Structure of Dynamical Networks

Distributed sensor failure detection in sensor networks

FlowBoost - Appearance Learning from Sparsely Annotated Video

Are Sparse Representations Really Relevant for Image Classification?

Sensor array optimization for sources separation and detection in the at-worst determined case

An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition