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Variational Methods for Human Modeling

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A Fast Parts-Based Approach to Speaker Verification Using Boosted Slice Classifiers

Sébastien Marcel, Mathew Magimai Doss, Anindya Roy

Speaker verification (SV) on portable devices like smartphones is gradually becoming popular. In this context, two issues need to be considered: 1) such devices have relatively limited computation resources, and 2) they are liable to be used everywhere, po ...

2012

A Constrained Latent Variable Model

Pascal Fua, Mathieu Salzmann, Raquel Urtasun, Aydin Varol

Latent variable models provide valuable compact representations for learning and inference in many computer vision tasks. However, most existing models cannot directly encode prior knowledge about the specific problem at hand. In this paper, we introduce a ...

Ieee2012

Macro-action discovery based on change point detection and boosting

François Fleuret, Leonidas Lefakis

We present a novel approach to automatic macroaction discovery and its application to a complex goal-planning task. The problem of macro-action discovery is framed as one of multiple change point detection and is addressed with the help of the Dynamic Prog ...

2012

SCOOP: A Real-Time Sparsity Driven People Localization Algorithm

Pierre Vandergheynst, Alexandre Massoud Alahi, Mohammad Golbabaee

Detecting and tracking people in scenes monitored by cameras is an important step in many application scenarios such as surveillance, urban planning or behavioral studies to name a few. The amount of data produced by camera feeds is so large that it is als ...

Springer Verlag2012

Object Duplicate Detection

Péter Vajda

With the technological evolution of digital acquisition and storage technologies, millions of images and video sequences are captured every day and shared in online services. One way of exploring this huge volume of images and videos is through searching a ...

EPFL2011

Finding Objects of Interest in Images using Saliency and Superpixels

Radhakrishna Achanta

The ability to automatically find objects of interest in images is useful in the areas of compression, indexing and retrieval, re-targeting, and so on. There are two classes of such algorithms – those that find any object of interest with no prior knowledg ...

EPFL2011

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

Face Detection using Ferns

Sébastien Marcel, Venkatesh Bala Subburaman

This paper discusses the use of ferns (a set of binary features) for face detection. The binary feature used here is the sign of pixel intensity difference. Ferns were first introduced for keypoint recognition and showed good performance, and improving the ...

Idiap2011

Comparing machines and humans on a visual categorization test

François Fleuret, Charles Dubout, Ting Li

Automated scene interpretation has benefited from advances in machine learning, and restricted tasks, such as face detection, have been solved with sufficient accuracy for restricted settings. However, the performance of machines in providing rich semantic ...

2011

Vision-Based Scene Understanding with Sparsity Promoting Priors

Alexandre Massoud Alahi

Human beings are interested in understanding their environments and the dynamic content that fills their surroundings. For applications ranging from security to marketing, people have installed networks of cameras to capture the dynamic elements of scenes. ...

EPFL2011