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This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

We propose a segmentation method based on the geometric representation of images as surfaces embedded in a higher dimensional space, handling naturally multichannel images. The segmentation is based on an active contour embedded in the image manifold, alon ...

The integration of audio and visual information improves speech recognition performance, specially in the presence of noise. In these circumstances it is necessary to introduce audio and visual weights to control the contribution of each modality to the re ...

The level set method is a popular technique for tracking moving interfaces in several disciplines including computer vision and fluid dynamics. However, despite its high flexibility, the original level set method is limited by two important numerical issue ...

In this paper we propose two alternatives to overcome the natural asynchrony of modalities in Audio-Visual Speech Recognition. We first investigate the use of asynchronous statistical models based on Dynamic Bayesian Networks with different levels of async ...

In this article, we study the adaptation of visual and audio-visual speech recognition systems to non-ideal visual conditions. We focus on overcoming the effects of a changing pose of the speaker, a problem encountered in natural situations where the speak ...

Class-specific classifiers for audio, visual and audio-visual speech recognition systems are developed and compared with traditional classifiers. We use state- of-the-art feature extraction methods and show the benefits of a class-specific classifier on ea ...

A quantitative measure of relevance is proposed for the task of constructing visual feature sets which are at the same time relevant and compact. A feature's relevance is given by the amount of information that it contains about the problem, while compactn ...

We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality from few measurements. The image reconstruction is done by iterating the two following steps: 1) estimation of normal vectors of the i ...

Virginia Estellers Casas

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Geometric Variational Models for Inverse Problems in Imaging

Harmonic Active Contours for multichannel image segmentation

On dynamic stream weighting for Audio-Visual Speech Recognition

An Efficient Algorithm for Level Set Method Preserving Distance Function

Overcoming Asynchrony in Audio-Visual Speech Recognition

Multi-pose lipreading and audio-visual speech recognition

Class-specific classifiers in audio-visual speech recognition

Selecting relevant visual features for speechreading

Enhanced Compressed Sensing Recovery with Level Set Normals

Multipose Audio-Visual Speech Recognition