Research
My research interests are in computer vision, image analysis, machine learning, and multimodal perception. I approach these problems with methods from Bayesian statistics, signal processing, and applied mathematics. Specific projects I have worked on include:
Perturb-and-MAP Random Fields: Reducing Random Sampling to Optimization
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We have been developing a new Perturb-and-MAP framework for one-shot random sampling in
Gaussian or discete-label Markov random fields (MRF).
With Perturb-and-MAP random fields we turn
powerful deterministic energy minimization methods into efficient random
sampling algorithms. By avoiding costly MCMC, we can generate in a
fraction of a second independent random samples from million-node
networks. Applications include model parameter estimation and solution
uncertainty quantification in computer vision applications. |
Bayesian Inference in Large-Scale Sparse Models: Efficient Monte-Carlo and Variational Approaches
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We study linear models under heavy-tailed priors from a probabilistic
viewpoint. Instead of computing a single sparse most probable (MAP) solution
as in standard deterministic techniques, the focus in the Bayesian compressed
sensing framework shifts towards capturing the full posterior distribution on
the latent variables. This allows quantifying the estimation uncertainty and
learning model parameters using maximum likelihood. The exact posterior
distribution under the sparse linear model is intractable and we propose both
Monte-Carlo and variational Bayesian methods to approximate it. Efficient
Gaussian sampling by local perturbations turns out to be a key computational
module that allows both of these classes of algorithms to handle large-scale
datasets with essentially the same memory and time complexity requirements as
conventional MAP estimation techniques. We experimentally demonstrate these
ideas in Bayesian total variation (TV) signal estimation, visual receptive
field learning, and blind image deconvolution, among other applications. |
Digital Restoration of Missing Parts in the Pre-historic Wall Paintings of Thera
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We have been working on PDE and wavelet-based techniques for the
digital restoration of missing parts in paintings. This is part of an
ongoing project on the virtual restoration of the 3,600 years old
wall paintings excavated in the pre-historic Aegean settlement in Akrotiri,
Thera, Greece. |
Audio-Visual Speech Recognition
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Audiovisual speech recognition refers to the problem of
recognizing speech by lipreading. We have developed highly
adaptive multimodal fusion rules based on uncertainty
compensation which are compatible with synchronous and
asynchronous multimodal interaction
architectures. Further, our
work on AAM-based face representations leads to
highly informative visual speech features which can be
extracted in real-time. |
Multigrid Geometric Active Contours
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We investigate multigrid techniques for the solution
of the time-dependent PDEs of geometric active contour
models in Computer Vision. The method allows
interactive solution of models whose numerical
implementation with conventional techniques has been
prohibitively slow. |
Audio-Visual Speech Inversion
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We focus on recovering aspects of vocal tract's geometry
and dynamics from speech, a problem referred to as speech
inversion. In our inversion scheme ambiguities inherent to
audio-only inversion are resolved by also exploiting visual
information from the speaker's face. |
Automatic Scale Selection in Nonlinear Scale-Spaces
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We consider optimal scale selection for fully automatic image
denoising in nonlinear diffusion or morphological scale-spaces. The
problem is studied from a statistical model selection viewpoint and
we employ cross-validation statistical techniques to address it in a
principled way. |