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Computer Vision
Awards
Kay H. Brodersen received a Trainee Abstract Award at HBM 2012 for his work on 'Model-Based Clustering Using Generative Embedding.' Kay will be giving a talk on his results in Beijing on 12 June.
François Cellier received the McLeod Founder's Award of the Society for Modeling and Simulation International.
Spotlight
Computational models of brain connectivity, coupled with machine learning algorithms, make it possible to infer neuronal disease mechanisms from
non-invasive functional magnetic resonance imaging (fMRI) data in humans.
This illustration shows how dynamic systems models can be used for reducing
complex (high-dimensional) brain activity data to a simple (low-dimensional)
and mechanistically interpretable representation (Brodersen et al., PLoS
Comput. Biol. 2011). Please also see the summary on ETH Life.
Introduction
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Recent advances in image and video capture have enabled very cheap acquisition
of image data. This calls for automatic tools for analysis, categorization and
interpretation. Such challenges in the field of computational vision present an
ideal test bed for pattern recognition and machine learning algorithms. We apply
new sampling techniques to image segmentation, investigating the different
approaches for efficient large scale inference. Graphical models are used to
learn object categorization from weakly labeled data, providing a framework for
integrating multiple sources of information. We use psychophysics to investigate
object detection in humans and we apply these techniques to real world problems,
such as the analysis of medical and neuroanatomical data.
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Projects
Discriminative methods for structured data
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In this project we consider discriminative models for problems that have
an underlying structure, i.e. we overcome the traditional independence
assumption present in many discriminative models. In our setting the
structure is specified by a graphical model and might correspond to a
grid for image segmentation in computer vision or a complete graph for
multilabel classification. One important research focus is approximate
training of these models, which is needed as these models are
computationally intractable in general. Furthermore, we are interested
in applications to vision problems.
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Conditional Random Fields allow for a principled, probabilistic
discriminative reasoning about structured data. We are investigating
applications in computer vision like multilabel categorization (top) or
training segmentations of photos into semantic classes (bottom).
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Funding source: SNF
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Utilizing multiple data sources by information fusion
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This project is dedicated to integrating approximate geometry inference
and object segmentation and recognition. The main challenges lie in
constructing a feature transform that is adequate to both tasks and
devising a learning algorithm that can exploit the relation of tasks
that are different, but defined on same domain.
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Funding source: SNF
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