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Markovian modeling and image classification

vrijdag, 13 april, 2012 - 16:00
Campus: Brussels Humanities, Sciences & Engineering campus
Antonios Katartzis

Bayesian estimation and Markov processes, like Markov chains or Markov
random Fields (MRFs), constitute a popular branch in statistics that has been
extensively used in the domain of computer vision. The objective of this
dissertation is the exploration of new Markovian models for the development of
image analysis methods in two important fields: (a) perceptual organization for
object identification and (b) pixel- and region-based image classification. The
proposed methodologies have been applied on the domains of remote-sensing
and medical imaging. They are based on a wide range of image analysis
concepts, such as color/multispectral image processing, mathematical
morphology, 3-D image geometry, along with the main principles of graph,
Gestalt and scale-space theory. The dissertation is organized in three thematic

The First part provides a general introduction to the concepts of Bayesian
estimation and Markov processes, emphasizing their potential in solving inverse
problems in computer vision. This introductory part includes an overview of the
basic Markovian models associated with both 1-D and higher dimensional
processes, along with a description of the commonly used methodologies for
Bayesian inference and parameter estimation.

The second part highlights the potential of MRF-theory in the case of highlevel
vision applications and in particular, those of 2-D and 3-D object
detection/recognition. The presented methodologies are based on the principles
of perceptual organization. The generation of object hypotheses is formulated as
an hierarchical grouping of image primitives, using a set of structural constraints
with a successive level of abstraction. These constraints can be efficiently
modeled in the MRF framework, giving rise to a wide variety of schemes for
shape/structure description and recognition. Based on this strategy, we have
developed two methods for the extraction of 2-D and 3-D structural information,
using remote sensing imagery. The ¯rst method refers to the identification of
linear features, like roads and paths but can also be applied to any other image
modality. A second application is the detection of 3-D objects corresponding to
buildings, using a single remote sensing image and minimal domain knowledge,
restricted only to the camera's extrinsic and intrinsic parameters and the time of
image acquisition.

Finally, the third part is concerned with low-level vision applications,
focusing on the issue of image classification. We present an application
dependent, pixel-based classification scheme for the identification of the breast
skin thickness in mammographic images and a more general region-based
approach for supervised and unsupervised image classification. Both approaches
consider a multiscale representation of the image. The first one employs a
wavelet-based observation field, in combination with a non-hierarchical MRF
prior model that describes the geometrical properties of the breast skin region.
The second approach introduces a novel classification framework, in which
multiscale information is incorporated in the labeling process in the form of a
hierarchical Markovian model that explicitly exploits a multiscale image hierarchy.
The novelty of the proposed method is the use of a hierarchical structure, which
has the form of a multiscale region adjacency graph. The latter is generated
using an image adaptive scale-space filter and a linking scheme for relating
image features across the scale dimension.