95 research outputs found
Statistical shape analysis for bio-structures : local shape modelling, techniques and applications
A Statistical Shape Model (SSM) is a statistical representation of a shape obtained
from data to study variation in shapes. Work on shape modelling is constrained by
many unsolved problems, for instance, difficulties in modelling local versus global
variation. SSM have been successfully applied in medical image applications such
as the analysis of brain anatomy. Since brain structure is so complex and varies
across subjects, methods to identify morphological variability can be useful for
diagnosis and treatment.
The main objective of this research is to generate and develop a statistical shape
model to analyse local variation in shapes. Within this particular context, this
work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point
Distribution Model and uses a combination of other well known techniques: Fractal
analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space
representation for the problem of contour localisation. Similarly, Diffusion Maps
are employed as a spectral shape clustering tool to identify sets of local partitions
useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis
method based on the Gaussian and Laplacian pyramids is explained and used to
compare the featured Local Shape Model.
Experimental results on a number of real contours such as animal, leaf and brain
white matter outlines have been shown to demonstrate the effectiveness of the
proposed model. These results show that local shape models are efficient in modelling
the statistical variation of shape of biological structures. Particularly, the
development of this model provides an approach to the analysis of brain images
and brain morphometrics. Likewise, the model can be adapted to the problem of
content based image retrieval, where global and local shape similarity needs to be
measured
Patch-based semantic labelling of images.
PhDThe work presented in this thesis is focused at associating a semantics
to the content of an image, linking the content to high level
semantic categories. The process can take place at two levels: either
at image level, towards image categorisation, or at pixel level, in se-
mantic segmentation or semantic labelling. To this end, an analysis
framework is proposed, and the different steps of part (or patch) extraction,
description and probabilistic modelling are detailed. Parts of
different nature are used, and one of the contributions is a method to
complement information associated to them. Context for parts has to
be considered at different scales. Short range pixel dependences are accounted
by associating pixels to larger patches. A Conditional Random
Field, that is, a probabilistic discriminative graphical model, is used
to model medium range dependences between neighbouring patches.
Another contribution is an efficient method to consider rich neighbourhoods
without having loops in the inference graph. To this end, weak
neighbours are introduced, that is, neighbours whose label probability
distribution is pre-estimated rather than mutable during the inference.
Longer range dependences, that tend to make the inference problem
intractable, are addressed as well. A novel descriptor based on local
histograms of visual words has been proposed, meant to both complement
the feature descriptor of the patches and augment the context
awareness in the patch labelling process. Finally, an alternative approach
to consider multiple scales in a hierarchical framework based
on image pyramids is proposed. An image pyramid is a compositional
representation of the image based on hierarchical clustering. All the
presented contributions are extensively detailed throughout the thesis,
and experimental results performed on publicly available datasets are
reported to assess their validity. A critical comparison with the state
of the art in this research area is also presented, and the advantage in
adopting the proposed improvements are clearly highlighted
Connected Attribute Filtering Based on Contour Smoothness
A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform
Video Sequence Alignment
The task of aligning multiple audio visual sequences with similar contents needs careful synchronisation in both spatial and temporal domains. It is a challenging task due to a broad range of contents variations, background clutter, occlusions, and other factors. This thesis is concerned with aligning video contents by characterising the spatial and temporal information embedded in the high-dimensional space. To that end a three- stage framework is developed, involving space-time representation of video clips with local linear coding, followed by their alignment in the manifold embedded space. The first two stages present a video representation techniques based on local feature extraction and linear coding methods. Firstly, the scale invariant feature transform (SIFT) is extended to extract interest points not only from the spatial plane but also from the planes along the space-time axis. Locality constrained coding is then incorporated to project each descriptor into a local coordinate system produced by a pooling technique. Human action classification benchmarks are adopted to evaluate these two stages, comparing their performance against existing techniques. The results shows that space-time extension of SIFT with a linear coding scheme outperforms most of the state-of-the-art approaches on the action classification task owing to its ability to represent complex events in video sequences.
The final stage presents a manifold learning algorithm with spatio-temporal constraints to embed a video clip in a lower dimensional space while preserving the intrinsic geometry of the data. The similarities observed between frame sequences are captured by defining two types of correlation graphs: an intra-correlation graph within a single video sequence and an inter-correlation graph between two sequences. A video retrieval and ranking tasks are designed to evaluate the manifold learning stage. The experimental outcome shows that the approach outperforms the conventional techniques in defining similar video contents and capture the spatio-temporal correlations between them
Statistical shape analysis for bio-structures : local shape modelling, techniques and applications
A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured.EThOS - Electronic Theses Online ServiceConsejo Nacional de Ciencia y Tecnología (Mexico) (CONACYT)GBUnited Kingdo
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