9 research outputs found
Texture and Colour in Image Analysis
Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews
Hyperspectral image representation and processing with binary partition trees
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed
according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representatio
Superpixel lattices
Superpixels are small image segments that are used in popular approaches to object
detection and recognition problems. The superpixel approach is motivated by the observation
that pixels within small image segments can usually be attributed the same
label. This allows a superpixel representation to produce discriminative features based
on data dependent regions of support. The reduced set of image primitives produced
by superpixels can also be exploited to improve the efficiency of subsequent processing
steps. However, it is common for the superpixel representation to have a different graph
structure from the original pixel representation of the image.
The first part of the thesis argues that a number of desirable properties of the
pixel representation should be maintained by superpixels and that this is not possible
with existing methods. We propose a new representation, the superpixel lattice, and
demonstrate its advantages.
The second part of the thesis investigates incorporating a priori information into
superpixel segmentations. We learn a probabilistic model that describes the spatial
density of object boundaries in the image. We demonstrate our approach using road
scene data and show that our algorithm successfully exploits the spatial distribution of
object boundaries to improve the superpixel segmentation.
The third part of the thesis presents a globally optimal solution to our superpixel
lattice problem in either the horizontal or vertical direction. The solution makes use of
a Markov Random Field formulation where the label field is guaranteed to be a set of
ordered layers. We introduce an iterative algorithm that uses this framework to learn
colour distributions across an image in an unsupervised manner.
We conclude that our approach achieves comparable or better performance than
competing methods and that it confers several additional advantages
Statistical analysis for longitudinal MR imaging of dementia
Serial Magnetic Resonance (MR) Imaging can reveal structural atrophy in the brains of
subjects with neurodegenerative diseases such as Alzheimer’s Disease (AD). Methods of
computational neuroanatomy allow the detection of statistically significant patterns of
brain change over time and/or over multiple subjects. The focus of this thesis is the
development and application of statistical and supporting methodology for the analysis
of three-dimensional brain imaging data. There is a particular emphasis on longitudinal
data, though much of the statistical methodology is more general.
New methods of voxel-based morphometry (VBM) are developed for serial MR data,
employing combinations of tissue segmentation and longitudinal non-rigid registration.
The methods are evaluated using novel quantitative metrics based on simulated data.
Contributions to general aspects of VBM are also made, and include a publication concerning
guidelines for reporting VBM studies, and another examining an issue in the
selection of which voxels to include in the statistical analysis mask for VBM of atrophic
conditions.
Research is carried out into the statistical theory of permutation testing for application
to multivariate general linear models, and is then used to build software for the analysis
of multivariate deformation- and tensor-based morphometry data, efficiently correcting
for the multiple comparison problem inherent in voxel-wise analysis of images. Monte
Carlo simulation studies extend results available in the literature regarding the different
strategies available for permutation testing in the presence of confounds.
Theoretical aspects of longitudinal deformation- and tensor-based morphometry are
explored, such as the options for combining within- and between-subject deformation
fields. Practical investigation of several different methods and variants is performed for a
longitudinal AD study
Unmet goals of tracking: within-track heterogeneity of students' expectations for
Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality
Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Book
Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Boo