Omnidirectional Vision Based Topological Navigation

Goedemé T., Van Gool L., ''Omnidirectional vision based topological navigation'', Mobile robots navigation, pp. 172-196, Barrera Alejandra, ed., March 2010, InTech.status: publishe

Cluster analysis of the signal curves in perfusion DCE-MRI datasets

Pathological studies show that tumors consist of different sub-regions with more homogeneous vascular properties during their growth. In addition, destroying tumor's blood supply is the target of most cancer therapies. Finding the sub-regions in the tissue of interest with similar perfusion patterns provides us with valuable information about tissue structure and angiogenesis. This information on cancer therapy, for example, can be used in monitoring the response of the cancer treatment to the drug. Cluster analysis of perfusion curves assays to find sub-regions with a similar perfusion pattern. The present work focuses on the cluster analysis of perfusion curves, measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The study, besides searching for the proper clustering method, follows two other major topics, the choice of an appropriate similarity measure, and determining the number of clusters. These three subjects are connected to each other in such a way that success in one direction will help solving the other problems. This work introduces a new similarity measure, parallelism measure (PM), for comparing the parallelism in the washout phase of the signal curves. Most of the previous works used the Euclidean distance as the measure of dissimilarity. However, the Euclidean distance does not take the patterns of the signal curves into account and therefore for comparing the signal curves is not sufficient. To combine the advantages of both measures a two-steps clustering is developed. The two-steps clustering uses two different similarity measures, the introduced PM measure and Euclidean distance in two consecutive steps. The results of two-steps clustering are compared with the results of other clustering methods. The two-steps clustering besides good performance has some other advantages. The granularity and the number of clusters are controlled by thresholds defined by considering the noise in signal curves. The method is easy to implement and is robust against noise. The focus of the work is mainly the cluster analysis of breast tumors in DCE-MRI datasets. The possibility to adopt the method for liver datasets is studied as well

MULTIPLE STRUCTURE RECOVERY VIA PREFERENCE ANALYSIS IN CONCEPTUAL SPACE

Finding multiple models (or structures) that fit data corrupted by noise and outliers is an omnipresent problem in empirical sciences, includingComputer Vision, where organizing unstructured visual data in higher level geometric structures is a necessary and basic step to derive better descriptions and understanding of a scene. This challenging problem has a chicken-and-egg pattern: in order to estimate models one needs to first segment the data, and in order to segment the data it is necessary to know which structure points belong to. Most of the multi-model fitting techniques proposed in the literature can be divided in two classes, according to which horn of the chicken-egg-dilemma is addressed first, namely consensus and preference analysis. Consensus-based methods put the emphasis on the estimation part of the problem and focus on models that describe has many points as possible. On the other side, preference analysis concentrates on the segmentation side in order to find a proper partition of the data, from which model estimation follows. The research conducted in this thesis attempts to provide theoretical footing to the preference approach and to elaborate it in term of performances and robustness. In particular, we derive a conceptual space in which preference analysis is robustly performed thanks to three different formulations of multiple structures recovery, i.e. linkage clustering, spectral analysis and set coverage. In this way we are able to propose new and effective strategies to link together consensus and preferences based criteria to overcome the limitation of both. In order to validate our researches, we have applied our methodologies to some significant Computer Vision tasks including: geometric primitive fitting (e.g. line fitting; circle fitting; 3D plane fitting), multi-body segmentation, plane segmentation, and video motion segmentation

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Model driven segmentation and the detection of bone fractures

Bibliography: leaves 83-90.The introduction of lower dosage image acquisition devices and the increase in computational power means that there is an increased focus on producing diagnostic aids for the medical trauma environment. The focus of this research is to explore whether geometric criteria can be used to detect bone fractures from Computed Tomography data. Conventional image processing of CT data is aimed at the production of simple iso-surfaces for surgical planning or diagnosis - such methods are not suitable for the automated detection of fractures. Our hypothesis is that through a model-based technique a triangulated surface representing the bone can be speedily and accurately produced. And, that there is sufficient structural information present that by examining the geometric structure of this representation we can accurately detect bone fractures. In this dissertation we describe the algorithms and framework that we built to facilitate the detection of bone fractures and evaluate the validity of our approach

A perceptual learning model to discover the hierarchical latent structure of image collections

Biology has been an unparalleled source of inspiration for the work of researchers in several scientific and engineering fields including computer vision. The starting point of this thesis is the neurophysiological properties of the human early visual system, in particular, the cortical mechanism that mediates learning by exploiting information about stimuli repetition. Repetition has long been considered a fundamental correlate of skill acquisition andmemory formation in biological aswell as computational learning models. However, recent studies have shown that biological neural networks have differentways of exploiting repetition in forming memory maps. The thesis focuses on a perceptual learning mechanism called repetition suppression, which exploits the temporal distribution of neural activations to drive an efficient neural allocation for a set of stimuli. This explores the neurophysiological hypothesis that repetition suppression serves as an unsupervised perceptual learning mechanism that can drive efficient memory formation by reducing the overall size of stimuli representation while strengthening the responses of the most selective neurons. This interpretation of repetition is different from its traditional role in computational learning models mainly to induce convergence and reach training stability, without using this information to provide focus for the neural representations of the data. The first part of the thesis introduces a novel computational model with repetition suppression, which forms an unsupervised competitive systemtermed CoRe, for Competitive Repetition-suppression learning. The model is applied to generalproblems in the fields of computational intelligence and machine learning. Particular emphasis is placed on validating the model as an effective tool for the unsupervised exploration of bio-medical data. In particular, it is shown that the repetition suppression mechanism efficiently addresses the issues of automatically estimating the number of clusters within the data, as well as filtering noise and irrelevant input components in highly dimensional data, e.g. gene expression levels from DNA Microarrays. The CoRe model produces relevance estimates for the each covariate which is useful, for instance, to discover the best discriminating bio-markers. The description of the model includes a theoretical analysis using Huber’s robust statistics to show that the model is robust to outliers and noise in the data. The convergence properties of themodel also studied. It is shown that, besides its biological underpinning, the CoRe model has useful properties in terms of asymptotic behavior. By exploiting a kernel-based formulation for the CoRe learning error, a theoretically sound motivation is provided for the model’s ability to avoid local minima of its loss function. To do this a necessary and sufficient condition for global error minimization in vector quantization is generalized by extending it to distance metrics in generic Hilbert spaces. This leads to the derivation of a family of kernel-based algorithms that address the local minima issue of unsupervised vector quantization in a principled way. The experimental results show that the algorithm can achieve a consistent performance gain compared with state-of-the-art learning vector quantizers, while retaining a lower computational complexity (linear with respect to the dataset size). Bridging the gap between the low level representation of the visual content and the underlying high-level semantics is a major research issue of current interest. The second part of the thesis focuses on this problem by introducing a hierarchical and multi-resolution approach to visual content understanding. On a spatial level, CoRe learning is used to pool together the local visual patches by organizing them into perceptually meaningful intermediate structures. On the semantical level, it provides an extension of the probabilistic Latent Semantic Analysis (pLSA) model that allows discovery and organization of the visual topics into a hierarchy of aspects. The proposed hierarchical pLSA model is shown to effectively address the unsupervised discovery of relevant visual classes from pictorial collections, at the same time learning to segment the image regions containing the discovered classes. Furthermore, by drawing on a recent pLSA-based image annotation system, the hierarchical pLSA model is extended to process and representmulti-modal collections comprising textual and visual data. The results of the experimental evaluation show that the proposed model learns to attach textual labels (available only at the level of the whole image) to the discovered image regions, while increasing the precision/ recall performance with respect to flat, pLSA annotation model