Thesis (M.A.J.)--Marshall University, 2009. / Title from document title page. Includes abstract. Document formatted into pages: contains v, 71 p. Includes bibliographical references p. 57-60

Thesis (M.Econ.)--University of Hong Kong, 2003. / Includes bibliographical references (leaves 12-13). Also available in print.

Part of the author's dissertation at Ohio State University. / "September 1993." Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

The scope of this project is to present a semi-automated vessels segmentation algorithm, to describe its usage and results. This will be achieved combining several segmentation algorithms to get proper vessel segmentation and visualization. Consequently, automatic segmentation can significantly reduce the scan-to-diagnosis time, thus helping the clinicians to reach the fundamental goal of efficient patient management. In order to complete our project, we can identify different phases: - Correct reading of CT and MR images and extraction of data needed for the post-processing. - Processing of these images using the most appropriate segmentation techniques to get the desired contour. - Visualization of the contour. / -

No description available.

De nos jours, le traitement et l’analyse d’images trouvent leur application dans de nombreux domaines. Dans le cas de la navigation d’un robot mobile (fauteuil roulant) en milieu intérieur, l’extraction de repères visuels et leur suivi constituent une étape importante pour la réalisation de tâches robotiques (localisation, planification, etc.). En particulier, afin de réaliser une tâche de franchissement de portes, il est indispensable de détecter et suivre automatiquement toutes les portes qui existent dans l’environnement. La détection des portes n’est pas une tâche facile : la variation de l’état des portes (ouvertes ou fermées), leur apparence (de même couleur ou de couleur différentes des murs) et leur position par rapport à la caméra influe sur la robustesse du système. D’autre part, des tâches comme la détection des zones navigables ou l’évitement d’obstacles peuvent faire appel à des représentations enrichies par une sémantique adaptée afin d’interpréter le contenu de la scène. Pour cela, les techniques de segmentation permettent d’extraire des régions pseudo-sémantiques de l’image en fonction de plusieurs critères (couleur, gradient, texture…). En ajoutant la dimension temporelle, les régions sont alors suivies à travers des algorithmes de segmentation spatio-temporelle. Dans cette thèse, des contributions répondant aux besoins cités sont présentées. Tout d’abord, une technique de détection et de suivi de portes dans un environnement de type couloir est proposée : basée sur des descripteurs géométriques dédiés, la solution offre de bons résultats. Ensuite, une technique originale de segmentation multirésolution et hiérarchique permet d’extraire une représentation en régions pseudosémantique. Enfin, cette technique est étendue pour les séquences vidéo afin de permettre le suivi des régions à travers le suivi de leurs contours. La qualité des résultats est démontrée et s’applique notamment au cas de vidéos de couloir. / Nowadays, image processing remains a very important step in different fields of applications. In an indoor environment, for a navigation system related to a mobile robot (electrical wheelchair), visual information detection and tracking is crucial to perform robotic tasks (localization, planning…). In particular, when considering passing door task, it is essential to be able to detect and track automatically all the doors that belong to the environment. Door detection is not an obvious task: the variations related to the door status (open or closed), their appearance (e.g. same color as the walls) and their relative position to the camera have influence on the results. On the other hand, tasks such as the detection of navigable areas or obstacle avoidance may involve a dedicated semantic representation to interpret the content of the scene. Segmentation techniques are then used to extract pseudosemantic regions based on several criteria (color, gradient, texture...). When adding the temporal dimension, the regions are tracked then using spatiotemporal segmentation algorithms. In this thesis, we first present joint door detection and tracking technique in a corridor environment: based on dedicated geometrical features, the proposed solution offers interesting results. Then, we present an original joint hierarchical and multiresolution segmentation framework able to extract a pseudo-semantic region representation. Finally, this technique is extended to video sequences to allow the tracking of regions along image sequences. Based on contour motion extraction, this solution has shown relevant results that can be successfully applied to corridor videos.

Cette thèse porte sur les problèmes de segmentation d’objets et la segmentation sémantique qui visent soit à séparer des objets du fond, soit à l’attribution d’une étiquette sémantique spécifique à chaque pixel de l’image. Nous proposons deux approches pour la segmentation d’objets, et une approche pour la segmentation sémantique. La première approche est basée sur la détection de saillance. Motivés par notre but de segmentation d’objets, un nouveau modèle de détection de saillance est proposé. Cette approche se formule dans le modèle de récupération de la matrice de faible rang en exploitant les informations de structure de l’image provenant d’une segmentation ascendante comme contrainte importante. La segmentation construite à l’aide d’un schéma d’optimisation itératif et conjoint, effectue simultanément, d’une part, une segmentation d’objets basée sur la carte de saillance résultant de sa détection et, d’autre part, une amélioration de la qualité de la saillance à l’aide de la segmentation. Une carte de saillance optimale et la segmentation finale sont obtenues après plusieurs itérations. La deuxième approche proposée pour la segmentation d’objets se fonde sur des images exemples. L’idée sous-jacente est de transférer les étiquettes de segmentation d’exemples similaires, globalement et localement, à l’image requête. Pour l’obtention des exemples les mieux assortis, nous proposons une représentation nouvelle de haut niveau de l’image, à savoir le descripteur orienté objet, qui reflète à la fois l’information globale et locale de l’image. Ensuite, un prédicteur discriminant apprend en ligne à l’aide les exemples récupérés pour attribuer à chaque région de l’image requête un score d’appartenance au premier plan. Ensuite, ces scores sont intégrés dans un schéma de segmentation du champ de Markov (MRF) itératif qui minimise l’énergie. La segmentation sémantique se fonde sur une banque de régions et la représentation parcimonieuse. La banque des régions est un ensemble de régions générées par segmentations multi-niveaux. Ceci est motivé par l’observation que certains objets peuvent être capturés à certains niveaux dans une segmentation hiérarchique. Pour la description de la région, nous proposons la méthode de codage parcimonieux qui représente chaque caractéristique locale avec plusieurs vecteurs de base du dictionnaire visuel appris, et décrit toutes les caractéristiques locales d’une région par un seul histogramme parcimonieux. Une machine à support de vecteurs (SVM) avec apprentissage de noyaux multiple est utilisée pour l’inférence sémantique. Les approches proposées sont largement évaluées sur plusieurs ensembles de données. Des expériences montrent que les approches proposées surpassent les méthodes de l’état de l’art. Ainsi, par rapport au meilleur résultat de la littérature, l’approche proposée de segmentation d’objets améliore la mesure d F-score de 63% à 68,7% sur l’ensemble de données Pascal VOC 2011. / This thesis focuses on the problems of object segmentation and semantic segmentation which aim at separating objects from background or assigning a specific semantic label to each pixel in an image. We propose two approaches for the object segmentation and one approach for semantic segmentation. The first proposed approach for object segmentation is based on saliency detection. Motivated by our ultimate goal for object segmentation, a novel saliency detection model is proposed. This model is formulated in the low-rank matrix recovery model by taking the information of image structure derived from bottom-up segmentation as an important constraint. The object segmentation is built in an iterative and mutual optimization framework, which simultaneously performs object segmentation based on the saliency map resulting from saliency detection, and saliency quality boosting based on the segmentation. The optimal saliency map and the final segmentation are achieved after several iterations. The second proposed approach for object segmentation is based on exemplar images. The underlying idea is to transfer segmentation labels of globally and locally similar exemplar images to the query image. For the purpose of finding the most matching exemplars, we propose a novel high-level image representation method called object-oriented descriptor, which captures both global and local information of image. Then, a discriminative predictor is learned online by using the retrieved exemplars. This predictor assigns a probabilistic score of foreground to each region of the query image. After that, the predicted scores are integrated into the segmentation scheme of Markov random field (MRF) energy optimization. Iteratively finding minimum energy of MRF leads the final segmentation. For semantic segmentation, we propose an approach based on region bank and sparse coding. Region bank is a set of regions generated by multi-level segmentations. This is motivated by the observation that some objects might be captured at certain levels in a hierarchical segmentation. For region description, we propose sparse coding method which represents each local feature descriptor with several basic vectors in the learned visual dictionary, and describes all local feature descriptors within a region by a single sparse histogram. With the sparse representation, support vector machine with multiple kernel learning is employed for semantic inference. The proposed approaches have been extensively evaluated on several challenging and widely used datasets. Experiments demonstrated the proposed approaches outperform the stateofthe- art methods. Such as, compared to the best result in the literature, the proposed object segmentation approach based on exemplar images improves the F-score from 63% to 68.7% on Pascal VOC 2011 dataset.

Image segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...

MCOM / Department of Business Management / Increasingly, third-world countries are relying on ecotourism to boost their economies. Similar to other forms of tourism, ecotourism as a business initiative is perceived to contribute positively to economic development. However, tourism service providers generally do not deliver satisfactory services to ecotourists due to misunderstanding the segment’s needs and the customer traits. In emerging countries such as South Africa, which has a conducive economic environment for tourism businesses, some provincial departments such as the Limpopo Department of Economic Development, Environment and Tourism has set objectives to make the Limpopo province a preferred ecotourism destination. These have been propagated by service providers who passively participate in ecotourism yet claim to be ecotourism service providers. Moreover, the attitude of passiveness by service providers is derived from insufficient knowledge of the ecotourist. This set the foundation for the current study, which sought to provide detailed profiles of ecotourists in the Capricorn District Municipality in order to gain a clear understanding of the ecotourists visiting the Limpopo province. The study reviewed theoretical and empirical works conducted by tourism scholars. The study was developed on a descriptive research design and employed a quantitative approach. It made use of self-administered questionnaires with a sample of 295 participants selected from tourism establishments and attractions in the region, utilising convenience and purposive sampling methods respectively. The Statistical Package for Social Sciences version 24 and Microsoft Office Excel was used to analyse the data. Descriptive statistics, factor analysis, cluster analysis and Chi-square tests were conducted to analyse the data of the study. The findings revealed demographic, psychographic and behavioural descriptors associated with the ecotourists in the Capricorn District Municipality. In addition, information sources preferred by ecotourists in the Capricorn District Municipality were identified. It is envisaged that the findings of the study will be of value to ecotourism service providers as they will provide a sound understanding of ecotourists and thereby help to deliver satisfactory ecotourist experiences. This will ultimately provide valuable input for the planning of the Limpopo Department of Economic Development, Environment and Tourism in promoting the Limpopo province as a preferred ecotourism destination in South Africa.