Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario

Visual analytics of multidimensional time-dependent trails:with applications in shape tracking

Lots of data collected for both scientific and non-scientific purposes have similar characteristics: changing over time with many different properties. For example, consider the trajectory of an airplane travelling from one location to the other. Not only does the airplane itself move over time, but its heading, height and speed are changing at the same time. During this research, we investigated different ways to collect and visualze data with these characteristics. One practical application being for an automated milking device which needs to be able to determine the position of a cow's teats. By visualizing all data which is generated during the tracking process we can acquire insights in the working of the tracking system and identify possibilites for improvement which should lead to better recognition of the teats by the machine. Another important result of the research is a method which can be used to efficiently process a large amount of trajectory data and visualize this in a simplified manner. This has lead to a system which can be used to show the movement of all airplanes around the world for a period of multiple weeks

Von Pixeln zu Regionen: Partielle Differentialgleichungen in der Bildanalyse

This work deals with applications of partial differential equations in image analysis. The focus is thereby on applications that can be used for image segmentation. This includes, among other topics, nonlinear diffusion, motion analysis, and image segmentation itself. From each chapter to the next, the methods are directed more and more to image segmentation. While Chapter 2 presents general denoising and simplification techniques, Chapter 4 already addresses the somewhat more special task to extract texture and motion from images. This is in order to employ the resulting features to the partitioning of images finally in Chapter 5. Thus, in this work, one can clearly make out the thread from the raw image data, the pixels, to the more abstract descriptions of images by means of regions. The fact that image processing techniques can also be useful in research areas besides conventional images is shown in Chapter 3. They are used here in order to improve numerical methods for conservation laws in physics. The work conceptually focuses on using as many different features as possible for segmentation. This includes besides image-driven features like texture and motion the knowledge-based information of a three-dimensional object model. The basic idea of this concept is to provide a preferably wide basis of information for separating object regions and thus increasing the number of situations in which the method yields satisfactory segmentation results. A further basic concept pursued in this thesis is to employ coarse-to-fine strategies. They are used both for motion estimation in Chapter 4 and for segmentation in Chapter 5. In both cases one has to deal with optimization problems that contain many local optima. Conventional local optimization therefore usually leads to results the quality of which heavily depends on the initialization. This situation can often be eased, if the optimization problem is first significantly simplified. One then tries to solve the original problem by continuously increasing the problem complexity. Apart from this, the work contains several essential technical novelties. In Chapter 2, nonlinear diffusion with unbounded diffusivities is considered. This also includes total variation flow(TV flow). A thorough analysis of TV flow thereby leads to an analytic solution that allows to show that TV flow is in the space-discrete, one-dimensional setting exactly identical to the corresponding variational approach called TV regularization. Moreover, various different numerical methods are investigated in order to determine their suitability for diffusion filters with unbounded diffusivities. TV flow can be regarded as an alternative to Gaussian smoothing, though there is the significant difference of TV flow being discontinuity preserving. By replacing Gaussian smoothing by TV flow, one can develop new discontinuity preserving versions of well-known operators such as the structure tensor. TV flow is also employed in Chapter 3 where the goal is to improve numerical schemes for the approximation of hyperbolic conservation laws by means of image processing techniques. The role of TV flow in this scope is to remove oscillations of a second order method. In an alternative approach, the approximation performance of a first order method is improved by a nonlinear inverse diffusion filter. The underlying concept is to remove exactly the amount of numerical diffusion that actually stabilizes the scheme. By means of an appropriate stabilization of the inverse diffusion process it is possible to preserve the positive stability properties of the original method. III IV Abstract Chapter 4 is separated into two parts. The first part deals with the extraction of texture features, whereas the second part focuses on motion estimation. Goal of the texture extraction method is to derive a feature space that is as low-dimensional as possible but still provides very good discrimination properties. The basic framework of this feature space is the structure tensor based on TV flow presented earlier in Chapter 2. It contains the orientation, magnitude, and homogeneity of a texture and therefore provides already very important features for texture discrimination. Additionally, a region based local scale measure is developed that supplements the size of texture elements to the feature space. This feature space is used later in Chapter 5 for texture segmentation. Two motion estimation methods are introduced in Chapter 4. One of them is based on the structure tensor from Section 2 and improves existing local methods. The other technique is based on a global variational approach. It differs from usual variational approaches by the use of a gradient constancy assumption. This assumption provides the method with the capability to yield good estimation results even in the presence of small local or global variations of illumination. Besides this novelty, the combination of non-linearized constancy assumptions and a coarse-to-fine strategy yields a numerical scheme that provides for the first time a well founded theory for the very successful warping methods. The described technique leads to results that are generally more accurate than all results presented in literature so far. As already mentioned, goal of the image segmentation approach in Chapter 5 is mainly to integrate the features derived in Chapter 4 and to utilize a coarse-to-fine strategy. This is done in the framework of region based, implicit active contour models which are set up on the concept of level sets. The involved region models are extended by nonparametric as well as local region statistics. A further novelty is the extension of the level set concept to multiple regions. The optimum number of regions is thereby estimated by a hierarchical approach. This is a considerable extension of conventional active contour models, which are usually restricted to two regions. Moreover, the idea to use three-dimensional object knowledge for segmentation is presented. The proposed method uses the extracted contour for estimating the pose of the object, while in return the projected object model supports the segmentation. The implementation of this idea as described in this thesis is only at an early stage. Plenty of interesting aspects can be derived from this concept that are to be investigated in the future.Die vorliegenden Arbeit beschäftigt sich mit Anwendungen partieller Differentialgleichungen in der Bildanalyse. Dabei stehen Anwendungen im Vordergrund, die sich zur Bildsegmentierung verwenden lassen. Dies schließt unter anderem nichtlineare Diffusion, Bewegungsschätzung und die Bildsegmentierung selbst ein. Von Kapitel zu Kapitel werden die verwendeten Methoden dabei mehr und mehr auf die Bildsegmentierung ausgerichtet. Werden in Kapitel 2 noch allgemeine Entrauschungs- und Bildvereinfachungsoperationen vorgestellt, behandelt Kapitel 4 die schon etwas speziellere Aufgabe, Textur und Bewegung aus Bildern zu extrahieren, um entsprechende Merkmale schließlich in Kapitel 5 zur Segmentierung von Bildern verwenden zu können. Dabei zieht sich der Weg von den rohen Bilddaten, den Pixeln, hin zur abstrakteren Beschreibung von Bildern mit Hilfe von Regionen als roter Faden durch die gesamte Arbeit. Dass sich Bildverarbeitungstechniken auch in Forschungsgebieten fern herkömmlicher Bilder als nützlich erweisen können, zeigt Kapitel 3. Hier werden Bildverarbeitungstechniken zur Verbesserung numerischer Verfahren für Erhaltungsgleichungen der Physik verwendet. Konzeptionell legt diese Arbeit Wert darauf, möglichst viele verschiedene Merkmale zur Segmentierung zu verwenden. Darunter fallen neben den bildgestützten Merkmalen wie Textur und Bewegung auch die wissensbasierte Information eines dreidimensionalen Oberflächenmodells. Die prinzipielle Idee hinter diesem Konzept ist, die Entscheidungsgrundlage zur Trennung von Objektregionen auf eine möglichst breite Informationsbasis zu stellen und somit die Anzahl der Situationen, in denen das Verfahren zufriedenstellende Segmentierungsergebnisse liefert, zu erhöhen. Ein weiteres Grundkonzept, das in dieser Arbeit verfolgt wird, ist die Verwendung von Coarse- To-Fine-Strategien. Sie kommen sowohl bei der Bewegungsschätzung in Kapitel 4 als auch in der Segmentierung in Kapitel 5 zum Einsatz. In beiden Fällen hat man es mit Optimierungsproblemen zu tun, die viele lokale Optima aufweisen. Herkömmliche lokale Optimierung führt daher meist zu Ergebnissen, deren Qualität stark von der Initialisierung abhängt. Diese Situation lässt sich häufig entschärfen, wenn man das entsprechende Optimierungsproblem zunächst deutlich vereinfacht und erst nach und nach das ursprüngliche Problem zu lösen versucht. Daneben enthält diese Arbeit viele wesentliche technische Neuerungen. In Kapitel 2 wird nichtlineare Diffusion mit unbeschränkten Diffusivitäten betrachtet, was auch Total-Variation- Flow (TV-Flow) mit einschließt. Eine genaue Analyse von TV-Flow führt dabei zu einer analytischen Lösung, mit Hilfe derer man zeigen kann, dass TV-Flow im diskreten, eindimensionalen Fall exakt identisch mit dem ensprechenden Variationsansatz der TV-Regularisierung ist. Desweiteren werden verschiedene numerische Verfahren in Bezug auf ihre Eignung für Diffusionsfilter mit unbeschränkten Diffusivitäten untersucht. Man kann TV-Flow als eine Alternative zur Gaußglättung ansehen, mit dem entscheidenden Unterschied, dass TV-Flow kantenerhaltend ist. Durch Ersetzen von Gaußglättung durch TV-Flow lassen sich so diskontinuitätserhaltende Varianten bekannter Operatoren wie etwa des Strukturtensors entwickeln. Auch in Kapitel 3 kommt TV-Flow zum Einsatz, wenn es darum geht, numerische Verfahren zur Approximation hyperbolischer Erhaltungsgleichungen durch Bildverarbeitungsmethoden zu verbessern. TV-Flow fällt dabei die Rolle zu, Oszillationen eines Verfahrens zweiter Ordnung zu beseitigen. In einem alternativen Ansatz werden die Approximationseigenschaften eines Verfahrens erster Ordnung durch einen nichtlinearen Rückwärtsdiffusionsfilter verbessert, indem die numerische Diffusion, die das Verfahren eigentlich stabilisiert, gezielt wieder entfernt wird. Dabei gelingt es durch eine geeignete Stabilisierung der Rückwärtsdiffusion, die positiven Stabilitätseigenschaften des Originalverfahrens zu erhalten. Kapitel 4 spaltet sich in zwei Teile auf, wobei der erste Teil von der Extrahierung von Texturmerkmalen handelt, während sich der zweite Teil auf Bewegungsschätzung konzentriert. Bei den Texturmerkmalen besteht dabei das Ziel, einen möglichst niederdimensionalen Merkmalsraum zu kreieren, der dennoch sehr gute Diskriminierungseigenschaften besitzt. Das Grundgerüst dieses Merkmalsraums stellt dabei der in Kapitel 2 vorgestellte, auf TV-Flow basierende Strukturtensor dar. Er beschreibt mit der Orientierung, Stärke und Homogenität der Texturierung bereits sehr wichtige Merkmale einer Textur. Daneben wird ein regionenbasiertes, lokales Skalenmaß entwickelt, das zusätzlich die Größe von Texturelementen als Merkmal einbringt. Diese Texturmerkmale werden später in Kapitel 5 zur Textursegmentierung verwendet. Zur Bewegungsschätzung werden zwei Verfahren vorgestellt. Das eine basiert auf dem in Kapitel 2 eingeführten Strukturtensor und stellt eine Verbesserung vorhandener lokaler Methoden dar. Das andere Verfahren basiert auf einem globalen Variationsansatz und unterscheidet sich von üblichen Variationsansätzen durch die Verwendung einer Gradientenkonstanzannahme. Diese stattet das Verfahren mit der Fähigkeit aus, auch beim Vorhandensein kleinerer lokaler oder globaler Helligkeitsschwankungen gute Schätzergebnisse zu liefern. Daneben ergibt sich aus der Kombination von nicht-linearisierten Konstanzannahmen und einer Coarse-To-Fine-Strategie ein numerisches Schema, das erstmals eine fundierte Theorie zu den sehr erfolgreichen Warping-Verfahren zur Verfügung stellt. Mit der beschriebenen Technik werden Ergebnisse erzielt, die grundsätzlich präziser sind als alles was bisher in der Literatur vorgestellt wurde. Bei der eigentlichen Bildsegmentierung in Kapitel 5 geht es schließlich, wie bereits erwähnt, hauptsächlich um die Einbringung der in Kapitel 4 entwickelten zusätzlichen Merkmale und um die Verwendung einer Coarse-To-Fine-Strategie. Dies geschieht im Rahmen von regionenbasierten, impliziten Aktiv-Kontur-Modellen, die auf dem Konzept der Level-Sets aufbauen. Dabei werden die Regionenmodelle um nichtparametrische und lokale Beschreibungen der Regionenstatistik erweitert. Eine weitere Neuerung ist die Erweiterung des Level-Set-Konzepts auf mehrere Regionen. In einem teils hierarchischen Ansatz wird dabei auch die optimale Anzahl der Regionen geschätzt, was eine erhebliche Erweiterung im Vergleich zu herkömmlichen Aktiv-Kontur- Modellen darstellt. Außerdem wird die Idee vorgestellt, dreidimensionales Objektwissen in der Segmentierung zu verwenden, indem anhand der Segmentierung die Lage des Objekts geschätzt wird und umgekehrt wiederum das projizierte Objektmodell die Segmentierung unterstützt. Die Umsetzung dieser Idee, wie sie in dieser Arbeit beschrieben wird, steht dabei erst am Anfang. Für die Zukunft ergeben sich hieraus noch viele interessanter Aspekte, die es zu untersuchen gilt

Segmentation and Characterization of Small Retinal Vessels in Fundus Images Using the Tensor Voting Approach

RÉSUMÉ La rétine permet de visualiser facilement une partie du réseau vasculaire humain. Elle offre ainsi un aperçu direct sur le développement et le résultat de certaines maladies liées au réseau vasculaire dans son entier. Chaque complication visible sur la rétine peut avoir un impact sur la capacité visuelle du patient. Les plus petits vaisseaux sanguins sont parmi les premières structures anatomiques affectées par la progression d’une maladie, être capable de les analyser est donc crucial. Les changements dans l’état, l’aspect, la morphologie, la fonctionnalité, ou même la croissance des petits vaisseaux indiquent la gravité des maladies. Le diabète est une maladie métabolique qui affecte des millions de personnes autour du monde. Cette maladie affecte le taux de glucose dans le sang et cause des changements pathologiques dans différents organes du corps humain. La rétinopathie diabétique décrit l’en- semble des conditions et conséquences du diabète au niveau de la rétine. Les petits vaisseaux jouent un rôle dans le déclenchement, le développement et les conséquences de la rétinopa- thie. Dans les dernières étapes de cette maladie, la croissance des nouveaux petits vaisseaux, appelée néovascularisation, présente un risque important de provoquer la cécité. Il est donc crucial de détecter tous les changements qui ont lieu dans les petits vaisseaux de la rétine dans le but de caractériser les vaisseaux sains et les vaisseaux anormaux. La caractérisation en elle-même peut faciliter la détection locale d’une rétinopathie spécifique. La segmentation automatique des structures anatomiques comme le réseau vasculaire est une étape cruciale. Ces informations peuvent être fournies à un médecin pour qu’elles soient considérées lors de son diagnostic. Dans les systèmes automatiques d’aide au diagnostic, le rôle des petits vaisseaux est significatif. Ne pas réussir à les détecter automatiquement peut conduire à une sur-segmentation du taux de faux positifs des lésions rouges dans les étapes ultérieures. Les efforts de recherche se sont concentrés jusqu’à présent sur la localisation précise des vaisseaux de taille moyenne. Les modèles existants ont beaucoup plus de difficultés à extraire les petits vaisseaux sanguins. Les modèles existants ne sont pas robustes à la grande variance d’apparence des vaisseaux ainsi qu’à l’interférence avec l’arrière-plan. Les modèles de la littérature existante supposent une forme générale qui n’est pas suffisante pour s’adapter à la largeur étroite et la courbure qui caractérisent les petits vaisseaux sanguins. De plus, le contraste avec l’arrière-plan dans les régions des petits vaisseaux est très faible. Les méthodes de segmentation ou de suivi produisent des résultats fragmentés ou discontinus. Par ailleurs, la segmentation des petits vaisseaux est généralement faite aux dépends de l’amplification du bruit. Les modèles déformables sont inadéquats pour segmenter les petits vaisseaux. Les forces utilisées ne sont pas assez flexibles pour compenser le faible contraste, la largeur, et vii la variance des vaisseaux. Enfin, les approches de type apprentissage machine nécessitent un entraînement avec une base de données étiquetée. Il est très difficile d’obtenir ces bases de données dans le cas des petits vaisseaux. Cette thèse étend les travaux de recherche antérieurs en fournissant une nouvelle mé- thode de segmentation des petits vaisseaux rétiniens. La détection de ligne à échelles multiples (MSLD) est une méthode récente qui démontre une bonne performance de segmentation dans les images de la rétine, tandis que le vote tensoriel est une méthode proposée pour reconnecter les pixels. Une approche combinant un algorithme de détection de ligne et de vote tensoriel est proposée. L’application des détecteurs de lignes a prouvé son efficacité à segmenter les vais- seaux de tailles moyennes. De plus, les approches d’organisation perceptuelle comme le vote tensoriel ont démontré une meilleure robustesse en combinant les informations voisines d’une manière hiérarchique. La méthode de vote tensoriel est plus proche de la perception humain que d’autres modèles standards. Comme démontré dans ce manuscrit, c’est un outil pour segmenter les petits vaisseaux plus puissant que les méthodes existantes. Cette combinaison spécifique nous permet de surmonter les défis de fragmentation éprouvés par les méthodes de type modèle déformable au niveau des petits vaisseaux. Nous proposons également d’utiliser un seuil adaptatif sur la réponse de l’algorithme de détection de ligne pour être plus robuste aux images non-uniformes. Nous illustrons également comment une combinaison des deux méthodes individuelles, à plusieurs échelles, est capable de reconnecter les vaisseaux sur des distances variables. Un algorithme de reconstruction des vaisseaux est également proposé. Cette dernière étape est nécessaire car l’information géométrique complète est requise pour pouvoir utiliser la segmentation dans un système d’aide au diagnostic. La segmentation a été validée sur une base de données d’images de fond d’oeil à haute résolution. Cette base contient des images manifestant une rétinopathie diabétique. La seg- mentation emploie des mesures de désaccord standards et aussi des mesures basées sur la perception. En considérant juste les petits vaisseaux dans les images de la base de données, l’amélioration dans le taux de sensibilité que notre méthode apporte par rapport à la méthode standard de détection multi-niveaux de lignes est de 6.47%. En utilisant les mesures basées sur la perception, l’amélioration est de 7.8%. Dans une seconde partie du manuscrit, nous proposons également une méthode pour caractériser les rétines saines ou anormales. Certaines images contiennent de la néovascula- risation. La caractérisation des vaisseaux en bonne santé ou anormale constitue une étape essentielle pour le développement d’un système d’aide au diagnostic. En plus des défis que posent les petits vaisseaux sains, les néovaisseaux démontrent eux un degré de complexité encore plus élevé. Ceux-ci forment en effet des réseaux de vaisseaux à la morphologie com- plexe et inhabituelle, souvent minces et à fortes courbures. Les travaux existants se limitent viii à l’utilisation de caractéristiques de premier ordre extraites des petits vaisseaux segmentés. Notre contribution est d’utiliser le vote tensoriel pour isoler les jonctions vasculaires et d’uti- liser ces jonctions comme points d’intérêts. Nous utilisons ensuite une statistique spatiale de second ordre calculée sur les jonctions pour caractériser les vaisseaux comme étant sains ou pathologiques. Notre méthode améliore la sensibilité de la caractérisation de 9.09% par rapport à une méthode de l’état de l’art. La méthode développée s’est révélée efficace pour la segmentation des vaisseaux réti- niens. Des tenseurs d’ordre supérieur ainsi que la mise en œuvre d’un vote par tenseur via un filtrage orientable pourraient être étudiés pour réduire davantage le temps d’exécution et résoudre les défis encore présents au niveau des jonctions vasculaires. De plus, la caractéri- sation pourrait être améliorée pour la détection de la rétinopathie proliférative en utilisant un apprentissage supervisé incluant des cas de rétinopathie diabétique non proliférative ou d’autres pathologies. Finalement, l’incorporation des méthodes proposées dans des systèmes d’aide au diagnostic pourrait favoriser le dépistage régulier pour une détection précoce des rétinopathies et d’autres pathologies oculaires dans le but de réduire la cessité au sein de la population.----------ABSTRACT As an easily accessible site for the direct observation of the circulation system, human retina can offer a unique insight into diseases development or outcome. Retinal vessels are repre- sentative of the general condition of the whole systematic circulation, and thus can act as a "window" to the status of the vascular network in the whole body. Each complication on the retina can have an adverse impact on the patient’s sight. In this direction, small vessels’ relevance is very high as they are among the first anatomical structures that get affected as diseases progress. Moreover, changes in the small vessels’ state, appearance, morphology, functionality, or even growth indicate the severity of the diseases. This thesis will focus on the retinal lesions due to diabetes, a serious metabolic disease affecting millions of people around the world. This disorder disturbs the natural blood glucose levels causing various pathophysiological changes in different systems across the human body. Diabetic retinopathy is the medical term that describes the condition when the fundus and the retinal vessels are affected by diabetes. As in other diseases, small vessels play a crucial role in the onset, the development, and the outcome of the retinopathy. More importantly, at the latest stage, new small vessels, or neovascularizations, growth constitutes a factor of significant risk for blindness. Therefore, there is a need to detect all the changes that occur in the small retinal vessels with the aim of characterizing the vessels to healthy or abnormal. The characterization, in turn, can facilitate the detection of a specific retinopathy locally, like the sight-threatening proliferative diabetic retinopathy. Segmentation techniques can automatically isolate important anatomical structures like the vessels, and provide this information to the physician to assist him in the final decision. In comprehensive systems for the automatization of DR detection, small vessels role is significant as missing them early in a CAD pipeline might lead to an increase in the false positive rate of red lesions in subsequent steps. So far, the efforts have been concentrated mostly on the accurate localization of the medium range vessels. In contrast, the existing models are weak in case of the small vessels. The required generalization to adapt an existing model does not allow the approaches to be flexible, yet robust to compensate for the increased variability in the appearance as well as the interference with the background. So far, the current template models (matched filtering, line detection, and morphological processing) assume a general shape for the vessels that is not enough to approximate the narrow, curved, characteristics of the small vessels. Additionally, due to the weak contrast in the small vessel regions, the current segmentation and the tracking methods produce fragmented or discontinued results. Alternatively, the small vessel segmentation can be accomplished at the expense of x background noise magnification, in the case of using thresholding or the image derivatives methods. Furthermore, the proposed deformable models are not able to propagate a contour to the full extent of the vasculature in order to enclose all the small vessels. The deformable model external forces are ineffective to compensate for the low contrast, the low width, the high variability in the small vessel appearance, as well as the discontinuities. Internal forces, also, are not able to impose a global shape constraint to the contour that could be able to approximate the variability in the appearance of the vasculature in different categories of vessels. Finally, machine learning approaches require the training of a classifier on a labelled set. Those sets are difficult to be obtained, especially in the case of the smallest vessels. In the case of the unsupervised methods, the user has to predefine the number of clusters and perform an effective initialization of the cluster centers in order to converge to the global minimum. This dissertation expanded the previous research work and provides a new segmentation method for the smallest retinal vessels. Multi-scale line detection (MSLD) is a recent method that demonstrates good segmentation performance in the retinal images, while tensor voting is a method first proposed for reconnecting pixels. For the first time, we combined the line detection with the tensor voting framework. The application of the line detectors has been proved an effective way to segment medium-sized vessels. Additionally, perceptual organization approaches like tensor voting, demonstrate increased robustness by combining information coming from the neighborhood in a hierarchical way. Tensor voting is closer than standard models to the way human perception functions. As we show, it is a more powerful tool to segment small vessels than the existing methods. This specific combination allows us to overcome the apparent fragmentation challenge of the template methods at the smallest vessels. Moreover, we thresholded the line detection response adaptively to compensate for non-uniform images. We also combined the two individual methods in a multi-scale scheme in order to reconnect vessels at variable distances. Finally, we reconstructed the vessels from their extracted centerlines based on pixel painting as complete geometric information is required to be able to utilize the segmentation in a CAD system. The segmentation was validated on a high-resolution fundus image database that in- cludes diabetic retinopathy images of varying stages, using standard discrepancy as well as perceptual-based measures. When only the smallest vessels are considered, the improve- ments in the sensitivity rate for the database against the standard multi-scale line detection method is 6.47%. For the perceptual-based measure, the improvement is 7.8% against the basic method. The second objective of the thesis was to implement a method for the characterization of isolated retinal areas into healthy or abnormal cases. Some of the original images, from which xi these patches are extracted, contain neovascularizations. Investigation of image features for the vessels characterization to healthy or abnormal constitutes an essential step in the direction of developing CAD system for the automatization of DR screening. Given that the amount of data will significantly increase under CAD systems, the focus on this category of vessels can facilitate the referral of sight-threatening cases to early treatment. In addition to the challenges that small healthy vessels pose, neovessels demonstrate an even higher degree of complexity as they form networks of convolved, twisted, looped thin vessels. The existing work is limited to the use of first-order characteristics extracted from the small segmented vessels that limits the study of patterns. Our contribution is in using the tensor voting framework to isolate the retinal vascular junctions and in turn using those junctions as points of interests. Second, we exploited second-order statistics computed on the junction spatial distribution to characterize the vessels as healthy or neovascularizations. In fact, the second-order spatial statistics extracted from the junction distribution are combined with widely used features to improve the characterization sensitivity by 9.09% over the state of art. The developed method proved effective for the segmentation of the retinal vessels. Higher order tensors along with the implementation of tensor voting via steerable filtering could be employed to further reduce the execution time, and resolve the challenges at vascular junctions. Moreover, the characterization could be advanced to the detection of prolifera- tive retinopathy by extending the supervised learning to include non-proliferative diabetic retinopathy cases or other pathologies. Ultimately, the incorporation of the methods into CAD systems could facilitate screening for the effective reduction of the vision-threatening diabetic retinopathy rates, or the early detection of other than ocular pathologies