<p>This Master’s thesis studies the possibility of using image processing as a tool to facilitate vine management, in particular shoot counting and assessment of the grapevine canopy. Both are areas where manual inspection is done today. The thesis presents methods of capturing images and segmenting different parts of a vine. It also presents and evaluates different approaches on how shoot counting can be done. Within canopy assessment, the emphasis is on methods to estimate canopy density. Other possible assessment areas are also discussed, such as canopy colour and measurement of canopy gaps and fruit exposure. An example of a vine assessment system is given.</p>

The determination of the center slice, defined as a slice through the lateral ventricles in the axial plane in a volume of MR images is important to the segmentation of the image into its anatomical parts. The center or ventricle slice in a set of MR images is recognized by the shape of the ventricles in the axial plane as depicted by the cerebro-spinal fluids in the image. Currently, no technique exists to detect this slice and the purpose of this thesis is to find a slice through the lateral ventricles in the axial plane from a volume of MRI brain scan slices. There are several methodologies which will be discussed in the thesis, the Hough Transform and Object Matching using deformable templates being the primary ones. It is shown, in the test cases used, that these algorithms used together provided results that had almost 80 percent accuracy. However, a simple method to spatially calculate the center slice is also competitive in accuracy.

Seismic layer segmentation, oil-gas boundary surfaces recognition, and 3D volume data reconstruction are three important tasks in three-dimensional seismic image processing. Geophysical and geological parameters and properties have been known to exhibit progressive changes in a layer. However, there are also times when sudden changes can occur between two layers. λ-connectedness was proposed to describe such a phenomenon. Based on graph theory, λ-connectedness describes the relationship among pixels in an image. It is proved that λ-connectedness is an equivalence relation. That is, it can be used to partition an image into different classes and hence can be used to perform image segmentation. Using the random graph theory and λ-connectivity of the image, the length of the path in a λ-connected set can be estimated. In addition to this, the normal λ-connected subsets preserve every path that is λ-connected in the subsets. An O(nlogn) time algorithm is designed for the normal λ-connected segmentation. Techniques developed are used to find objects in 2D/3D seismic images. Finding the interface between two layers or finding the boundary surfaces of an oil-gas reserve is often asked. This is equivalent to finding out whether a λ-connected set is an interface or surface. The problem that is raised is how to recognize a surface in digital spaces. λ-connectedness is a natural and intuitive way for describing digital surfaces and digital manifolds. Fast algorithms are designed to recognize whether an arbitrary set is a digital surface. Furthermore, the classification theorem of simple surface points is deduced: there are only six classes of simple surface points in 3D digital spaces. Our definition has been proved to be equivalent to Morgenthaler-Rosenfeld's definition of digital surfaces in direct adjacency. Reconstruction of a surface and data volume is important to the seismic data processing. Given a set of guiding pixels, the problem of generating a λ-connected (subset of image) surface is an inverted problem of λ-connected segmentation. In order to simplify the fitting algorithm, gradual variation, an equivalent concept of λ-connectedness, is used to preserve the continuity of the fitted surface. The key theorem, the necessary and sufficient condition for the gradually varied interpolation, has been mathematically proven. A random gradually varied surface fitting is designed, and other theoretical aspects are investigated. The concepts are used to successfully reconstruct 3D seismic real data volumes. This thesis proposes λ-connectedness and its applications as applied to seismic data processing. It is used for other problems such as ionogram scaling and object tracking. It has the potential to become a general technique in image processing and computer vision applications. Concepts and knowledge from several areas in mathematics such as Set Theory, Fuzzy Set Theory, Graph Theory, Numerical Analysis, Topology, Discrete Geometry, Computational Complexity, and Algorithm Design and Analysis have been applied to the work of this thesis.

Image segmentation is an important research area in digital image processing with several applications in vision-guided autonomous robotics, product quality inspection, medical diagnosis, the analysis of remotely sensed images, etc. The aim of image segmentation can be defined as partitioning an image into homogeneous regions in terms of the features of pixels extracted from the image. Image segmentation methods can be classified into four main categories: 1) clustering methods, 2) region-based methods, 3) hybrid methods, and 4) bayesian methods. In this thesis, major image segmentation methods belonging to first three categories are examined and tested on typical images. Moreover, improvements are also proposed to well-known Recursive Shortest-Spanning Tree (RSST) algorithm. The improvements aim to better model each region during merging stage. Namely, grayscale histogram, joint histogram and homogeneous texture are used for better region modeling.

Computed tomography (CT) has become a standard in pulmonary imaging which allows the analysis of diseases like lung nodules, emphysema and embolism. The improved spatial and temporal resolution involves a dramatic increase in the amount of data that has to be stored and processed. This has motivated the development of computer aided diagnostics (CAD) systems that have released the physician from the tedious task of manually delineating the boundary of the structures of interest from such a large number of images, a pre-processing step known as image segmentation. Apart from being impractical, the manual segmentation is prone to high intra and inter observer subjectiveness. Automatic segmentation of the lungs with atelectasis poses a challenge because in CT images they have similar texture and gray level as the surrounding tissue. Consequently, the available graphical information is not sufficient to distinguish the boundary of the lung. The present work aims to close the existing gap left by the segmentation of atelectatic lungs in volume CT data. A-priori knowledge of anatomical information plays a key role in the achievement of this goal.

The generation of urban digital elevation models from satellite images using stereo reconstruction techniques poses several challenges due to its precision requirements. In this thesis we study three problems related to the reconstruction of urban models using stereo images in a low baseline disposition. They were motivated by the MISS project, launched by the CNES (Centre National d'Etudes Spatiales), in order to develop a low baseline acquisition model. The first problem is the restoration of irregularly sampled images and image fusion using a band limited interpolation model. A novel restoration algorithm is proposed, which incorporates the image formation model as a set of local constraints, and uses of a family of regularizers that allow to control the spectral behavior of the solution. Secondly, the problem of interpolating sparsely sampled images is addressed using a self-similarity prior. The related problem of image inpainting is also considered, and a novel framework for exemplar-based image inpainting is proposed. This framework is then extended to consider the interpolation of sparsely sampled images. The third problem is the regularization and interpolation of digital elevation models imposing geometric restrictions. The geometric restrictions come from a reference image. For this problem three different regularization models are studied: an anisotropic minimal surface regularizer, the anisotropic total variation and a new piecewise affine interpolation algorithm. / La generación de modelos urbanos de elevación a partir de imágenes de satélite mediante técnicas de reconstrucción estereoscópica presenta varios retos debido a sus requisitos de precisión. En esta tesis se estudian tres problemas vinculados a la generación de estos modelos partiendo de pares estereoscópicos adquiridos por satélites en una configuración con baseline pequeño. Estos problemas fueron motivados por el proyecto MISS, lanzado por el CNES (Centre National d'Etudes Spatiales) con el objetivo de desarrollar las técnicas de reconstrucción para imágenes adquiridas con baseline pequeños. El primer problema es la restauración de imágenes muestreadas irregularmente y la fusión de imágenes usando un modelo de interpolación de banda limitada. Se propone un nuevo método de restauración, el cual usa una familia de regularizadores que permite controlar el decaimiento espectral de la solución e incorpora el modelo de formación de imagen como un conjunto de restricciones locales. El segundo problema es la interpolación de imágenes muestreadas en forma dispersa usando un prior de auto similitud, se considera también el problema relacionado de inpainting de imágenes. Se propone un nuevo framework para inpainting basado en ejemplares, el cual luego es extendido a la interpolación de imágenes muestreadas en forma dispersa. El tercer problema es la regularización e interpolación de modelos digitales de elevación imponiendo restricciones geométricas las cuales se extraen de una imagen de referencia. Para este problema se estudian tres modelos de regularización: un regularizador anisótropo de superficie mínima, la variación total anisótropa y un nuevo algoritmo de interpolación afín a trozos.

[Truncated abstract] Texture exists all around us and serves as an important visual cue for the human visual system. Captured within an image, we identify texture by its recognisable visual pattern. It carries extensive information and plays an important role in our interpretation of a visual scene. The subject of this thesis is texture synthesis, which is de ned as the creation of a new texture that shares the fundamental visual characteristics of an existing texture such that the new image and the original are perceptually similar. Textures are used in computer graphics, computer-aided design, image processing and visualisation to produce realistic recreations of what we see in the world. For example, the texture on an object communicates its shape and surface properties in a 3D scene. Humans can discriminate between two textures and decide on their similarity in an instant, yet, achieving this algorithmically is not a simple process. Textures range in complexity and developing an approach that consistently synthe- sises this immense range is a dfficult problem to solve and motivates this research. Typically, texture synthesis methods aim to replicate texture by transferring the recognisable repeated patterns from the sample texture to synthesised output. Feature transferal can be achieved by matching pixels or patches from the sample to the output. As a result, two main approaches, pixel-based and patch-based, have es- tablished themselves in the active eld of texture synthesis. This thesis contributes to the present knowledge by introducing two novel texture synthesis methods. Both methods use image segmentation to improve synthesis results. ... The sample is segmented and the boundaries of the middle patch are confined to follow segment boundaries. This prevents texture features from being cut o prematurely, a common artifact of patch-based results, and eliminates the need for patch boundary comparisons that most other patch- based synthesis methods employ. Since no user input is required, this method is simple and straight-forward to run. The tiling of pre-computed tile pairs allows outputs that are relatively large to the sample size to be generated quickly. Output results show great success for textures with stochastic and semi-stochastic clustered features but future work is needed to suit more highly structured textures. Lastly these two texture synthesis methods are applied to the areas of image restoration and image replacement. These two areas of image processing involve replacing parts of an image with synthesised texture and are often referred to as constrained texture synthesis. Images can contain a large amount of complex information, therefore replacing parts of an image while maintaining image fidelity is a difficult problem to solve. The texture synthesis approaches and constrained synthesis implementations proposed in this thesis achieve successful results comparable with present methods.

Σκοπός της παρούσας εργασίας είναι η μελέτη, η ανάπτυξη καθώς και η μερική εφαρμογή κάποιων μεθόδων για την ανίχνευση θέσης κάποιου οχήματος. Ιδιαίτερη βάση δόθηκε στη μελέτη και την ανάλυση της οπτικής ροής που θεωρείται βασική συγκριτικά με τις υπόλοιπες μεθόδους.Τέλος αναλύεται και μια μέθοδος κατάτμησης εικόνων. / The goal of this thesis is to study, develop and implement some methods of car detection. Particular emphasis is given to the analysis of optical flow, which is considered to be critical compared to other methods. Finally an analysis of a method for image segmentation is being developed.

Il y a un intérêt grandissant pour le développement d’outils de traitements adaptés aux images multimodales (plusieurs images de la même scène acquises avec différentes caractéristiques). Permettant une représentation plus complète de la scène, ces images multimodales ont de l'intérêt dans plusieurs domaines du traitement d'images, mais les exploiter et les manipuler de manière optimale soulève plusieurs questions. Cette thèse étend les représentations hiérarchiques, outil puissant pour le traitement et l’analyse d’images classiques, aux images multimodales afin de mieux exploiter l’information additionnelle apportée par la multimodalité et améliorer les techniques classiques de traitement d’images. Cette thèse se concentre sur trois différentes multimodalités fréquemment rencontrées dans le domaine de la télédétection. Nous examinons premièrement l’information spectrale-spatiale des images hyperspectrales. Une construction et un traitement adaptés de la représentation hiérarchique nous permettent de produire une carte de segmentation de l'image optimale vis-à-vis de l'opération de démélange spectrale. Nous nous concentrons ensuite sur la multimodalité temporelle, traitant des séquences d’images hyperspectrales. En utilisant les représentations hiérarchiques des différentes images de la séquence, nous proposons une nouvelle méthode pour effectuer du suivi d’objet et l’appliquons au suivi de nuages de gaz chimique dans des séquences d’images hyperspectrales dans le domaine thermique infrarouge. Finalement, nous étudions la multimodalité sensorielle, c’est-à-dire les images acquises par différents capteurs. Nous appuyant sur le concept des tresses de partitions, nous proposons une nouvelle méthodologie de segmentation se basant sur un cadre de minimisation d’énergie. / There is a growing interest in the development of adapted processing tools for multimodal images (several images acquired over the same scene with different characteristics). Allowing a more complete description of the scene, multimodal images are of interest in various image processing fields, but their optimal handling and exploitation raise several issues. This thesis extends hierarchical representations, a powerful tool for classical image analysis and processing, to multimodal images in order to better exploit the additional information brought by the multimodality and improve classical image processing techniques. %when applied to real applications. This thesis focuses on three different multimodalities frequently encountered in the remote sensing field. We first investigate the spectral-spatial information of hyperspectral images. Based on an adapted construction and processing of the hierarchical representation, we derive a segmentation which is optimal with respect to the spectral unmixing operation. We then focus on the temporal multimodality and sequences of hyperspectral images. Using the hierarchical representation of the frames in the sequence, we propose a new method to achieve object tracking and apply it to chemical gas plume tracking in thermal infrared hyperspectral video sequences. Finally, we study the sensorial multimodality, being images acquired with different sensors. Relying on the concept of braids of partitions, we propose a novel methodology of image segmentation, based on an energetic minimization framework.

The text covers the theory of the Mumford and Shah model for digital image segmentation. The strong and weak formulation is presented and the questions of existence, uniqueness, and solution regularity is answered. Then, a particular variant of the model called `active contours without edges' is numerically implemented. This implementation is tested on several images, the results are presented in detail and theoretically explained.