Este trabalho apresenta uma metodologia de classificação de imagens multiespectrais aplicada a análise e mapeamento da evolução do uso da terra/cobertura vegetal em São Félix do Xingu, Sul do Pará. Imagens frações representando as proporções de sombra, vegetação e solo foram estimadas a partir das bandas 1 a 5 e 7 do Landsat-5 TM e relacionadas com as estruturas das classes de uso da terra/cobertura vegetal. As imagens frações geradas do modelo linear de mistura espectral foram importantes para reduzir a massa de dados e ao mesmo tempo realçar alvos de interesse na imagem. A banda do infravermelho próximo (TM-4) foi importante para realçar áreas de queimadas. A classificação adotada foi divida em etapas combinando técnica de segmentação por crescimento de regiões e uso de máscaras. Por meio da máscara foi possível restringir o processo de segmentação em regiões pré-estabelecidas com o intuito de adquirir um melhor particionamento da imagem. Adotando este procedimento, ao invés de realizar uma única segmentação para mapear todas as classes em uma única vez, foram realizadas várias segmentações ao longo das etapas. As regiões segmentadas foram agrupadas com um classificador não-supervionado batizado de ISOSEG. Os resultados mostram que a metodologia é bastante eficiente. A matriz de erro gerada para a classificação de 2008 apontou que as confusões mais freqüentes ocorreram entre as classes que apresentaram em certas localidades proporções de misturas parecidas: Capoeira e Campo/Pastagem-2; Campo/Pastagem-1 e Campo/Pastagem-2; Queimada-1 e Queimada-2; Solo Exposto e Campo/Pastagem-1. Considerando nove classes, o índice Kappa atingiu 0,58, o que representa um valor de concordância classificada como moderada. Quando o numero de classes foi reduzido para 6, agrupando as classes que apresentaram as maiores confusões, o índice Kappa subiu para 0,80, atingindo um valor de concordância quase perfeita. A comparação dos resultados das classificações de 1987, 1992, 2000 e 2008 juntamente com a analise de dados auxiliares permitiu traçar um modelo de evolução do desmatamento e do uso da terra para São Félix do Xingu. O intenso desmatamento observado principalmente a partir de 2000 foi relacionado com o incremento da atividade pecuária, sendo São Félix do Xingu o município que detém atualmente o segundo maior rebanho bovino do País. / In this work we present a methodological procedure for multi-spectral images classification to evaluate and map land-use and land-cover changes in São Félix do Xingu, Southern Pará (Brazilian Amazon). Fraction images representing shade, vegetation and soil abundance at the pixel scale were estimated using all six reflective bands of Thematic Mapper sensor (TM-1 to TM-5 and TM-7) and related to different types of land-use and land-cover classes. The linear spectral mixing analysis method was an alternative approach adopted to reduce the data-dimensionality while at the same time enhancing targets of interest. Also, the near-infrared band (TM-4) was employed to separate areas affected by burns (Queimadas in Portuguese). The classification routines were performed in stages by combining region-growing segmentation and use of masking techniques. For each stage, the segmentation process was directed to preselected areas by masking techniques in order to obtain a better image partitioning. This procedure resulted in more than one segmentation thereby reducing confusing errors during the classification routine. An unsupervised classifier by region named ISOSEG was employed to classify the segmented images. The analysis of classification results was mainly qualitative and visual except for the 2008 classification which was assessed through an error matrix. According to the error matrix analysis, misclassifications arose more frequently when a set of classes with similar mixture proportions were involved, such as: Capoeira and Campo/Pastagem-2; Campo/Pastagem-1 and Campo/Pastagem- 2; Queimada-1 and Queimada-2, and finally Bare Soil and Campo/Pastagem-1. As a robust measure of concordance for dichotomous data, the kappa statistic reached a value of 0.62 by considering nine land types of classes and it rose to 0.80 when the mapping classes were diminished to six. Theses kappa values represent moderate and strong agreements between the remotely sensed classification and the reference data, respectively. Making use of the classification results from 1987, 1992, 2000 and 2008 and auxiliary data, we tried to design a simple land evolution model to São Félix do Xingu. The deforestation process notably intensified since 2000 has been driven mainly by a continuous increase in cattle breeding, for wich São Félix do Xingu has the second-largest cattle herd of all Brazilian municipalities.

Cette thèse est dédiée à la détection de l'ombre, de la végétation et des bâtiments à partir d'une unique image optique très haute résolution. La première partie présente une nouvelle méthode pour détecter simultanément les ombres et la végétation : plusieurs indices d'ombre et de végétation sont comparés puis fusionnés grâce à la théorie de l'évidence de Dempster-Shafer afin d'obtenir une segmentation en trois classes : “ombre”, “végétation” et “autre”. Comme la fusion est une méthode pixellique, elle est incorporée dans un contexte markovien pour régulariser la segmentation. Dans la deuxième partie, une nouvelle technique de segmentation d'images par croissance de région est proposée. L'image est tout d'abord sur-segmentée en régions homogènes afin de remplacer la structure rigide de la grille de pixels. Une classification-fusion itérative est ensuite appliquée sur ces régions. À chaque itération, les régions sont classées en utilisant une segmentation markovienne, puis regroupées entre elles en fonction de la position des ombres, de leur classe, et de la rectangularité de la forme fusionnée. Les bâtiments sont estimés à partir de la classification finale comme étant les rectangles d'emprise minimale. Ces deux algorithmes ont été validés sur plusieurs images de télédétection et ont permis de démontrer leur efficacité. / This PhD thesis is devoted to the detection of shadows, vegetation and buildings from single high resolution optical remote sensing images. The first part introduces a new method for simultaneously detecting shadows and vegetation. Several shadow and vegetation indices were investigated and merged using the Dempster-Shafer evidence theory so as to obtain a segmentation map with three classes : “shadow”, “vegetation” and “other”. However, the performance of the fusion is sensitive to noise since it processes at a pixel-level. A Markov random field (MRF) is thus integrated to model spatial information within the image. In the second part, a novel region growing segmentation technique is proposed. The image is oversegmented into smaller homogeneous regions which replace the rigid structure of the pixel grid. An iterative region classification-merging is then applied over these regions. At each iteration, regions are classified using a MRF-based image segmentation, then, according to the position of shadows, regions having the same class are merged to produce shapes appropriate to rectangles. The final buildings are estimated using the recursive minimum bounding rectangle method from the final classification. These two algorithms have been validated on a variety of image datasets and demonstrate their efficiency.

The methodology of visualization of soft tissue is in biology and medicine a topic for many years. During this period there were approving many techniques how to achieve accurate and authentic image of the researched object or structure. X-ray computed tomography is very helpful to get this goal but is necessary to improve contrasting techniques as well as the techniques of image post-processing. This thesis deals with imaging soft tissue. Specifically, it focuses on mouse liver contrasting with the artificial resin Microfil. Thesis also describes image processing technique (thresholding and region growing) for the data of the measurement with the goal of the visualization of the sample in 3D.

This diploma thesis deals with a segmentation of soft tissues in facial part of mouse embryos in Matlab. Segmentation of soft tissues of mouse embryos was not fully automated and every case needs a specific solution. Solving parts of this issues can provide valuable data for evolutionary biologists. Issues about staining and segmentation techniques are described. On the basis of accessible literature otsu thresholding, region growing, k-means clustering and segmentation with atlas were tested. In the end of this paper are those methods tested and evaluated on 3D microtomography data.

The aim of this semestral thesis is to compare the possibilities of available micro-CT systems. Theoretic part of this thesis there deals with possibilities of staining soft tissues and embryos because of enhancement the contrast of micro-CT images. Here follows a description of sources X-ray and detectors of available micro-CT systems. In practice there was realized the staining of embryo in cooperation with Department of histology and embryology in Brno. Then followed a measuring on FSI in Brno, ČVUT in Prague and synchrotron Elettra in Italy. In semestral thesis are described of the thesis there are compared the micro-CT systems and results of measuring embryos by means of presented systems and results are compared.The best results were obtained on micro-CT in Brno, where X-ray tube and flat panel detector were used. This images were used for a segmentation of cartilage olfactory system by means of 3D region growing. From results they were created 3D models for comparison with a manually segmented model. A less accurate results were obtain with the semi-automatic segmentation but this method isn’t too time-consuming.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18556号 / 工博第3917号 / 新制||工||1602(附属図書館) / 31456 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 田村 正行, 准教授 須﨑 純一, 教授 小池 克明 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A cobertura regular de quase todo o planeta por sistemas de radar de abertura sintÃtica (synthetic aperture radar - SAR) orbitais e o uso de sistemas aerotransportados tÃm propiciado novos meios para obter informaÃÃes atravÃs do sensoriamento remoto de vÃrias regiÃes de nosso planeta, muitas delas inacessÃveis. Este trabalho trata do processamento de imagens digitais geradas por radar de abertura sintÃtica, especificamente da segmentaÃÃo, que consiste do isolamento ou particionamento dos objetos relevantes presentes em uma cena. A segmentaÃÃo de imagens digitais visa melhorar a interpretaÃÃo das mesmas em procedimentos subseqÃentes. As imagens SAR sÃo corrompidas por ruÃdo coerente, conhecido por speckle, que mascara pequenos detalhes e zonas de transiÃÃo entre os objetos. Tal ruÃdo à inerente ao processo de formaÃÃo dessas imagens e dificulta tarefas como a segmentaÃÃo automÃtica dos objetos existentes e a identificaÃÃo de seus contornos. Uma possibilidade para efetivar a segmentaÃÃo de imagens SAR consiste na filtragem preliminar do ruÃdo speckle, como etapa de tratamento dos dados. A outra possibilidade, aplicada neste trabalho, consiste em segmentar diretamente a imagem ruidosa, usando seus pixels originais como fonte de informaÃÃo. Para isso, à desenvolvida uma metodologia de segmentaÃÃo baseada em crescimento e fusÃo estatÃstica de regiÃes, que requer alguns parÃmetros para controlar o processo. As vantagens da utilizaÃÃo dos dados originais para realizar a segmentaÃÃo de imagens de radar sÃo a eliminaÃÃo de etapas de prÃ-processamento e o favorecimento da detecÃÃo das estruturas presentes nas mesmas. à realizada uma avaliaÃÃo qualitativa e quantitativa das imagens segmentadas, sob diferentes situaÃÃes, aplicando a tÃcnica proposta em imagens de teste contaminadas artificialmente com ruÃdo multiplicativo. Este segmentador à aplicado tambÃm no processamento de imagens SAR reais e os resultados sÃo promissores. / The regular coverage of the planet surface by spaceborne synthetic aperture radar (SAR)and also airborne systems have provided alternative means to gather remote sensing information of various regions of the planet, even of inaccessible areas. This work deals with the digital processing of synthetic aperture radar imagery, where segmentation is the main subject. It consists of isolating or partitioning relevant objects in a scene, aiming at improving image interpretation and understanding in subsequent tasks. SAR images are contaminated by coherent noise, known as speckle, which masks small details and transition zones among the objects. Such a noise is inherent in radar image generation process, making difficult tasks like automatic segmentation of the objects, as well as their contour identification. To segment radar images, one possible way is to apply speckle filtering before segmentation. Another one, applied in this work, is to perform noisy image segmentation using the original SAR pixels as input data, without any preprocessing,such as filtering. To provide segmentation, an algorithm based on region growing and statistical region merging has been developed, which requires some parameters to control the process. This task presents some advantages, as long as it eliminates preprocessing steps and favors the detection of the image structures, since original pixel information is exploited. A qualitative and quantitative performance evaluation of the segmented images is also executed, under different situations, by applying the proposed technique to simulated images corrupted with multiplicative noise. This segmentation method is also applied to real SAR images and the produced results are promising.

Orientador: Erivaldo Antonio da Silva / Resumo: O processamento digital de imagens (PDI) consiste em uma área de grande interesse científico. Em Cartografia, o PDI é muito utilizado para extração de feições cartográficas de interesse presentes nas imagens de sensoriamento remoto (SR). Dentre as feições cartográficas, a detecção de malhas viárias é de grande interesse científico, pois proporciona a obtenção de informações atualizadas e acuradas para a realização de planejamentos urbanos. Devido à sua importância, a literatura científica possui diversos trabalhos propondo diferentes metodologias de extração de malhas viárias em imagens digitais. Dentre as metodologias, é possível encontrar metodologias propostas baseadas em lógica fuzzy, em detector de bordas e crescimento de regiões, por exemplo. Contudo, os estudos existentes focam na aplicação da metodologia de extração para determinadas áreas ou situações e utilizam recortes da imagem em seus estudos devido à grande quantidade de informações contidas nessas imagens. O avanço tecnológico proporcionou que imagens de SR sejam adquiridas com alta resolução espacial, espectral e temporal. Esse fato produz uma grande quantidade de dados a serem processados durante estudos desenvolvidos nessas imagens, o que acarreta um alto custo computacional e, consequentemente, um alto tempo de processamento. Na tentativa de reduzir o tempo de execução das metodologias de extração, os desenvolvedores dedicam esforços na redução da complexidade dos algoritmos e na utilização de outros recurs... (Resumo completo, clicar acesso eletrônico abaixo) / Resumen: El procesamiento digital de imágenes (PDI) consiste en un área de gran interés científico en diferentes áreas. En Cartografía, el PDI es muy utilizado en estudios de teledetección para extracción de los objetos cartográficos de interés presentes en las imágenes orbitales. Entre los objetos cartográficos de interés, la detección de redes viales se ha vuelto de gran interés científico proporcionando la obtención de informaciones actualizadas y precisas para la realización de planificaciones urbanas, por ejemplo. En este sentido, la literatura científica posee diversos trabajos proponiendo diferentes metodologías de extracción de redes viales en imágenes orbitales. Es posible encontrar metodologías propuestas basadas en lógica fuzzy, detector de bordes y crecimiento por región, por ejemplo. Sin embargo, los estudios existentes se centran en la aplicación de la metodología de extracción para determinadas áreas o situaciones y utilizan recortes de la imagen orbitales en sus estudios debido a la gran cantidad de informaciones contenidas en esas imágenes. Además, el avance tecnológico proporcionó que las imágenes de teledetección se adquieran con altas resoluciones espacial, espectral y temporal. Este hecho produce una gran cantidad de datos a ser procesados durante estudios desarrollados en esas imágenes, lo que acarrea en un alto costo computacional y, consecuentemente, un alto tiempo de procesamiento. En el intento de reducir el tiempo de respuesta de las metodologías de extracci... (Resumen completo clicar acceso eletrônico abajo) / Abstract: Digital image processing (DIP) consists of an area of great scientific interest in different areas. In Cartography, the DIP is widely used in remote sensing studies to extract cartographic features of interest present in orbital images. Among the cartographic features, the detection of road networks has become of great scientific interest, since it can provide accurate and updated information for urban planning, for example. In this sense, the scientific literature has several works proposing different methodologies of extraction of road networks in orbital images. It is possible to find proposed methodologies based on fuzzy logic, edge detector and growth by region, for example. However, the existing studies focus on the application of the extraction methodology to certain areas or situations and use orbital image cuts in their studies due to the large amount of information contained in these images. In addition, the technological advance has allowed the acquisition of remote sensing images with high spatial, spectral and temporal resolutions. This fact produces a large amount of data to be processed during studies developed in these images, which results in a high computational cost and, consequently, a high processing time. In an attempt to reduce the response time of the extraction methodologies, the developers dedicate efforts in reducing the complexity of the algorithms and in using some available hardware resources suggesting solutions that include software and hardwar... (Complete abstract click electronic access below) / Doutor

Le traitement d’images est un axe de recherche très actif depuis des années. L’interprétation des images constitue une de ses branches les plus importantes de par ses applications socio-économiques et scientifiques. Cependant cette interprétation, comme la plupart des processus de traitements d’images, nécessite une phase de segmentation pour délimiter les régions à analyser. En fait l’interprétation est un traitement qui permet de donner un sens aux régions détectées par la phase de segmentation. Ainsi, la phase d’interprétation ne pourra analyser que les régions détectées lors de la segmentation. Bien que l’objectif de l’interprétation automatique soit d’avoir le même résultat qu’une interprétation humaine, la logique des techniques classiques de ce domaine ne marie pas celle de l’interprétation humaine. La majorité des approches classiques d’interprétation d’images séparent la phase de segmentation et celle de l’interprétation. Les images sont d’abord segmentées puis les régions détectées sont interprétées. En plus, au niveau de la segmentation les techniques classiques parcourent les images de manière séquentielle, dans l’ordre de stockage des pixels. Ce parcours ne reflète pas nécessairement le parcours de l’expert humain lors de son exploration de l’image. En effet ce dernier commence le plus souvent par balayer l’image à la recherche d’éventuelles zones d’intérêts. Dans le cas échéant, il analyse les zones potentielles sous trois niveaux de vue pour essayer de reconnaitre de quel objet s’agit-il. Premièrement, il analyse la zone en se basant sur ses caractéristiques physiques. Ensuite il considère les zones avoisinantes de celle-ci et enfin il zoome sur toute l’image afin d’avoir une vue complète tout en considérant les informations locales à la zone et celles de ses voisines. Pendant son exploration, l’expert, en plus des informations directement obtenues sur les caractéristiques physiques de l’image, fait appel à plusieurs sources d’informations qu’il fusionne pour interpréter l’image. Ces sources peuvent inclure les connaissent acquises grâce à son expérience professionnelle, les contraintes existantes entre les objets de ce type d’images, etc. L’idée de l’approche présentée ici est que simuler l’activité visuelle de l’expert permettrait une meilleure compatibilité entre les résultats de l’interprétation et ceux de l’expert. Ainsi nous retenons de cette analyse trois aspects importants du processus d’interprétation d’image que nous allons modéliser dans l’approche proposée dans ce travail : 1. Le processus de segmentation n’est pas nécessairement séquentiel comme la plus part des techniques de segmentations qu’on rencontre, mais plutôt une suite de décisions pouvant remettre en cause leurs prédécesseurs. L’essentiel étant à la fin d’avoir la meilleure classification des régions. L’interprétation ne doit pas être limitée par la segmentation. 2. Le processus de caractérisation d’une zone d’intérêt n’est pas strictement monotone i.e. que l’expert peut aller d’une vue centrée sur la zone à vue plus large incluant ses voisines pour ensuite retourner vers la vue contenant uniquement la zone et vice-versa. 3. Lors de la décision plusieurs sources d’informations sont sollicitées et fusionnées pour une meilleure certitude. La modélisation proposée de ces trois niveaux met particulièrement l’accent sur les connaissances utilisées et le raisonnement qui mène à la segmentation des images. / Image processing has been a very active area of research for years. The interpretation of images is one of its most important branches because of its socio-economic and scientific applications. However, the interpretation, like most image processing processes, requires a segmentation phase to delimit the regions to be analyzed. In fact, interpretation is a process that gives meaning to the regions detected by the segmentation phase. Thus, the interpretation phase can only analyze the regions detected during the segmentation. Although the ultimate objective of automatic interpretation is to produce the same result as a human, the logic of classical techniques in this field does not marry that of human interpretation. Most conventional approaches to this task separate the segmentation phase from the interpretation phase. The images are first segmented and then the detected regions are interpreted. In addition, conventional techniques of segmentation scan images sequentially, in the order of pixels appearance. This way does not necessarily reflect the way of the expert during the image exploration. Indeed, a human usually starts by scanning the image for possible region of interest. When he finds a potential area, he analyzes it under three view points trying to recognize what object it is. First, he analyzes the area based on its physical characteristics. Then he considers the region's surrounding areas and finally he zooms in on the whole image in order to have a wider view while considering the information local to the region and those of its neighbors. In addition to information directly gathered from the physical characteristics of the image, the expert uses several sources of information that he merges to interpret the image. These sources include knowledge acquired through professional experience, existing constraints between objects from the images, and so on.The idea of the proposed approach, in this manuscript, is that simulating the visual activity of the expert would allow a better compatibility between the results of the interpretation and those ofthe expert. We retain from the analysis of the expert's behavior three important aspects of the image interpretation process that we will model in this work: 1. Unlike what most of the segmentation techniques suggest, the segmentation process is not necessarily sequential, but rather a series of decisions that each one may question the results of its predecessors. The main objective is to produce the best possible regions classification. 2. The process of characterizing an area of interest is not a one way process i.e. the expert can go from a local view restricted to the region of interest to a wider view of the area, including its neighbors and vice versa. 3. Several information sources are gathered and merged for a better certainty, during the decision of region characterisation. The proposed model of these three levels places particular emphasis on the knowledge used and the reasoning behind image segmentation.

This thesis presents a 3D semi-automatic segmentation technique for extracting the lumen surface of the Carotid arteries including the bifurcation from 3D and 4D ultrasound examinations. Ultrasound images are inherently noisy. Therefore, to aid the inspection of the acquired data an adaptive edge preserving filtering technique is used to reduce the general high noise level. The segmentation process starts with edge detection with a recursive and separable 3D Monga-Deriche-Canny operator. To reduce the computation time needed for the segmentation process, a seeded region growing technique is used to make an initial model of the artery. The final segmentation is based on the inflatable balloon model, which deforms the initial model to fit the ultrasound data. The balloon model is implemented with the finite element method. The segmentation technique produces 3D models that are intended as pre-planning tools for surgeons. The results from a healthy person are satisfactory and the results from a patient with stenosis seem rather promising. A novel 4D model of wall motion of the Carotid vessels has also been obtained. From this model, 3D compliance measures can easily be obtained.