Segmentar uma imagem consiste em particioná-la em regiões relevantes para uma dada aplicação, como para isolar um objeto de interesse no domínio de uma imagem. A segmentação é um dos problemas mais fundamentais e desafiadores em processamento de imagem e visão computacional. Ela tem desempenhado um papel importante, por exemplo, na pesquisa em neurologia, envolvendo imagens de Ressonância Magnética (RM), para fins de diagnóstico e tratamento de doenças relacionadas com alterações na anatomia do cérebro humano. Métodos de segmentação baseados na transformada imagem- floresta (IFT, Image Foresting Transform), com funções de conexidade suaves, possuem resultados ótimos, segundo o critério da otimalidade dos caminhos descrito no artigo original da IFT, e têm sido usados com sucesso em várias aplicações, como por exemplo na segmentação de imagens RM de 1.5 Tesla. No entanto, esses métodos carecem de restrições de regularização de borda, podendo gerar segmentações com fronteiras muito irregulares e indesejadas. Eles também não distinguem bem entre bordas similares com orientações opostas, e possuem alta sensibilidade à estimativa dos pesos das arestas do grafo, gerando problemas em imagens com efeitos de inomogeneidade. Nesse trabalho são propostas extensões da IFT, do ponto de vista teórico e experimental, através do uso de funções de conexidade não suaves, para a segmentação interativa de imagens por região. A otimalidade dos novos métodos é suportada pela maximização de energias de corte em grafo, ou como o fruto de uma sequência de iterações de otimização de caminhos em grafos residuais. Como resultados principais temos: O projeto de funções de conexidade mais adaptativas e flexíveis, com o uso de pesos dinâmicos, que permitem um melhor tratamento de imagens com forte inomogeneidade. O uso de grafos direcionados, de modo a explorar a polaridade de borda dos objetos na segmentação por região, e o uso de restrições de forma que ajudam a regularizar a fronteira delineada, favorecendo a segmentação de objetos com formas mais regulares. Esses avanços só foram possíveis devido ao uso de funções não suaves. Portanto, a principal contribuição desse trabalho consiste no suporte teórico para o uso de funções não suaves, até então evitadas na literatura, abrindo novas perpectivas na pesquisa de processamento de imagens usando grafos. / Segmenting an image consist in to partition it into relevant regions for a given application, as to isolate an object of interest in the domain of an image. Segmentation is one of the most fundamental and challenging problems in image processing and computer vision. It has played an important role, for example, in neurology research, involving images of Magnetic Resonance (MR), for the purposes of diagnosis and treatment of diseases related to changes in the anatomy of the human brain. Segmentation methods based on the Image Foresting Transform (IFT), with smooth connectivity functions, have optimum results, according to the criterion of path optimality described in the original IFT paper, and have been successfully used in many applications as, for example, the segmentation of MR images of 1.5 Tesla. However, these methods present a lack of boundary regularization constraints and may produce segmentations with quite irregular and undesired boundaries. They also do not distinguish well between similar boundaries with opposite orientations, and have high sensitivity to the arc-weight estimation of the graph, producing poor results in images with strong inhomogeneity effects. In this work, we propose extensions of the IFT framework, from the theoretical and experimental points of view, through the use of non-smooth connectivity functions for region-based interactive image segmentation. The optimality of the new methods is supported by the maximization of graph cut energies, or as the result of a sequence of paths optimizations in residual graphs. We have as main results: The design of more adaptive and flexible connectivity functions, with the use of dynamic weights, that allow better handling of images with strong inhomogeneity. The use of directed graphs to exploit the boundary polarity of the objects in region-based segmentation, and the use of shape constraints that help to regularize the segmentation boundary, by favoring the segmentation of objects with more regular shapes. These advances were only made possible by the use of non-smooth functions. Therefore, the main contribution of this work is the theoretical support for the usage of non-smooth functions, which were until now avoided in literature, opening new perspectives in the research of image processing using graphs.

Segmentação de objetos em imagens é um dos problemas mais fundamentais e desafiadores em processamento de imagem e visão computacional. O conhecimento de alto nível e específico do usuário é frequentemente requerido no processo de segmentação, devido à presença de fundos heterogêneos, objetos com bordas fracamente definidas, inomogeneidade de campo, ruído, artefatos, efeitos de volume parcial e seus efeitos conjuntos. Propriedades globais do objeto de interesse, tais como conexidade, restrições de forma e polaridade de borda, são conhecimentos prévios de alto nível úteis para a sua segmentação, permitindo a customização da segmentação para um objeto alvo. Nesse trabalho, apresentamos um novo método chamado Transformada Imagem-Floresta Orientada Conexa (COIFT, Connected Oriented Image Foresting Transform), que fornece soluções ótimas globais de acordo com uma medida de corte em grafo, incorporando a restrição de conexidade na Transformada Imagem-Floresta Orientada (OIFT, Oriented Image Foresting Transform), com o fim de garantir a geração de objetos conexos, bem como permitir o controle simultâneo da polaridade de borda. Enquanto o emprego de restrições de conexidade em outros arcabouços, tais como no algoritmo de corte-mínimo/fluxo-máximo (min-cut/max-flow), leva a um problema NP-difícil, a COIFT conserva o baixo custo computacional da OIFT. Experimentos mostram que a COIFT pode melhorar consideravelmente a segmentação de objetos com partes finas e alongadas, para o mesmo número de sementes em segmentação baseada em marcadores. / Object segmentation is one of the most fundamental and challenging problems in image processing and computer vision. The high-level and specific knowledge of the user is often required in the segmentation process, due to the presence of heterogeneous backgrounds, objects with poorly defined boundaries, field inhomogeneity, noise, artifacts, partial volume effects and their joint effects. Global properties of the object of interest, such as connectivity, shape constraints and boundary polarity, are useful high-level priors for its segmentation, allowing the customization of the segmentation for a given target object. In this work, we introduce a new method called Connected Oriented Image Foresting Transform (COIFT), which provides global optimal solutions according to a graph-cut measure in graphs, subject to the connectivity constraint in the Oriented Image Foresting Transform (OIFT), in order to ensure the generation of connected objects, as well as allowing the simultaneous control of the boundary polarity. While the use of connectivity constraints in other frameworks, such as in the min-cut/max-flow algorithm, leads to a NP-Hard problem, COIFT retains the low computational cost of OIFT. Experiments show that COIFT can considerably improve the segmentation of objects with thin and elongated parts, for the same number of seeds in segmentation based on markers.

L'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parole / Access to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysis

Dans cette thèse, nous nous intéressons à la recherche d’images similaires avec des descripteurs structurés par découpages d’images sur les graphes.Nous proposons une nouvelle approche appelée “bag-of-bags of words” (BBoW) pour la recherche d’images par le contenu (CBIR). Il s’agit d’une extension du modèle classique dit sac-de-mots (bag of words - BoW). Dans notre approche, une image est représentée par un graphe placé sur une grille régulière de pixels d’image. Les poids sur les arêtes dépendent de caractéristiques locales de couleur et texture. Le graphe est découpé en un nombre fixe de régions qui constituent une partition irrégulière de l’image. Enfin, chaque partition est représentée par sa propre signature suivant le même schéma que le BoW. Une image est donc décrite par un ensemble de signatures qui sont ensuite combinées pour la recherche d’images similaires dans une base de données. Contrairement aux méthodes existantes telles que Spatial Pyramid Matching (SPM), le modèle BBoW proposé ne repose pas sur l’hypothèse que des parties similaires d’une scène apparaissent toujours au même endroit dans des images d’une même catégorie. L’extension de cette méthode ` a une approche multi-échelle, appelée Irregular Pyramid Matching (IPM), est ´ également décrite. Les résultats montrent la qualité de notre approche lorsque les partitions obtenues sont stables au sein d’une même catégorie d’images. Une analyse statistique est menée pour définir concrètement la notion de partition stable.Nous donnons nos résultats sur des bases de données pour la reconnaissance d’objets, d’indexation et de recherche d’images par le contenu afin de montrer le caractère général de nos contributions / Image representation is a fundamental question for several computer vision tasks. The contributions discussed in this thesis extend the basic bag-of-words representations for the tasks of object recognition and image retrieval.In the present thesis, we are interested in image description by structural graph descriptors. We propose a model, named bag-of-bags of words (BBoW), to address the problems of object recognition (for object search by similarity), and especially Content-Based Image Retrieval (CBIR) from image databases. The proposed BBoW model, is an approach based on irregular pyramid partitions over the image. An image is first represented as a connected graph of local features on a regular grid of pixels. Irregular partitions (subgraphs) of the image are further built by using graph partitioning methods. Each subgraph in the partition is then represented by its own signature. The BBoW model with the aid of graphs, extends the classical bag-of-words (BoW) model by embedding color homogeneity and limited spatial information through irregular partitions of an image. Compared to existing methods for image retrieval, such as Spatial Pyramid Matching (SPM), the BBoW model does not assume that similar parts of a scene always appear at the same location in images of the same category. The extension of the proposed model to pyramid gives rise to a method we named irregular pyramid matching (IPM).The experiments demonstrate the strength of our approach for image retrieval when the partitions are stable across an image category. The statistical analysisof subgraphs is fulfilled in the thesis. To validate our contributions, we report results on three related computer vision datasets for object recognition, (localized)content-based image retrieval and image indexing. The experimental results in a database of 13,044 general-purposed images demonstrate the efficiency and effectiveness of the proposed BBoW framework.

台灣位於環太平洋地震帶上，地形為山地居多，且地質脆弱，加上位於西太平洋副熱帶地區，使得山區常受到颱風的侵擾而發生崩塌，導致土石流和洪水等災害發生，進而影響人民的生命和財產安全。因此，如何有效地建置崩塌地區域資料庫，成為國土保育與災害防治的重要課題。以往利用遙測與航測技術於崩塌地萃取的研究中，大多是於幾何糾正後衛星影像或是航測正射影像上分析崩塌地，但產製正射影像或是糾正衛星影像時，都需要花費較多的時間，對於講求時效性的救災行動而言頗為不利。本研究之目的為發展一套不需使用正射影像萃取崩塌地的方法，以物件導向影像分類法，於DMC（Digital Mapping Camera）航測原始影像上直接萃取崩塌地資訊。首先採取多重解析影像分割的技術，將航測影像依像元光譜和形狀同質性分割成不同區塊（物件），接著利用影像光譜統計值搭配區域成長法，偵測影像中的雲覆蓋地區並過濾。其次，根據光譜亮度統計特徵值，將影像區分成陰暗地區、正常地區以及較亮地區之三種土地覆蓋類型，使用線性相關糾正法（Linear-correlation Correction）將陰暗地區光譜亮度值轉換至正常地區，並利用物件的特徵值，如光譜、面積、形狀以及相關性依序萃取此三種土地覆蓋類型內的崩塌地。最後，使用光線追蹤法 （Ray-tracing），將崩塌地區塊從影像坐標轉換至地圖坐標，使其可以套疊地形資料如坡度、坡向，並進行空間分析以提升崩塌地的判釋精度。研究結果顯示，崩塌地萃取之使用者精度和生產者精度，均有82%以上，並且整個實驗可大量批次處理影像，及快速建立崩塌地資料庫，本研究之方法和崩塌地資料庫將有助於國土保育與崩塌地的災害防治。 / Being located in a subtropical and seismic zone of the West Pacific, the geology is fragile and topography is mountainous in Taiwan. Landslides, floods and other disasters induced by typhoons occur frequently, and it cause the life-threatening and property loss of human beings in Taiwan. Therefore, how to establish landslides data effectively become an important issue of land conservation and disasters management. In recently years, most of the researchers used aerial ortho-images or satellite georeferencing images to detect landslides sites. However, it spent a lot of time generating aerial ortho-images and rectifying satellite images, and it also reduced the efficiency of landslides analysis. Thus, this study developed an object-oriented classification method, which can be directly applied in raw image data, to detect landslides sites. Firstly, this study used multi-resolution image segmentation technique to segment images acquired by Z/I DMC（Digital Mapping Camera） into individual regions （objects） according to the homogeneity of spectral and shape features, and then removed cloud areas by using brightness features depended on the spectral information of images. Secondly, the study divided the entire image into three areas, which are darker area, normal area and lighter area, according to brightness value. Next, Linear-correlation correction （LCC） method was used in this study to transform darker area to normal area so that it can easily detect the landslides sites in darker area, and the object features, such as spectral, area, shape and space correlation indices, were used to extract landslide sites in images. Finally, in order to enhance the accuracy of landslide, the initial landslides were converted from image coordinate system to map coordinate system by ray-tracing method, so the initial landslides data can be further extracted by using topographic data, including slope and aspect data. The results of this study showed that the user and producer accuracies of detecting landslides can reach up to 82%. Moreover, the entire experiments process of this study can batch analyze automatically and establish landslides database quickly. It is expected that the method and landslides data of this study may have contribution to land conservation and disasters management.

Superpixels are the results of an image oversegmentation. They are an established intermediate level image representation and used for various applications including object detection, 3d reconstruction and semantic segmentation. While there are various approaches to create such segmentations, there is a lack of knowledge about their properties. In particular, there are contradicting results published in the literature. This thesis identifies segmentation quality, stability, compactness and runtime to be important properties of superpixel segmentation algorithms. While for some of these properties there are established evaluation methodologies available, this is not the case for segmentation stability and compactness. Therefore, this thesis presents two novel metrics for their evaluation based on ground truth optical flow. These two metrics are used together with other novel and existing measures to create a standardized benchmark for superpixel algorithms. This benchmark is used for extensive comparison of available algorithms. The evaluation results motivate two novel segmentation algorithms that better balance trade-offs of existing algorithms: The proposed Preemptive SLIC algorithm incorporates a local preemption criterion in the established SLIC algorithm and saves about 80 % of the runtime. The proposed Compact Watershed algorithm combines Seeded Watershed segmentation with compactness constraints to create regularly shaped, compact superpixels at the even higher speed of the plain watershed transformation. Operating autonomous systems over the course of days, weeks or months, based on visual navigation, requires repeated recognition of places despite severe appearance changes as they are for example induced by illumination changes, day-night cycles, changing weather or seasons - a severe problem for existing methods. Therefore, the second part of this thesis presents two novel approaches that incorporate superpixel segmentations in place recognition in changing environments. The first novel approach is the learning of systematic appearance changes. Instead of matching images between, for example, summer and winter directly, an additional prediction step is proposed. Based on superpixel vocabularies, a predicted image is generated that shows, how the summer scene could look like in winter or vice versa. The presented results show that, if certain assumptions on the appearance changes and the available training data are met, existing holistic place recognition approaches can benefit from this additional prediction step. Holistic approaches to place recognition are known to fail in presence of viewpoint changes. Therefore, this thesis presents a new place recognition system based on local landmarks and Star-Hough. Star-Hough is a novel approach to incorporate the spatial arrangement of local image features in the computation of image similarities. It is based on star graph models and Hough voting and particularly suited for local features with low spatial precision and high outlier rates as they are expected in the presence of appearance changes. The novel landmarks are a combination of local region detectors and descriptors based on convolutional neural networks. This thesis presents and evaluates several new approaches to incorporate superpixel segmentations in local region detection. While the proposed system can be used with different types of local regions, in particular the combination with regions obtained from the novel multiscale superpixel grid shows to perform superior to the state of the art methods - a promising basis for practical applications.

Η τεχνολογία των DNA μικροσυστοιχιών είναι μια υψηλής απόδοσης τεχνική που καθορίζει το κατά πόσο ένα κύτταρο μπορεί να ελέγξει, ταυτόχρονα, την έκφραση ενός πολύ μεγάλου αριθμού γονιδίων. Οι DNA μικροσυστοιχίες χρησιμοποιούνται για την παρακολούθηση και τον έλεγχο των αλλαγών που υφίστανται τα επίπεδα της γονιδιακής έκφρασης λόγω περιβαλλοντικών συνθηκών ή των αλλαγών που λαμβάνουν χώρα σε ασθενή κύτταρα σε σχέση με τα υγιή, χρησιμοποιώντας εξελιγμένες μεθόδους επεξεργασίας πληροφοριών. Εξαιτίας του τρόπου με τον οποίον παράγονται οι μικροσυστοιχίες, κατά την πειραματική επεξεργασία τους, εμφανίζεται ένας μεγάλος αριθμός διαδικασιών που εισάγουν σφάλματα, γεγονός που αναπόφευκτα οδηγεί στην δημιουργία υψηλού επιπέδου θορύβου και σε κατασκευαστικά προβλήματα στα προκύπτοντα δεδομένα. Κατά την διάρκεια των τελευταίων δεκαπέντε ετών, έχουν προταθεί από αρκετούς ερευνητές, πολλές και ικανές μέθοδοι που δίνουν λύσεις στο πρόβλημα της ενίσχυσης και της βελτίωσης των εικόνων μικροσυστοιχίας. Παρά το γεγονός της ευρείας ενασχόλησης των ερευνητών με τις μεθόδους επεξεργασίας των εικόνων μικροσυστοιχίας, η διαδικασία βελτίωσης τους αποτελεί ακόμη, ένα θέμα που προκαλεί ενδιαφέρον καθώς η ανάγκη για καλύτερα αποτελέσματα δεν έχει μειωθεί. Στόχος της διδακτορικής διατριβής είναι να συνεισφέρει σημαντικά στην προσπάθεια βελτίωσης των αποτελεσμάτων προτείνοντας μεθόδους ψηφιακής επεξεργασίας εικόνας που επιφέρουν βελτίωση της ποιότητας των εικόνων μέσω της μείωσης των συνιστωσών του θορύβου και της τεμαχιοποίησης της εικόνας. Πιο συγκεκριμένα, στα πλαίσια εκπόνησης της διατριβής παρουσιάζεται μια νέα αυτόματη μέθοδος εντοπισμού της θέσης των υπομικροσυστοιχιών σκοπός της οποίας είναι να καλυφθεί εν μέρει το κενό που υπάρχει στην βιβλιογραφία των μικροσυστοιχιών για το βήμα της προεπεξεργασίας που αφορά στην αυτόματη εύρεση της θέσης των υπομικροσυστοιχιών σε μια μικροσυστοιχία. Το βήμα αυτό της προεπεξεργασίας, σπανίως, λαμβάνεται υπόψιν καθώς στις περισσότερες εργασίες σχετικές με τις μικροσυστοιχίες, γίνεται μια αυθαίρετη υπόθεση ότι οι υπομικροσυστοιχίες έχουν με κάποιον τρόπο ήδη εντοπιστεί. Στα πραγματικά συστήματα αυτόματης ανάλυσης της εικόνας μικροσυστοιχίας, την αρχική εκτίμηση της θέσης των υπομικροσυστοιχιών, συνήθως, ακολουθεί η διόρθωση που πραγματοποιείται σε κάθε μια από τις θέσεις αυτές από τους χειριστές των συστημάτων. Η αυτοματοποίηση της εύρεσης θέσης των υπομικροσυστοιχιών οδηγεί σε πιο γρήγορους και ακριβείς υπολογισμούς που αφορούν στην πληροφορία που προσδιορίζεται από την εικόνα μικροσυστοιχίας. Στην συνέχεια της διατριβής, παρουσιάζεται μια συγκριτική μελέτη για την αποθορυβοποίηση των εικόνων μικροσυστοιχίας χρησιμοποιώντας τον μετασχηματισμό κυματιδίου και τα χωρικά φίλτρα ενώ επιπλέον με την βοήθεια τεχνικών της μαθηματικής μορφολογίας πραγματοποιείται δραστική μείωση του θορύβου που έχει την μορφή «αλάτι και πιπέρι». Τέλος, στα πλαίσια της εκπόνησης της διδακτορικής διατριβής, παρουσιάζεται μια μέθοδος κατάτμησης των περιοχών των spot των μικροσυστοιχιών, βασιζόμενη στον αλγόριθμο Random Walker. Κατά την πειραματική διαδικασία επιτυγχάνεται επιτυχής κατηγοριοποίηση των spot, ακόμα και στην περίπτωση εικόνων μικροσυστοιχίας με σοβαρά προβλήματα (θόρυβος, κατασκευαστικά λάθη, λάθη χειρισμού κατά την διαδικασία κατασκευής της μικροσυστοιχίας κ.α.), απαιτώντας σαν αρχική γνώση μόνο ένα μικρό αριθμό από εικονοστοιχεία προκειμένου να επιτευχθεί υψηλής ποιότητας κατάτμηση εικόνας. Τα πειραματικά αποτελέσματα συγκρίνονται ποιοτικά με αυτά που προκύπτουν με την εφαρμογή του μοντέλου κατάτμησης Chan-Vese το οποίο χρησιμοποιεί μια αρχική υπόθεση των συνόρων που υπάρχουν μεταξύ των ομάδων προς ταξινόμηση, αποδεικνύοντας ότι η ακρίβεια με την οποία η προτεινόμενη μέθοδος ταξινομεί τις περιοχές των spot στην σωστή κατηγορία σε μια μικροσυστοιχία, είναι σαφώς καλύτερη και πιο ακριβής. / DNA microarray technology is a high-throughput technique that determines how a cell can control the expression of large numbers of genes simultaneously. Microarrays are used to monitor changes in the expression levels of genes in response to changes in environmental conditions or in healthy versus diseased cells by using advanced information processing methods. Due to the nature of the acquisition process, microarray experiments involve a large number of error-prone procedures that lead to a high level of noise and structural problems in the resulting data. During the last fifteen years, robust methods have been proposed by many researchers resulting in several solutions for the enhancement of the microarray images. Though microarray image analysis has been elaborated quite enough, the enhancement process is still an intriguing issue as the need for even better results has not decreased. The goal of this PhD thesis is to significantly contribute to the above effort by proposing enhancing methods (denoising, segmentation) for the microarray image analysis. More specifically, a novel automated subgrid detection method is presented introducing a pre-processing step of the subgrid detection. This step is rarely taken into consideration as in most microarray enhancing methods it is arbitrarily assumed that the subgrids have been already identified. The automation of the subgrid detection leads to faster and more accurate information extraction from microarray images. Consequently, the PhD thesis presents a comparative denoising framework for microarray image denoising that includes wavelets and spatial filters while on the other hand uses mathematical morphology methods to reduce the “salt&pepper”-like noise in microarray images. Finally, a method for microarray spot segmentation is proposed, based on the Random Walker algorithm. During the experimental process, accurate spot segmentation is obtained even in case of relatively-high distorted images, using only an initial annotation for a small number of pixels for high-quality image segmentation. The experimental results are qualitatively compared to the Chan-Vese segmentation model, showing that the accuracy of the proposed microarray spot detection method is more accurate than the spot borders defined by the compared method.

Made available in DSpace on 2014-12-17T15:48:04Z (GMT). No. of bitstreams: 1 TiagoSS_DISSERT.pdf: 2900373 bytes, checksum: ea7bd73351348f5c75a5bf4f337c599f (MD5) Previous issue date: 2012-08-17 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The segmentation of an image aims to subdivide it into constituent regions or objects that have some relevant semantic content. This subdivision can also be applied to videos. However, in these cases, the objects appear in various frames that compose the videos. The task of segmenting an image becomes more complex when they are composed of objects that are defined by textural features, where the color information alone is not a good descriptor of the image. Fuzzy Segmentation is a region-growing segmentation algorithm that uses affinity functions in order to assign to each element in an image a grade of membership for each object (between 0 and 1). This work presents a modification of the Fuzzy Segmentation algorithm, for the purpose of improving the temporal and spatial complexity. The algorithm was adapted to segmenting color videos, treating them as 3D volume. In order to perform segmentation in videos, conventional color model or a hybrid model obtained by a method for choosing the best channels were used. The Fuzzy Segmentation algorithm was also applied to texture segmentation by using adaptive affinity functions defined for each object texture. Two types of affinity functions were used, one defined using the normal (or Gaussian) probability distribution and the other using the Skew Divergence. This latter, a Kullback-Leibler Divergence variation, is a measure of the difference between two probability distributions. Finally, the algorithm was tested in somes videos and also in texture mosaic images composed by images of the Brodatz album / A segmenta??o de uma imagem tem como objetivo subdividi-la em partes ou objetos constituintes que tenham algum conte?do sem?ntico relevante. Esta subdivis?o pode tamb?m ser aplicada a um v?deo, por?m, neste, os objetos est?o presentes nos diversos quadros que comp?em o v?deo. A tarefa de segmentar uma imagem torna-se mais complexa quando estas s?o compostas por objetos que contenham caracter?sticas texturais, com pouca ou nenhuma informa??o de cor. A segmenta??o difusa, do Ingl?s fuzzy, ? uma t?cnica de segmenta??o por crescimento de regi?es que determina para cada elemento da imagem um grau de pertin?ncia (entre zero e um) indicando a confian?a de que esse elemento perten?a a um determinado objeto ou regi?o existente na imagem, fazendo-se uso de fun??es de afinidade para obter esses valores de pertin?ncia. Neste trabalho ? apresentada uma modifica??o do algoritmo de segmenta??o fuzzy proposto por Carvalho [Carvalho et al. 2005], a fim de se obter melhorias na complexidade temporal e espacial. O algoritmo foi adaptado para segmentar v?deos coloridos tratando-os como volumes 3D. Para segmentar os v?deos, foram utilizadas informa??es provenientes de um modelo de cor convencional ou de um modelo h?brido obtido atrav?s de uma metodologia para a escolha dos melhores canais para realizar a segmenta??o. O algoritmo de segmenta??o fuzzy foi aplicado tamb?m na segmenta??o de texturas, fazendo-se uso de fun??es de afinidades adaptativas ?s texturas de cada objeto. Dois tipos de fun??es de afinidades foram utilizadas, uma utilizando a distribui??o normal de probabilidade, ou Gaussiana, e outra utilizando a diverg?ncia Skew. Esta ?ltima, uma varia??o da diverg?ncia de Kullback- Leibler, ? uma medida da diverg?ncia entre duas distribui??es de probabilidades. Por fim, o algoritmo foi testado com alguns v?deos e tamb?m com imagens de mosaicos de texturas criadas a partir do ?lbum de Brodatz e outros

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Made available in DSpace on 2014-12-17T14:55:35Z (GMT). No. of bitstreams: 1 JacksonGS.pdf: 1963039 bytes, checksum: ed3464892d7bb73b5dcab563e42f0e01 (MD5) Previous issue date: 2009-09-28 / Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth / Segmenta??o de imagens ? um dos problemas de processamento de imagens que merece especial interesse da comunidade cient?fica. Neste trabalho, s?o estudado m?todos n?o-supervisionados para detec??o de algomerados (clustering) e reconhecimento de padr?es (pattern recognition) em segmenta??o de imagens m?dicas M?todos baseados em t?cnicas de computa??o natural t?m se mostrado bastante atrativos nestas tarefas e s?o estudados aqui como uma forma de verificar a sua aplicabilidade em segmenta??o de imagens m?dicas. Este trabalho trata de implementa os m?todos GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm) PSOKA (Algoritmo de clustering baseado em PSO (Particle Swarm Optimization) e K means) e PSOFCM (Algoritmo de clustering baseado em PSO e FCM (Fuzzy C Means)). Al?m disso, como forma de avaliar os resultados fornecidos pelos algoritmos s?o utilizados ?ndices de valida??o de clustering como forma de medida quantitativa Avalia??es visuais e qualitativas tamb?m s?o realizadas, principalmente utilizando dados do sistema BrainWeb, um gerador de imagens do c?rebro, como ground truth