Segmentation en régions non supervisée par relaxation markovienne, une étude comparative

Nous présentons un algorithme de segmentation en régions non supervisé qui utilise la théorie des champs de Markov. Ces techniques de relaxation markoviennes amènent à de bons résultats mais présentent cependant les inconvénients, dans leur implantation classique, de conduire à des temps de calcul élevés et de nécessiter l'introduction de plusieurs seuils. Nous nous sommes donc proposé de résoudre ces deux problèmes majeurs et avons développé une nouvelle technique de relaxation markovienne. Une étude comparative des différentes stratégies d'utilisation des relaxations markoviennes est ensuite menée, de manière rigoureuse, aussi bien du point de vue qualité des résultats que du point de vue coût algorithmique. Des conclusions intéressantes en découlent

Unsupervised segmentation of road images. A multicriteria approach

This paper presents a region-based segmentation algorithm which can be applied to various problems since it does not requir e a priori knowledge concerning the kind of processed images . This algorithm, based on a split and merge method, gives reliable results both on homogeneous grey level images and on textured images . First, images are divided into rectangular sectors . The splitting algorithm works independently on each sector, and uses a homogeneity criterion based only on grey levels . The mergin g is then achieved through assigning labels to each region obtained by the splitting step, using extracted feature measurements . We modeled exploited fields (data field and label field) by Markov Random Fields (MRF), the segmentation is then optimall y determined using the Iterated Conditional Modes (ICM) . Input data of the merging step are regions obtained by the splitting step and their corresponding features vector. The originality of this algorithm is that texture coefficients are directly computed from these regions . These regions will be elementary sites for the Markov relaxation process . Thus, a region- based segmentation algorith m using texture and grey level is obtained . Results from various images types are presented .Nous présentons ici un algorithme de segmentation en régions pouvant s'appliquer à des problèmes très variés car il ne tient compte d'aucune information a priori sur le type d'images traitées. Il donne de bons résultats aussi bien sur des images possédant des objets homogènes au sens des niveaux de gris que sur des images possédant des régions texturées. C'est un algorithme de type division-fusion. Lors d'une première étape, l'image est découpée en fenêtres, selon une grille. L'algorithme de division travaille alors indépendamment sur chaque fenêtre, et utilise un critère d'homogénéité basé uniquement sur les niveaux de gris. La texture de chacune des régions ainsi obtenues est alors calculée. A chaque région sera associé un vecteur de caractéristiques comprenant des paramètres de luminance, et des paramètres de texture. Les régions ainsi définies jouent alors le rôle de sites élémentaires pour le processus de fusion. Celui-ci est fondé sur la modélisation des champs exploités (champ d'observations et champ d'étiquettes) par des champs de Markov. Nous montrerons les résultats de segmentation obtenus sur divers types d'images

SEGMENTAÇÃO DE DADOS DE PERFILAMENTO A LASER EM ÁREAS URBANAS UTILIZANDO UMA ABORDAGEM BAYESIANA

In this paper is presented a region-based methodology for Digital Elevation Modelsegmentation obtained from laser scanning data. The methodology is based on twosequential techniques, i.e., a recursive splitting technique using the quad treestructure followed by a region merging technique using the Markov Random Fieldmodel. The recursive splitting technique starts splitting the Digital Elevation Modelinto homogeneous regions. However, due to slight height differences in the DigitalElevation Model, region fragmentation can be relatively high. In order to minimizethe fragmentation, a region merging technique based on the Markov Random Fieldmodel is applied to the previously segmented data. The resulting regions are firstlystructured by using the so-called Region Adjacency Graph. Each node of theRegion Adjacency Graph represents a region of the Digital Elevation Modelsegmented and two nodes have connectivity between them if corresponding regionsshare a common boundary. Next it is assumed that the random variable related toeach node, follows the Markov Random Field model. This hypothesis allows thederivation of the posteriori probability distribution function whose solution isobtained by the Maximum a Posteriori estimation. Regions presenting highprobability of similarity are merged. Experiments carried out with laser scanningdata showed that the methodology allows to separate the objects in the DigitalElevation Model with a low amount of fragmentation.Neste artigo é apresentada uma metodologia para a segmentação de um ModeloDigital de Elevação obtido a partir de um sistema de varredura a laser. Ametodologia de segmentação baseia-se na utilização das técnicas de divisãorecursiva usando a estrutura quadtree e de fusão de regiões usando o modeloMarkov Random Field. Inicialmente a técnica de divisão recursiva é usada paraparticionar o Modelo Digital de Elevação em regiões homogêneas. No entanto,devido a ligeiras diferenças de altura no Modelo Digital de Elevação, nesta etapa afragmentação das regiões pode ser relativamente alta. Para minimizar essafragmentação, uma técnica de fusão de regiões baseada no modelo Markov RandomField é aplicada nos dados segmentados. As regiões resultantes são estruturadasusando um grafo de regiões adjacentes (Region Adjacency Graph). Cada nó doRegion Adjacency Graph corresponde a uma região do Modelo Digital de Elevaçãosegmentado e dois nós tem conectividade entre eles se as duas regiõescorrespondentes compartilham de uma mesma fronteira. Em seguida, assume-se queo comportamento da variável aleatória em relação a cada nó dá se de acordo comum Markov Random Field. Esta hipótese permite a obtenção da chamadadistribuição de probabilidade a posteriori, cuja solução é obtida via estimativa Maximum a Posteriori. Regiões que apresentam alta probabilidade de fusão sãofundidas. Os experimentos realizados com os dados de perfilamento a lasermostraram que a metodologia proposta permitiu separar os objetos no ModeloDigital de Elevação com um baixo nível de fragmentação

Novel Application of Neutrosophic Logic in Classifiers Evaluated under Region-Based Image Categorization System

Neutrosophic logic is a relatively new logic that is a generalization of fuzzy logic. In this dissertation, for the first time, neutrosophic logic is applied to the field of classifiers where a support vector machine (SVM) is adopted as the example to validate the feasibility and effectiveness of neutrosophic logic. The proposed neutrosophic set is integrated into a reformulated SVM, and the performance of the achieved classifier N-SVM is evaluated under an image categorization system. Image categorization is an important yet challenging research topic in computer vision. In this dissertation, images are first segmented by a hierarchical two-stage self organizing map (HSOM), using color and texture features. A novel approach is proposed to select the training samples of HSOM based on homogeneity properties. A diverse density support vector machine (DD-SVM) framework that extends the multiple-instance learning (MIL) technique is then applied to the image categorization problem by viewing an image as a bag of instances corresponding to the regions obtained from the image segmentation. Using the instance prototype, every bag is mapped to a point in the new bag space, and the categorization is transformed to a classification problem. Then, the proposed N-SVM based on the neutrosophic set is used as the classifier in the new bag space. N-SVM treats samples differently according to the weighting function, and it helps reduce the effects of outliers. Experimental results on a COREL dataset of 1000 general purpose images and a Caltech 101 dataset of 9000 images demonstrate the validity and effectiveness of the proposed method

Geometric refinement of laser-derived building roof contours using photogrammetric data

In this paper, a methodology is proposed for the geometric refinement of laser scanning building roof contours using high-resolution aerial images and Markov Random Field (MRF) models. The proposed methodology takes for granted that the 3D description of each building roof reconstructed from the laser scanning data (i.e., a polyhedron) is topologically correct and that it is only necessary to improve its accuracy. Since roof ridges are accurately extracted from laser scanning data, our main objective is to use high-resolution aerial images to improve the accuracy of roof outlines. In order to meet this goal, the available roof contours are first projected onto the image-space. After that,  the projected polygons and the straight lines extracted from the image are used to establish an MRF description, which is based on relations (relative length, proximity, and orientation) between the two sets of straight lines. The energy function associated with the MRF is minimized by using a modified version of the brute force algorithm, resulting in the grouping of straight lines for each roof object. Finally, each grouping of straight lines is topologically reconstructed based on the topology of the corresponding laser scanning polygon projected onto the image-space. The preliminary results showed that the proposed methodology is promising, since most sides of the refined polygons are geometrically better than corresponding projected laser scanning straight lines.Neste artigo uma metodologia é proposta para o refinamento geométrico de contornos de telhados extraídos de dados de varredura a laser, usando imagens aéreas de alta resolução e modelos de campo aleatório de Markov (MRF - Markov Random Field). A metodologia proposta assume que a descrição 3D (isto é, um poliedro) de cada telhado de edifício reconstruído de dados de varredura a laser está topologicamente correta e que é necessário apenas melhorar sua acurácia. Visto que as cumeeiras de telhado são acuradamente extraídas de dados de varredura a laser, o objetivo básico é usar imagens aéreas de  alta resolução para melhorar somente a qualidade geométrica dos contornos de telhado. A fim de atingir esta meta, os contornos 3D representando contornos de telhados são primeiramente transformados para o espaço imagem. Na seqüência, as retas extraídas da imagem e as retas resultantes dos polígonos projetados são utilizadas para estabelecer uma descrição MRF com base em relações (de comprimento, proximidade e orientação) entre ambos os conjuntos de retas. A função de energia associada com a descrição MRF é minimizada através de uma versão modificada do algoritmo de força bruta, resultando num agrupamento de retas para  cada contorno de telhado. Finalmente, cada agrupamento de retas é topologicamente reconstruído baseando-se na topologia do correspondente polígono projetado no espaço imagem. Os resultados obtidos mostraram que a metodologia proposta é promissora, visto que geralmente os polígonos refinados são geometricamente melhores que os correspondentes polígonos resultantes da projeção dos contornos 3D de telhados

EXTRAÇÃO AUTOMÁTICA DE CONTORNOS DE TELHADOS USANDO DADOS DE VARREDURA A LASER E CAMPOS RANDÔMICOS DE MARKOV

This paper proposes a methodology for automatic extraction of building roofcontours from a Digital Elevation Model (DEM), which is generated through theregularization of an available laser point cloud. The methodology is based on twosteps. First, in order to detect high objects (buildings, trees etc.), the DEM issegmented through a recursive splitting technique and a Bayesian mergingtechnique. The recursive splitting technique uses the quadtree structure forsubdividing the DEM into homogeneous regions. In order to minimize thefragmentation, which is commonly observed in the results of the recursive splittingsegmentation, a region merging technique based on the Bayesian framework isapplied to the previously segmented data. The high object polygons are extracted byusing vectorization and polygonization techniques. Second, the building roofcontours are identified among all high objects extracted previously. Taking intoaccount some roof properties and some feature measurements (e. g., area,rectangularity, and angles between principal axes of the roofs), an energy functionwas developed based on the Markov Random Field (MRF) model. The solution ofthis function is a polygon set corresponding to building roof contours and is foundby using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM´s showed that the methodologyworks properly, as it delivered roof contours with approximately 90% shapeaccuracy and no false positive was verified.Este artigo propõe uma metodologia para a extração automática de contornos detelhados de edifícios a partir de um MDE (Modelo Digital de Elevação), gerado apartir da regularização de uma malha irregular de dados laser preexistentes. Ametodologia baseia-se em duas etapas. Primeiramente, a fim de detectar objetosaltos (edifícios altos, árvores etc.), o MDE é segmentado através de uma técnica dedivisão recursiva e de uma técnica de fusão bayesiana. A técnica de divisãorecursiva usa a estrutura quadtree para subdividir o MDE em regiões homogêneas.A fim de minimizar a fragmentação que freqüentemente é observada nos resultadosda segmentação por divisão recursiva, uma técnica de fusão baseada em InferênciaBayesiana é aplicada aos dados previamente segmentados. Os contornos dos objetos altos são obtidos através de técnicas de vetorização e poligonização. Na segundaetapa, os contornos de telhados de edifícios são identificados entre todos os objetosaltos extraídos previamente. Levando em conta algumas propriedades de telhado ealguns atributos (por exemplo, área, retangularidade e ângulos entre os eixosprincipais dos telhados), uma função de energia foi desenvolvida com base nomodelo Markov Random Field (MRF). A solução desta função é um conjunto depolígonos representando contornos de telhados de edifícios e é encontrada atravésde técnicas de minimização, como o algoritmo Simulated Annealing (SA). Váriosexperimentos foram realizados com base em DEM´s obtidos a partir de dados devarredura a laser, os quais demonstraram que a metodologia proposta funcionaadequadamente, visto que foram extraídos contornos de telhados comaproximadamente 90% de completeza de área e nenhum falso positivo foiverificado

A comparative study of unsupervised regions segmentation strategies by Markov random fields

This article deals with the important problem of unsupervised segmentation of luminance images based on a markovian relaxation . As these relaxations need important computation times, we have developed a new approach which considerably decreases thes e times, without modify quality of segmentation results . This approach is based on a split and merge technique . The splitting step allows to decrease, in a important way, the number of entities present in the merging step and, thereafter, computation times . The method introduced for the splitting step is original ; it is based on extraction of second order statistics from cooccurrence matrices . The study shows advantages of these statistics, and compares them to those of one order extracted from grey level s histograms . The second point developed in this report concerns the merging step . It is accomplished by a markovian relaxation achieved o n irregular adjacency graph of regions coming from the splitting step . Many original contributions are presented to estimate th e hyper – parameters of the system .Cet article aborde le problème de la segmentation non supervisée par champs de Markov d'images de luminance. L'approche développée est de type division-fusion. L'étape de division, qui est une sursegmentation rapide, permet de diminuer de manière importante le nombre de données présentes dans le processus de fusion et, par la suite, les temps de calcul. La méthode introduite pour la division est basée sur l'extraction de statistiques d'ordre deux à partir des matrices de cooccurrence. L'étude menée montre l'avantage de ces statistiques par rapport à celles, d'ordre un, extraites des histogrammes de niveaux de gris. Le second point abordé dans cet article concerne la fusion. Elle est réalisée grâce à une modélisation par champs de Markov, à partir du graphe d'adjacence irrégulier de régions issues de la division. Des contributions sont amenées afin d'estimer les hyper-paramètres du système et le nombre d'étiquettes. Plusieurs résultats de segmentation sur différents types d'images réelles sont présentés afin de valider la méthode. Une étude comparative sur les différentes stratégies d'utilisation de relaxation par champs de Markov est alors menée, de manière rigoureuse, sur des images de synthèse. Cette comparaison est effectuée aussi bien du point de vue qualité des résultats que du point de vue coût algorithmique. Elle permet de montrer les avantages de la méthode proposée : diminuer considérablement les temps de calcul mis pour obtenir les segmentations, tout en n'altérant pas la qualité des résultats de celles-ci

An attention model and its application in man-made scene interpretation

The ultimate aim of research into computer vision is designing a system which interprets its surrounding environment in a similar way the human can do effortlessly. However, the state of technology is far from achieving such a goal. In this thesis different components of a computer vision system that are designed for the task of interpreting man-made scenes, in particular images of buildings, are described. The flow of information in the proposed system is bottom-up i.e., the image is first segmented into its meaningful components and subsequently the regions are labelled using a contextual classifier. Starting from simple observations concerning the human vision system and the gestalt laws of human perception, like the law of “good (simple) shape” and “perceptual grouping”, a blob detector is developed, that identifies components in a 2D image. These components are convex regions of interest, with interest being defined as significant gradient magnitude content. An eye tracking experiment is conducted, which shows that the regions identified by the blob detector, correlate significantly with the regions which drive the attention of viewers. Having identified these blobs, it is postulated that a blob represents an object, linguistically identified with its own semantic name. In other words, a blob may contain a window a door or a chimney in a building. These regions are used to identify and segment higher order structures in a building, like facade, window array and also environmental regions like sky and ground. Because of inconsistency in the unary features of buildings, a contextual learning algorithm is used to classify the segmented regions. A model which learns spatial and topological relationships between different objects from a set of hand-labelled data, is used. This model utilises this information in a MRF to achieve consistent labellings of new scenes

Hierarchical and Spatial Structures for Interpreting Images of Man-made Scenes Using Graphical Models

The task of semantic scene interpretation is to label the regions of an image and their relations into meaningful classes. Such task is a key ingredient to many computer vision applications, including object recognition, 3D reconstruction and robotic perception. It is challenging partially due to the ambiguities inherent to the image data. The images of man-made scenes, e. g. the building facade images, exhibit strong contextual dependencies in the form of the spatial and hierarchical structures. Modelling these structures is central for such interpretation task. Graphical models provide a consistent framework for the statistical modelling. Bayesian networks and random fields are two popular types of the graphical models, which are frequently used for capturing such contextual information. The motivation for our work comes from the belief that we can find a generic formulation for scene interpretation that having both the benefits from random fields and Bayesian networks. It should have clear semantic interpretability. Therefore our key contribution is the development of a generic statistical graphical model for scene interpretation, which seamlessly integrates different types of the image features, and the spatial structural information and the hierarchical structural information defined over the multi-scale image segmentation. It unifies the ideas of existing approaches, e. g. conditional random field (CRF) and Bayesian network (BN), which has a clear statistical interpretation as the maximum a posteriori (MAP) estimate of a multi-class labelling problem. Given the graphical model structure, we derive the probability distribution of the model based on the factorization property implied in the model structure. The statistical model leads to an energy function that can be optimized approximately by either loopy belief propagation or graph cut based move making algorithm. The particular type of the features, the spatial structure, and the hierarchical structure however is not prescribed. In the experiments, we concentrate on terrestrial man-made scenes as a specifically difficult problem. We demonstrate the application of the proposed graphical model on the task of multi-class classification of building facade image regions. The framework for scene interpretation allows for significantly better classification results than the standard classical local classification approach on man-made scenes by incorporating the spatial and hierarchical structures. We investigate the performance of the algorithms on a public dataset to show the relative importance of the information from the spatial structure and the hierarchical structure. As a baseline for the region classification, we use an efficient randomized decision forest classifier. Two specific models are derived from the proposed graphical model, namely the hierarchical CRF and the hierarchical mixed graphical model. We show that these two models produce better classification results than both the baseline region classifier and the flat CRF.Hierarchische und räumliche Strukturen zur Interpretation von Bildern anthropogener Szenen unter Nutzung graphischer Modelle Ziel der semantischen Bildinterpretation ist es, Bildregionen und ihre gegenseitigen Beziehungen zu kennzeichnen und in sinnvolle Klassen einzuteilen. Dies ist eine der Hauptaufgabe in vielen Bereichen des maschinellen Sehens, wie zum Beispiel der Objekterkennung, 3D Rekonstruktion oder der Wahrnehmung von Robotern. Insbesondere Bilder anthropogener Szenen, wie z.B. Fassadenaufnahmen, sind durch starke räumliche und hierarchische Strukturen gekennzeichnet. Diese Strukturen zu modellieren ist zentrale Teil der Interpretation, für deren statistische Modellierung graphische Modelle ein geeignetes konsistentes Werkzeug darstellen. Bayes Netze und Zufallsfelder sind zwei bekannte und häufig genutzte Beispiele für graphische Modelle zur Erfassung kontextabhängiger Informationen. Die Motivation dieser Arbeit liegt in der überzeugung, dass wir eine generische Formulierung der Bildinterpretation mit klarer semantischer Bedeutung finden können, die die Vorteile von Bayes Netzen und Zufallsfeldern verbindet. Der Hauptbeitrag der vorliegenden Arbeit liegt daher in der Entwicklung eines generischen statistischen graphischen Modells zur Bildinterpretation, welches unterschiedlichste Typen von Bildmerkmalen und die räumlichen sowie hierarchischen Strukturinformationen über eine multiskalen Bildsegmentierung integriert. Das Modell vereinheitlicht die existierender Arbeiten zugrunde liegenden Ideen, wie bedingter Zufallsfelder (conditional random field (CRF)) und Bayesnetze (Bayesian network (BN)). Dieses Modell hat eine klare statistische Interpretation als Maximum a posteriori (MAP) Schätzer eines mehrklassen Zuordnungsproblems. Gegeben die Struktur des graphischen Modells und den dadurch definierten Faktorisierungseigenschaften leiten wir die Wahrscheinlichkeitsverteilung des Modells ab. Dies führt zu einer Energiefunktion, die näherungsweise optimiert werden kann. Der jeweilige Typ der Bildmerkmale, die räumliche sowie hierarchische Struktur ist von dieser Formulierung unabhängig. Wir zeigen die Anwendung des vorgeschlagenen graphischen Modells anhand der mehrklassen Zuordnung von Bildregionen in Fassadenaufnahmen. Wir demonstrieren, dass das vorgeschlagene Verfahren zur Bildinterpretation, durch die Berücksichtigung räumlicher sowie hierarchischer Strukturen, signifikant bessere Klassifikationsergebnisse zeigt, als klassische lokale Klassifikationsverfahren. Die Leistungsfähigkeit des vorgeschlagenen Verfahrens wird anhand eines öffentlich verfügbarer Datensatzes evaluiert. Zur Klassifikation der Bildregionen nutzen wir ein Verfahren basierend auf einem effizienten Random Forest Klassifikator. Aus dem vorgeschlagenen allgemeinen graphischen Modell werden konkret zwei spezielle Modelle abgeleitet, ein hierarchisches bedingtes Zufallsfeld (hierarchical CRF) sowie ein hierarchisches gemischtes graphisches Modell. Wir zeigen, dass beide Modelle bessere Klassifikationsergebnisse erzeugen als die zugrunde liegenden lokalen Klassifikatoren oder die einfachen bedingten Zufallsfelder

Mobile hyperspectral imaging for structural damage detection

Title from PDF of title page viewed May 29, 2020Thesis advisor: ZhiQiang ChenVitaIncludes bibliographical references (pages 60-72)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020Numerous optical-imaging and machine-vision based inspection methods are found that aim to replace visual and human-based inspection with an automated or a highly efficient procedure. However, these machine-vision systems have not been entirely endorsed by civil engineers towards deploying these techniques in practice, partially due to their poor performance in object detection when structural cracks coexist with other complex scenes. A mobile hyperspectral imaging system is developed in this work, which captures hundreds of spectral reflectance values at a pixel in the visible and near-infrared (VNIR) portion of the electromagnetic spectrum bands. To prove its potential in discriminating complex objects, a machine learning methodology is developed with classification models that are characterized by four different feature extraction processes. Experimental validation with quantitative measures proves that hyperspectral pixels, when used conjunctly with dimensionality reduction, possess outstanding potential in recognizing eight different structural surface objects including cracks for concrete and asphalt surfaces, and outperform the gray-values that characterize the texture/shape of the objects. The authors envision the advent of computational hyperspectral imaging for automating structural damage inspection, especially when dealing with complex structural scenes in practice.Introduction -- Hyperspectral Image -- Preprocessing -- Methodology -- Machine Learning Approach -- Discussion -- Appendix 1-