Segmentação e simulação de contornos em imagens através de processos físicos

O objectivo principal deste trabalho é, a partir de duas imagens de um mesmo objecto em dois instantes distintos, simular as formas intermédias que o objecto assume quando o seu comportamento é guiado por princípios físicos. Para tal, é preciso começar por segmentar o objecto nas duas imagens em questão extraindo o seu contorno, após definição manual de um contorno inicial em cada uma delas. Seguidamente, cada um dos contornos definidos evoluirá automaticamente ao longo de um processo iterativo até alcançar a fronteira do objecto desejado. Para isso, é construído um modelo deformável para cada um dos contornos usando o método dos elementos finitos. Posteriormente, a evolução temporal do modelo físico até ao contorno final desejado é obtida resolvendo a equação de equilíbrio dinâmico que contrabalança as forças externas e internas virtualmente aplicadas no objecto modelizado.Para simular a deformação entre os dois contornos obtidos na segmentação, é utilizada análise modal complementada com técnicas de optimização para estabelecer a correspondência entre os dados pontuais dos mesmos. Após esta fase de emparelhamento, o campo dos deslocamentos entre os dois contornos é simulado através da equação de equilíbrio dinâmico

Image processing and analysis : applications and trends

The computational analysis of images is challenging as it usually involves tasks such as segmentation, extraction of representative features, matching, alignment, tracking, motion analysis, deformation estimation, and 3D reconstruction. To carry out each of these tasks in a fully automatic, efficient and robust manner is generally demanding.The quality of the input images plays a crucial role in the success of any image analysis task. The higher their quality, the easier and simpler the tasks are. Hence, suitable methods of image processing such as noise removal, geometric correction, edges and contrast enhancement or illumination correction are required.Despite the challenges, computational methods of image processing and analysis are suitable for a wide range of applications.In this paper, the methods that we have developed for processing and analyzing objects in images are introduced. Furthermore, their use in applications from medicine and biomechanics to engineering and materials sciences are presented

Computer analysis of objects’ movement in image sequences: methods and applications

Computer analysis of objects’ movement in image sequences is a very complex problem, considering that it usually involves tasks for automatic detection, matching, tracking, motion analysis and deformation estimation. In spite of its complexity, this computational analysis has a wide range of important applications; for instance, in surveillance systems, clinical analysis of human gait, objects recognition, pose estimation and deformation analysis. Due to the extent of the purposes, several difficulties arise, such as the simultaneous tracking of manifold objects, their possible temporary occlusion or definitive disappearance from the image scene, changes of the viewpoints considered in images acquisition or of the illumination conditions, or even nonrigid deformations that objects may suffer in image sequences. In this paper, we present an overview of several methods that may be considered to analyze objects’ movement; namely, for their segmentation, tracking and matching in images, and for estimation of the deformation involved between images.This paper was partially done in the scope of project “Segmentation, Tracking and Motion Analysis of Deformable (2D/3D) Objects using Physical Principles”, with reference POSC/EEA-SRI/55386/2004, financially supported by FCT -Fundação para a Ciência e a Tecnologia from Portugal. The fourth, fifth and seventh authors would like to thank also the support of their PhD grants from FCT with references SFRH/BD/29012/2006, SFRH/BD/28817/2006 and SFRH/BD/12834/2003, respectively

Accessing earth observation data using JPEG2000

Applications like, change detection, global monitoring, disaster detection and management have emerging requirements that need the availability of large amounts of data. This data is currently being captured by a multiplicity of instruments and Earth Observation (EO) sensors originating large volumes of data that needs to be stored, processed and accessed in order to be useful. The authors of this paper have been involved on an ESA-founded project, called HICOD2000 to study the applicability of the new image encoding standard JPEG2000 - to EO products. This paper presents and describes the system that was developed for HICOD2000 project, which allows, not only the encoding and decoding of several EO products, but also supports some of the security requirements identified previously that allows ESA to define and apply efficient EO data access security policies and even to exploit some EO products electronic commerce over the Internet. This system was integrated with the existing ESA Ground Segment systems specifically the Services Support Environment (SSE).info:eu-repo/semantics/acceptedVersio

Matching contours in images using curvature information

The work here described consists in searching for the optimum global matching between contours of two objects represented in images, which are sampled by equal or different number of points. Thus, to determine the optimum global matching between the points of two contours, it is used curvature information, that is totally invariant to rigid transformations. For the case of contours sampled by different numbers of points, two approaches are proposed to exclude, from the matching process, the additional points. In the last section of this paper, a method for the determination of the rigid transformation associated to two contours that is based in the same solution considered in our matching process is also described

Determination of objects contours using physical principles

Segmentation, the identification of an object represented in a static image or along image sequences,is one of the most common and complex tasks in the domain of Computational Vision. Usually,whenever we intend to extract higher level information from images, we need to start by segmentingthem.The main goal of this work is to segment an object represented in an image by extracting its contourafter defining an initial contour for it; this coarse contour will evolve along an iterative process untilit reaches the frontier of the desired object, figure 1. For that purpose, a deformable model is used,whose behaviour is driven by physical principles

An automated system for lung nodule detection in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 9 pages, 9 figures; Proceedings of the SPIE Medical Imaging Conference, 17-22 February 2007, San Diego, California, USA, Vol. 6514, 65143

3D computational simulation and experimental characterization of polymeric stochastic network materials : case studies in reinforced eucalyptus office paper and nanofibrous materials

The properties of stochastic fibrous materials like paper and nanowebs are highly dependent on those fibers from which the network structure is made. This work contributes to a better understanding of the effect of fiber properties on the network structural properties, using an original 3D fibrous material model with experimental validation, and its application to different fibrous materials used in reinforced Eucalyptus office paper and nanofibrous networks. To establish the relationships between the fiber and the final structural material properties, an experimental laboratorial plan has been executed for a reinforced fibrous structure, and a physical based 3D model has been developed and implemented. The experimental plan was dedicated to an important Portuguese material: the reinforced Eucalyptus based office paper. Office paper is the principal Portuguese paper industry product. This paper is mainly produced from Eucalyptus globulus bleached kraft pulp with a small incorporation of a softwood pulp to increase paper strength. It is important to access the contribution of different reinforcement pulp fibers with different biometry and coarseness to the final paper properties. The two extremes of reinforcement pulps are represented by a Picea abies kraft softwood pulp, usually considered the best reinforcement fiber, and the Portuguese pine Pinus pinaster kraft pulp. Fiber flexibility was determined experimentally using the Steadman and Luner method with a computerized acquisition device. When comparing two reinforcement fibers, the information about fiber flexibility and biometry is determinant to predict paper properties. The values presented correspond to the two extremes of fibers available as reinforcement fibers, regarding wall thickness, beating ability and flexibility values. Pinus pinaster has the thickest fiber wall, and consequently it is less flexible than the thinner wall fibers: Pinus sylvestris and Picea abies. Experimental results for the evolutions of paper properties, like paper apparent density, air permeability, tensile and tear strength, together with fiber flexibility for the two reinforcement fibers, constitute valuable information, also applicable for other reinforcement fibers, with fiber walls dimensions in this range. After having quantified the influence of fiber flexibility, we identified that this is as a key physical property to be included in our structural model. Therefore, we chose to develop a 3D network model that includes fiber bending in the z direction as an important parameter. The inclusion of fiber flexibility was done for the first time by Niskanen, in a model known as the KCL-Pakka model. We propose an extension of this model, with improvements on the fiber model, as well as an original computational implementation. A simulator has been developed from scratch and the results have been validated experimentally using handmade laboratory structures made from Eucalyptus fibers (hardwood fibers), and also Pinus pinaster, Pinus Sylvestris and Picea abies fibers, which are representative reinforcement fibers. Finally, the model was modified and extended to obtain an original simulator to nanofibrous materials, which is also an important innovation. In the network model developed in this work, the structure is formed by the sequential deposition of fibers, which are modeled individually. The model includes key papermaking fiber properties like morphology, flexibility, and collapsibility and process operations such as fiber deposition, network forming or densification. For the first time, the model considers the fiber microstructure level, including lumen and fiber wall thickness, with a resolution up to 0.05μm for the paper material case and 0.05nm for the nanofibrous materials. The computational simulation model was used to perform simulation studies. In the case of paper materials, it was used to investigate the relative influence of fiber properties such as fiber flexibility, dimensions and collapsibility. The developed multiscale model gave realistic predictions and enabled us to link fiber microstructure and paper properties. In the case of nanofibrous materials, the 3D network model was modified and implemented for Polyamide-6 electrospun and cellulose nanowebs. The influence of computational fiber flexibility and dimensions was investigated. For the Polyamide-6 electrospun network experimental results were compared visually with simulation results and similar evolutions were observed. For cellulose nanowebs the simulation study used literature data to obtain the input information for the nanocellulose fibers. The design of computer experiments was done using a space filling design, namely the Latin hypercube sampling design, and the simulations results were organized and interpreted using regression trees. Both the experimental characterization, and computational modeling, contributed to study the relationships between the polymeric fibers and the network structure formed.As propriedades de materiais estocásticos constituídos por fibras, tais como o papel ou nanoredes poliméricas, dependem fortemente das fibras a partir das quais a estrutura em rede se forma. Este trabalho contribui para uma melhor compreensão da influência das propriedades das fibras nas propriedades estruturais das redes, utilizando um modelo original 3D para materiais constituídos por fibras, com validação experimental, bem como a sua aplicação aos materiais utilizados no papel de escritório de Eucalyptus, com fibras de reforço, e a redes de nanofibras. Para estabelecer as relações entre a fibra e as propriedades estruturais do material, executou-se um planeamento experimental para uma estrutura fibrosa reforçada, e desenvolveu-se e implementou-se um modelo 3D de base física. O plano experimental teve como objecto um material relevante em Portugal: o papel de escritório de Eucalyptus com fibras de reforço. O papel de escritório é o produto principal da indústria de papel Portuguesa. Este tipo de papel é produzido a partir da pasta kraft branqueada de Eucalyptus globulus, com incorporação de uma pequena quantidade de pasta de reforço, “softwood”, para melhorar a resistência do papel. É importante avaliar a contribuição de diferentes fibras de reforço, com biometria e massas linear distinta, nas diferentes propriedades finais do papel. Os dois extremos das fibras de reforço estão representados pela pasta kraft de Picea abies, usualmente considerada a melhor fibra de reforço, e a pasta kraft Portuguesa de Pinus pinaster. A flexibilidade da fibra determinou-se experimentalmente utilizando o método de Steadman e Luner, com um dispositivo de aquisição automatizado. A informação relativa à flexibilidade e biometria da fibra é fundamental para inferir sobre as propriedades do papel. Os valores determinados correspondem a valores dos extremos, paras as fibras de reforço disponíveis no mercado, no que diz respeito a espessura de parede, refinabilidade e valores de flexibilidade. Pode considerar-se a fibra de Pinus pinaster num extremo, sendo a fibra de paredes mais espessas, e consequentemente menos flexível que as fibras de paredes mais finas: Pinus sylvestris e Picea abies. Desta forma, os resultados experimentais obtidos para estas fibras, relativos à evolução de propriedades do papel, nomeadamente densidade, permeabilidade ao ar, resistência à tracção e ao rasgamento, entre outros, constituem informação importante que pode ser aplicada a outras fibras de reforço, que se situem nesta gama. Como consequência lógica da identificação da flexibilidade da fibra como uma propriedade física determinante, e após a quantificação experimental, a escolha do modelo de papel recaiu sobre um modelo que inclui a flexibilidade como propriedade chave. Assim, desenvolvemos um modelo 3D que inclui a flexão das fibras na direcção transversal, isto é, a direcção da espessura do papel, também reconhecida como direcção da coordenada z. A inclusão da flexibilidade da fibra baseia-se no modelo de Niskanen, conhecido como o modelo KCL-Pakka. Apresenta-se uma extensão deste modelo, com modificações no modelo da fibra, bem como uma implementação computacional original. Desenvolveu-se um simulador para matérias em rede, que se validou com resultados experimentais. Efectuaram-se, também, as modificações necessárias para obter um simulador para nanomateriais, o que constitui uma inovação relevante. No modelo deste trabalho, desenvolvido para materiais fibrosos em rede, as fibras modelam-se individualmente e a estrutura forma-se sequencialmente pela sua deposição e conformação à estrutura existente. O modelo inclui propriedades das fibras determinantes, tais como morfologia, flexibilidade e colapsabilidade. Bem como etapas do processo, nomeadamente a deposição das fibras e a formação da rede, isto é, a densificação da estrutura. De uma forma original, o modelo da fibra inclui a espessura do lúmen e da parede da fibra, com uma resolução de 0.05μm para as fibras do papel e 0.05nm no caso das nanofibras. O modelo computacional desenvolvido utilizou-se na realização de estudos de simulação. No caso dos materiais papeleiros, utilizou-se para investigar a influência das propriedades das fibras, tendo-se obtido previsões realistas. No caso dos nanomateriais, o modelo foi modificado e implementado para as fibras electrofiadas de Poliamida-6 e redes de nanocelulose. O plano de experiencias computacionais utilizou uma distribuição no espaço “Latin hypercube” e os resultados das simulações organizaram-se recorrendo a árvores de regressão. Tanto a caracterização experimental, como a modelação computacional, contribuíram com valiosa informação para o estudo das relações entre as fibras poliméricas e as estruturas em rede por elas formadas

Computing statistics from a graph representation of road networks in satellite images for indexing and retrieval.

Retrieval from remote sensing image archives relies on the extraction of pertinent information from the data about the entity of interest (e.g. land cover type), and on the robustness of this extraction to nuisance variables (e.g. illumination). Most image-based characterizations are not invariant to such variables. However, other semantic entities in the image may be strongly correlated with the entity of interest and their properties can therefore be used to characterize this entity. Road networks are one example: their properties vary considerably, for example, from urban to rural areas. This paper takes the first steps towards classification (and hence retrieval) based on this idea. We study the dependence of a number of network features on the class of the image ('urban' or 'rural'). The chosen features include measures of the network density, connectedness, and 'curviness'. The feature distributions of the two classes are well separated in feature space, thus providing a basis for retrieval. Classification using kernel k-means confirms this conclusion