A área médica tem se beneficiado das ferramentas construídas pela Computação e, ao mesmo tempo, tem impulsionado o desenvolvimento de novas técnicas em diversas especialidades da Computação. Dentre estas técnicas a segmentação tem como objetivo separar em uma imagem objetos de interesse, podendo chamar a atenção do profissional de saúde para áreas de relevância ao diagnóstico. Além disso, os resultados da segmentação podem ser utilizados para a reconstrução de modelos tridimensionais, que podem ter características extraídas que auxiliem o médico em tomadas de decisão. No entanto, a segmentação de imagens médicas ainda é um desafio, por ser extremamente dependente da aplicação e das estruturas de interesse presentes na imagem. Esta dissertação apresenta uma técnica de segmentação semiautomática do endocárdio do ventrículo esquerdo em conjuntos de imagens cardíacas de Ressonância Magnética Nuclear. A principal contribuição é a segmentação considerando todas as imagens provenientes de um exame, por meio da propagação dos resultados obtidos em imagens anteriormente processadas. Os resultados da segmentação são avaliados usando-se métricas objetivas como overlap, entre outras, comparando com imagens fornecidas por especialistas na área de Cardiologia / The medical field has been benefited from the tools built by Computing and has promote the development of new techniques in diverse Computer specialties. Among these techniques, the segmentation aims to divide an image into interest objects, leading the attention of the specialist to areas that are relevant in diagnosys. In addition, segmentation results can be used for the reconstruction of three-dimensional models, which may have extracted features that assist the physician in decision making. However, the segmentation of medical images is still a challenge because it is extremely dependent on the application and structures of interest present in the image. This dissertation presents a semiautomatic segmentation technique of the left ventricular endocardium in sets of cardiac images of Nuclear Magnetic Resonance. The main contribution is the segmentation considering all the images coming from an examination, through the propagation of the results obtained in previously processed images. Segmentation results are evaluated using objective metrics such as overlap, among others, compared to images provided by specialists in the Cardiology field

A área médica tem se beneficiado das ferramentas construídas pela Computação e, ao mesmo tempo, tem impulsionado o desenvolvimento de novas técnicas em diversas especialidades da Computação. Dentre estas técnicas a segmentação tem como objetivo separar em uma imagem objetos de interesse, podendo chamar a atenção do profissional de saúde para áreas de relevância ao diagnóstico. Além disso, os resultados da segmentação podem ser utilizados para a reconstrução de modelos tridimensionais, que podem ter características extraídas que auxiliem o médico em tomadas de decisão. No entanto, a segmentação de imagens médicas ainda é um desafio, por ser extremamente dependente da aplicação e das estruturas de interesse presentes na imagem. Esta dissertação apresenta uma técnica de segmentação semiautomática do endocárdio do ventrículo esquerdo em conjuntos de imagens cardíacas de Ressonância Magnética Nuclear. A principal contribuição é a segmentação considerando todas as imagens provenientes de um exame, por meio da propagação dos resultados obtidos em imagens anteriormente processadas. Os resultados da segmentação são avaliados usando-se métricas objetivas como overlap, entre outras, comparando com imagens fornecidas por especialistas na área de Cardiologia / The medical field has been benefited from the tools built by Computing and has promote the development of new techniques in diverse Computer specialties. Among these techniques, the segmentation aims to divide an image into interest objects, leading the attention of the specialist to areas that are relevant in diagnosys. In addition, segmentation results can be used for the reconstruction of three-dimensional models, which may have extracted features that assist the physician in decision making. However, the segmentation of medical images is still a challenge because it is extremely dependent on the application and structures of interest present in the image. This dissertation presents a semiautomatic segmentation technique of the left ventricular endocardium in sets of cardiac images of Nuclear Magnetic Resonance. The main contribution is the segmentation considering all the images coming from an examination, through the propagation of the results obtained in previously processed images. Segmentation results are evaluated using objective metrics such as overlap, among others, compared to images provided by specialists in the Cardiology field

Out-of-plane displacement (warpage) is one of the major thermomechanical reliability concerns for board-level electronic packaging. Printed wiring board (PWB) and component warpage results from CTE mismatch among the materials that make up the PWB assembly (PWBA). Warpage occurring during surface-mount assembly reflow processes and normal operations may cause serious reliability problems. In this research, a convective reflow and projection moire warpage measurement system was developed. The system is the first real-time, non-contact, and full-field measurement system capable of measuring PWB/PWBA/chip package warpage with the projection moire technique during different thermal reflow processes. In order to accurately simulate the reflow process and to achieve the ideal heating rate, a convective heating system was designed and integrated with the projection moire system. An advanced feedback controller was implemented to obtain the optimum heating responses. The developed system has the advantages of simulating different types of reflow processes, and reducing the temperature gradients through the PWBA thickness to ensure that the projection moire system can provide more accurate measurements. Automatic package detection and segmentation algorithms were developed for the projection moire system. The algorithms are used for automatic segmentation of the PWB and assembled packages so that the warpage of the PWB and chip packages can be determined individually. The effect of initial PWB warpage on the fatigue reliability of solder bumps on board assemblies was investigated using finite element modeling (FEM) and the projection moire system. The 3-D models of PWBAs with varying board warpage were used to estimate the solder bump fatigue life for different chip packages mounted on PWBs. The simulation results were validated and correlated with the experimental results obtained using the projection moire system and accelerated thermal cycling tests. Design of experiments and an advanced prediction model were generated to predict solder bump fatigue life based on the initial PWB warpage, package dimensions and locations, and solder bump materials. This study led to a better understanding of the correlation between PWB warpage and solder bump thermomechanical reliability on board assemblies.

This thesis deals with a graph cut approach for segmentation of the anatomical structures in volumetric medical images. The method used requires some voxels to be a priori identified as object or background seeds. The goal of this thesis is implementation of the graph cut method and construction of an interactive tool for segmentation. Selected metod's behaviour is examined on two datasets with manually-guided segmentation results. Testing is in one case focused on the influence of method parameters on segmentation results, while in the other deals with method tolerance towards various signal-to-noise and contrast-to-noise ratios on input. To assess the consistency of a given segmentation with the ground-truth the F-measure is used.

This work describes design and implementation of a tool for creating photomontages. The tool is based on methods of semi-automatic image segmentation. Work outlines problems of segmentation of image data and benefits of interaction with the user. It analyzes different approaches to interactive image segmentation, explains their principles and shows their positive and negative aspects. It also presents advantages and disadvantages of currently used photo-editing applications. Proposes application for creating photomontages which consists of two steps: Extraction of an object from picture and insertion of it into another picture. The first step uses the method of semi-automatic segmentation GrabCut based on the graph theory. The work also includes comparison between application and other applications in which it is possible to create a photomontage, and application tests done by users.

DeepPaint is a semi-automated segmentation tool that utilises a U-net architecture to performbinary segmentation. To maximise the model’s performance and minimise user time, it isadvisable to apply Transfer Learning (TL) and reuse a model trained on a similar segmentationtask. However, due to the sensitivity of medical data and the unique properties of certainsegmentation tasks, TL is not feasible for some applications. In such circumstances, SelfSupervised Learning (SSL) emerges as the most viable option to minimise the time spent inDeepPaint by a user. Various pretext tasks, exploring both corruption segmentation and corruption restoration, usingsuperpixels and square patches, were designed and evaluated. With a limited number ofiterations in both the pretext and downstream tasks, significant improvements across fourdifferent datasets were observed. The results reveal that SSL models, particularly those pretrained on corruption segmentation tasks where square patches were corrupted, consistentlyoutperformed models without pre-training, with regards to a cumulative Dice SimilarityCoefficient (DSC). To examine whether a model could learn relevant features from a pretext task, Centred KernelAlignment (CKA) was used to measure the similarity of feature spaces across a model's layersbefore and after fine-tuning on the downstream task. Surprisingly, no significant positivecorrelation between downstream DSC and CKA was observed in the encoder, likely due to thelimited fine-tuning allowed. Furthermore, it was examined whether pre-training on the entiredataset, as opposed to only the training subset, yielded different downstream results. Asexpected, significantly higher DSC in the downstream task is more likely if the model hadaccess to all data during the pretext task. The differences in downstream segmentationperformance between models that accessed different data subsets during pre-training variedacross datasets.

Gestos são ações que fazem parte da comunicação humana. Frequentemente, eles ocorrem junto com a fala e podem se manifestar por uma ação proposital, como o uso das mãos para explicar o formato de um objeto, ou como um padrão de comportamento, como coçar a cabeça ou ajeitar os óculos. Os gestos ajudam o locutor a construir sua fala e também ajudam o ouvinte a compreender a mensagem que está sendo transmitida. Pesquisadores de diversas áreas são interessados em entender como se dá a relação dos gestos com outros elementos do sistema linguístico, seja para suportar estudos das áreas da Linguística e da Psicolinguística, seja para melhorar a interação homem-máquina. Há diferentes linhas de estudo que exploram essa temática e entre elas está aquela que analisa os gestos a partir de fases: preparação, pré-stroke hold, stroke, pós-stroke hold, hold e retração. Assim, faz-se útil o desenvolvimento de sistemas capazes de automatizar a segmentação de um gesto em suas fases. Técnicas de aprendizado de máquina supervisionado já foram aplicadas a este problema e resultados promissores foram obtidos. Contudo, há uma dificuldade inerente à análise das fases do gesto, a qual se manifesta na alteração do contexto em que os gestos são executados. Embora existam algumas premissas básicas para definição do padrão de manifestação de cada fase do gesto, em contextos diferentes tais premissas podem sofrer variações que levariam a análise automática para um nível de alta complexidade. Este é o problema abordado neste trabalho, a qual estudou a variabilidade do padrão inerente à cada uma das fases do gesto, com apoio de aprendizado de máquina, quando a manifestação delas se dá a partir de um mesmo indivíduo, porém em diferentes contextos de produção do discurso. Os contextos de discurso considerados neste estudo são: contação de história, improvisação, descrição de cenas, entrevistas e aulas expositivas / Gestures are actions that make part of human communication. Commonly, gestures occur at the same time as the speech and they can manifest either through an intentional act, as using the hands to explain the format of an object, or as a pattern of behavior, as scratching the head or adjusting the glasses. Gestures help the speaker to build their speech and also help the audience to understand the message being communicated. Researchers from several areas are interested in understanding what the relationship of gestures with other elements of the linguistic system is like, whether in supporting studies in Linguistics or Psycho linguistics, or in improving the human-machine interaction. There are different lines of study that explore such a subject, and among them is the line that analyzes gestures according to their phases: preparation, pre-stroke hold, stroke, post-stroke hold, hold and retraction. Thus, the development of systems capable of automating the segmentation of gestures into their phases can be useful. Techniques that implement supervised machine learning have already been applied in this problem and promising results have been achieved. However, there is an inherent difficulty to the analysis of phases of gesture that is revealed when the context (in which the gestures are performed) changes. Although there are some elementary premises to set the pattern of expression of each gesture phase, such premises may vary and lead the automatic analysis to high levels of complexity. Such an issue is addressed in the work herein, whose purpose was to study the variability of the inherent pattern of each gesture phase, using machine learning techniques, when their execution is made by the same person, but in different contexts. The contexts of discourse considered in this study are: storytelling, improvisation, description of scenes, interviews and lectures

Gestos são ações que fazem parte da comunicação humana. Frequentemente, eles ocorrem junto com a fala e podem se manifestar por uma ação proposital, como o uso das mãos para explicar o formato de um objeto, ou como um padrão de comportamento, como coçar a cabeça ou ajeitar os óculos. Os gestos ajudam o locutor a construir sua fala e também ajudam o ouvinte a compreender a mensagem que está sendo transmitida. Pesquisadores de diversas áreas são interessados em entender como se dá a relação dos gestos com outros elementos do sistema linguístico, seja para suportar estudos das áreas da Linguística e da Psicolinguística, seja para melhorar a interação homem-máquina. Há diferentes linhas de estudo que exploram essa temática e entre elas está aquela que analisa os gestos a partir de fases: preparação, pré-stroke hold, stroke, pós-stroke hold, hold e retração. Assim, faz-se útil o desenvolvimento de sistemas capazes de automatizar a segmentação de um gesto em suas fases. Técnicas de aprendizado de máquina supervisionado já foram aplicadas a este problema e resultados promissores foram obtidos. Contudo, há uma dificuldade inerente à análise das fases do gesto, a qual se manifesta na alteração do contexto em que os gestos são executados. Embora existam algumas premissas básicas para definição do padrão de manifestação de cada fase do gesto, em contextos diferentes tais premissas podem sofrer variações que levariam a análise automática para um nível de alta complexidade. Este é o problema abordado neste trabalho, a qual estudou a variabilidade do padrão inerente à cada uma das fases do gesto, com apoio de aprendizado de máquina, quando a manifestação delas se dá a partir de um mesmo indivíduo, porém em diferentes contextos de produção do discurso. Os contextos de discurso considerados neste estudo são: contação de história, improvisação, descrição de cenas, entrevistas e aulas expositivas / Gestures are actions that make part of human communication. Commonly, gestures occur at the same time as the speech and they can manifest either through an intentional act, as using the hands to explain the format of an object, or as a pattern of behavior, as scratching the head or adjusting the glasses. Gestures help the speaker to build their speech and also help the audience to understand the message being communicated. Researchers from several areas are interested in understanding what the relationship of gestures with other elements of the linguistic system is like, whether in supporting studies in Linguistics or Psycho linguistics, or in improving the human-machine interaction. There are different lines of study that explore such a subject, and among them is the line that analyzes gestures according to their phases: preparation, pre-stroke hold, stroke, post-stroke hold, hold and retraction. Thus, the development of systems capable of automating the segmentation of gestures into their phases can be useful. Techniques that implement supervised machine learning have already been applied in this problem and promising results have been achieved. However, there is an inherent difficulty to the analysis of phases of gesture that is revealed when the context (in which the gestures are performed) changes. Although there are some elementary premises to set the pattern of expression of each gesture phase, such premises may vary and lead the automatic analysis to high levels of complexity. Such an issue is addressed in the work herein, whose purpose was to study the variability of the inherent pattern of each gesture phase, using machine learning techniques, when their execution is made by the same person, but in different contexts. The contexts of discourse considered in this study are: storytelling, improvisation, description of scenes, interviews and lectures

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-16T21:13:39Z No. of bitstreams: 1 HeberSousa.pdf: 2248069 bytes, checksum: e89eac1d4562ac1f2f53007d699f9c71 (MD5) / Made available in DSpace on 2017-08-16T21:13:39Z (GMT). No. of bitstreams: 1 HeberSousa.pdf: 2248069 bytes, checksum: e89eac1d4562ac1f2f53007d699f9c71 (MD5) Previous issue date: 2013-01-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Lung cancer is one of the most common of malignant tumors. It also has one of the highest rates of mortality among cancers. The reason for this is mainly linked to late diagnosis of the disease. For early detection of disease is very helpful to use medical images as support, the most important being, CT. With the acquisition of digital images is becoming more common to use computer systems for medical imaging. These systems assist in the clinical diagnosis, disease monitoring, and in some cases is used as a support for surgery. Because the search for new ways of human-computer interaction, natural interaction arises, which aims to provide a form of control with higher cognition. This control is usually performed using gestures. Interactions of gestures can be useful in controlling medical imaging systems and can ensure necessary sterility in operating rooms, because they are not required contacts manuals. Among the activities computer assisted important for the treatment of lung cancer, we have the segmentation of nodules. The segmentation of nodules can be performed automatically, semiautomatically or interactively. It is useful to speed up the diagnostic process, taking measurements, or observe the morphological appearance of the nodule. The objective of this study is to investigate the use of natural interaction interface for activities such as medical image visualization and segmentation of pulmonary nodules. The paper proposes the study of interaction techniques based on gestures to segment nodules in an interactive and semiautomatic. Finally, conducting experiments to evaluate the techniques proposed in the items ease of use, intuitiveness, accuracy and comfortability / O câncer de pulmão é um dos mais comuns dentre os tumores malignos. Ele também possui uma das taxas mais altas de mortalidade dentre os tipos de câncer. O motivo disso está ligado principalmente ao diagnóstico tardio da doença. Para a sua detecção precoce é muito útil a utilização de imagens médicas como apoio, sendo a mais importante, a tomografia computadorizada. Com a aquisição digital das imagens está cada vez mais comum a utilização de sistemas computacionais de visualização médica. Estes sistemas auxiliam no diagnóstico clínico, no acompanhamento de doenças, e em alguns casos é utilizado como apoio a cirurgias. Em virtude da busca por novos meios de interação humano-computador, surge a interação natural, que objetiva uma forma de controle mais próximo cognitivamente das ações realizadas, e geralmente é realizada através de gestos. Interações por gestos podem ser úteis no controle de sistemas de visualização médica e podem garantir a esterilização necessária em salas cirúrgicas, pois não são necessários contatos manuais. Dentre as atividades assistidas por computador importantes para o tratamento do câncer pulmonar, temos a segmentação de nódulos. A segmentação de nódulos pode ser realizada de forma automática, semiautomática ou interativamente. Elas são úteis para agilizar o processo de diagnóstico, realizar medições, ou observar o aspecto morfológico do nódulo. O objetivo do presente trabalho é investigar a utilização da interação natural como interface para atividades de visualização de imagens médicas e segmentação de nódulos pulmonares. Foi implementada uma série de ferramentas de segmentação, interativas e semiautomáticas, controladas a partir de gestos. Estes gestos foram desenvolvidos a partir de imagens capturadas por uma câmera especial chamada Kinect, que traduz a imagem em mapas de profundidade, podendo medir com precisão a distância de objetos na cena. Ao final do estudo, foi realizado experimentos para avaliar as técnicas propostas nos quesitos facilidade de uso, intuitividade, conforto e precisão.

Useful information extraction from satellite images for the use of other higher level applications such as road network extraction and update, city planning etc. is a very important and active research area. It is seen that pixel-based techniques becomes insufficient for this task with increasing spatial resolution of satellite imaging sensors day by day. Therefore, the use of object-based techniques becomes indispensable and the segmentation method selection is very crucial for object-based techniques. In this thesis, various segmentation algorithms applied in remote sensing literature are presented and a segmentation process that is based on watersheds and multi-scale segmentation is proposed to use as the segmentation step of an object-based classifier. For every step of the proposed segmentation process, qualitative and quantitative comparisons with alternative approaches are done. The ones which provide best performance are incorporated into the proposed algorithm. Also, an unsupervised segmentation accuracy metric to determine all parameters of the algorithm is proposed. By this way, the proposed segmentation algorithm has become a fully automatic approach. Experiments that are done on a database formed with images taken from Google Earth&reg / software provide promising results.