Schistosoma mansoni é o parasita causador da esquistossomose mansônica que, de acordo com o Ministério da Saúde do Brasil, afeta atualmente vários milhões de pessoas no país. Uma das formas de diagnóstico da esquistossomose é a detecção de ovos do parasita através da análise de lâminas microscópicas com material fecal. Esta tarefa é extremamente cansativa, principalmente nos casos de baixa endemicidade, pois a quantidade de ovos é muito pequena. Nesses casos, uma abordagem computacional para auxílio na detecção de ovos facilitaria o trabalho de diagnóstico. Os ovos têm formato ovalado, possuem uma membrana translúcida, apresentam uma espícula e sua cor é ligeiramente amarelada. Porém nem todas essas características são observadas em todos os ovos e algumas delas são visíveis apenas com uma ampliação adequada. Além disso, o aspecto visual do material fecal varia muito de indivíduo para indivíduo em termos de cor e presença de diversos artefatos (tais como partículas que não são desintegradas pelo sistema digestivo), tornando difícil a tarefa de detecção dos ovos. Neste trabalho investigamos, em particular, o problema de detecção das linhas que contornam a borda de vários dos ovos. Propomos um método composto por duas fases. A primeira fase consiste na detecção de estruturas do tipo linha usando operadores morfológicos. A detecção de linhas é dividida em três etapas principais: (i) realce de linhas, (ii) detecção de linhas, e (iii) refinamento do resultado para eliminar segmentos de linhas que não são de interesse. O resultado dessa fase é um conjunto de segmentos de linhas. A segunda fase consiste na detecção de subconjuntos de segmentos de linha dispostos em formato elíptico, usando um algoritmo baseado na transformada Hough. As elipses detectadas são fortes candidatas a contorno de ovos de S. mansoni. Resultados experimentais mostram que a abordagem proposta pode ser útil para compor um sistema de auxílio à detecção dos ovos. / Schistosoma mansoni is one of the parasites which causes schistosomiasis. According to the Brazilian Ministry of Health, several million people in the country are currently affected by schistosomiasis. One way of diagnosing it is by egg identification in stool. This task is extremely time-consuming and tiring, especially in cases of low endemicity, when only few eggs are present. In such cases, a computational approach to help the detection of eggs would greatly facilitate the diagnostic task. Schistosome eggs present oval shape, have a translucent membrane and a spike, and their color is slightly yellowish. However, not all these features are observed in every egg and some of them are visible only with an adequate microscopic magnification. Furthermore, the visual aspect of the fecal material varies widely from person to person in terms of color and presence of different artifacts (such as particles which are not disintegrated by the digestive system), making it difficult to detect the eggs. In this work we investigate the problem of detecting lines which delimit the contour of the eggs. We propose a method comprising two steps. The first phase consists in detecting line-like structures using morphological operators. This line detection phase is divided into three steps: (i) line enhancement, (ii) line detection, and (iii) result refinement in order to eliminate line segments that are not of interest. The output of this phase is a set of line segments. The second phase consists in detecting subsets of line segments arranged in an elliptical shape, using an algorithm based on the Hough transform. Detected ellipses are strong candidates to contour of S. mansoni eggs. Experimental results show that the proposed approach has potential to be effectively used as a component in a computer system to help egg detection.

Diplomová práce se zabývá vývojem dálkoměrného modulu určeného pro rozšíření senzorické výbavy fotbalového robotu kategorie MiroSot. Tento modul na vstupu přijímá data ze senzorické jednotky vyvinuté na Ústavu automatizace a měřicí techniky a z těchto dat extrahuje polohu míčku. Je srovnáno využití neuronové sítě a zjednodušené Houghovy transformace pro získání polohy těžiště míčku. V práci je popsána pomocná implementace funkcionality v prostředích MATLAB a C#.NET i hlavní implementace pro signálový mikrokontrolér Freescale MC56F8013. Výsledný modul splňuje nároky zadání a je plně funkční.

This thesis attempts to develop a conversion algorithm between local coordinates in constituent detector modules and global coordinates encompassing the whole detector structure in a generic detector. The thesis is a part of preparatory work for studying the Hough Transform as a means of track reconstruction in the level-1 hardware trigger in the upgraded trigger and data acquisition (TDAQ) system in the phase 2 upgrade of the ATLAS detector at CERN. The upgrades being made are to withstand much more extreme conditions that come with the high-Luminosity Large Hadron Collider (HL-LHC). Two algorithms have been made and then implemented in Python scripts to test their feasibility and to compare them against each-other. The Rotation algorithm uses several rotations to correctly place the local coordinates in the global system. The second, the Shear algorithm, simplifies the process into two shears and one rotation, using the small angle approximation. Both algorithms need to be extended to work with more parts of the detector to be considered complete. Despite having lower maximum precision the second algorithm is considered the most promising attempt, since it is much less sensitive to the truncation error that results from working in an integer environment, which is a requirement for use in FPGAs. / I denna uppsats görs ett försök att skapa en omvandlingsalgoritm mellan lokala koordinater i konstituerande detektormoduler och globala koordinater i hela detektorstrukturen för en generisk detektor. Uppsatsen är en del i förberedande arbete för att undersöka hur Houghtransformen kan användas för spårrekonstruktion i den hårdvarubaserade level-1 triggern i det uppgraderade trigger- och datainsamlingssystemet (TDAQ) i fas två-uppgraderingen av ATLAS detektorn vid CERN. Uppgraderingarna som görs är för att kunna utstå de mycket mer extrema förhållanden som medförs av högluminositetsuppgraderingen av Large Hadron Collider (HL-LHC). Två algoritmer har skapats och implementerats i Pythonskript för att testa genomförbarhet och för att jämföra med varandra. Rotationsalgoritmen använder ett antal rotationer för att korrekt placera ut de lokala koordinaterna i det globala systemet. Den andra, Skjuvalgortimen, förenklar processen till två skjuvningar och en rotation med hjälp av liten vinkel-approximationen. Båda algoritmerna behöver utökas för att fungera för fler delar av detektorn för att anses kompletta. Trots lägre maximal precision bedöms den andra algoritmen vara det mest lovande försöket, eftersom den är mycket mindre känslig för trunkeringsfelet som kommer av att arbeta i en heltalsmiljö, som är ett krav för FPGA-implementationen.

The aim of this project is to design an algorithm that identifies the Area of Interest (AOI) in ELISpot and FluoroSpot images. ELISpot and FluoroSpot are two varieties of a biochemical test used to analyze immune responses by quantifying the amount of cytokine secreted by cells. ELISpot and FluoroSpot images show a well that contains the cytokinesecreting cells which appear as scattered spots. Prior to counting the number of spots, it is required to detect the area in which to count the spots, i.e. the area delimited by the contour of the well. We propose to use the Circle Hough Transform together with filtering and the Laplacian of Gaussian edge detector in order to accurately detect such area. Furthermore we develop an outlier removal method that contributes to increase the robustness of the proposed detection method. Finally we compare our algorithm with another algorithm already in use. A Swedish biotech company called Mabtech has implemented an AOI identifier in the same field. Our proposed algorithm proves to be more robust and provides consistent results for all the images in the dataset.

Doenças vasculares são um problema mundial, que representa 28% das mortes no mundo e 66% do total de doenças que acometem os brasileiros. Dessa forma, há um grande interesse em pesquisar formas de prevenção e tratamento dessas doenças. Algumas medidas são relevantes no auxílio de diagnóstico, tal como: tamanho médio dos ramos, diâmetro médio das seções transversais dos vasos e padrões de divisão de ramos. Calcular essas medidas de forma manual é uma tarefa demorada e trabalhosa. Assim, esta Tese tem como objetivo, propor um método computacional de rastreamento e extração de atributos em redes vasculares a partir de imagens 3D de angiografia por ressonância magnética e por tomografia computadorizada. Trata-se de uma abordagem de rastreamento e identificação de bifurcações que difere das técnicas anteriores, utilizando a Transformada de Hough para identificar o diâmetro do vaso em cortes transversais num dado ponto ao longo de um vaso sanguíneo. Mais detalhadamente, essa abordagem utiliza um campo vetorial advindo do cálculo de uma matriz formada por derivadas parciais de segunda ordem, obtida da intensidade luminosa da imagem, para identificar a direção de um ramo de vaso. Além disso, durante o processo de rastreamento de um ramo de vaso, são calculados vários descritores de forma com o objetivo de classificar regiões como pertencentes a uma bifurcação ou não. Em adição a estes descritores, desenvolvemos uma nova medida chamada de variância do raio que permite distinguir, bifurcações, não-bifurcações e segmentos de vaso com stents (aparelho metálico usado para aumentar o diâmetro dos vasos). Para a classificação de bifurcações, criamos a medida de bifurcação, que trata-se de uma combinação linear de todos os descritores de forma apresentados neste trabalho. Testes foram realizados para atestar a eficácia da abordagem proposta, utilizando tanto imagens sintéticas quantoimagens reais. Os resultados mostraram que o método é capaz de rastrear 91% de uma rede vascular sintética variando o ponto de inicialização e 76% variando o nível de ruído. Também foi observado por meio de testes que o método proposto consegue rastrear vasos e identificar bifurcações em imagens reais sem avaliação numérica. Essa abordagem permite a extração da relação hierárquica entre os ramos em uma rede vascular e a extração do padrão de divisão dos vasos, o que contribui sobremaneira para o estudo do comportamento do fenômeno da angiogênese e no auxílio no diagnóstico de anomalias vasculares. / Vascular diseases are a main health problem, representing 28% of deaths worldwide and 66% of all diseases affecting the Brazilian population. Thus, it is important that researches in prevention and treatment of this type of disease increase. Moreover, there are several demands, such as computational tools capable of analyzing and extracting attributes from non-invasive images. The scope of this work is the analysis and extraction of data from magnetic resonance angiography and computed tomography angiography images by highlighting blood vessels. In this context, this thesis aims the development of a novel computational tracking and feature extraction method for vascular networks from 3D images. Our approach presents the following steps: First, identify the vessel cross-sections along it using the Hough transform. Then, compute a matrix composed of second order partial derivatives of image intensity to identify the direction of the vessel. Perform a feature analysis of the vessel contour to classify the bifurcation point, and finally, identify the direction of the new branch in a bifurcation point. The main contribution of this Thesis is the two new measures developed, called radius ratio and bifurcation measure, the radius ratio is capable to distinguish between a region with bifurcation, stents or without both of them. The bifurcation measure is a linear combination that allows to classify a region as bifurcation or not. Tests were performed in order to verify the proposed approach effectiveness, using both synthetic images and real images. The results showed the method is capable to track 91% of synthetic vascular networks varying the seed point and 76% varying the level of noise. Also, we performed tests in real images and by visual evaluation, we could observed that the proposed method was able to track vessels and identify bifurcations from different parts of the body. This approach allows to calculate, in the future, the density of bifurcations in a vascular network, the distance between them, the stenosis and aneurysms grading and characterize specific vessels. In addition, the vascular networks extraction allows the study of the angiogenesis phenomena and vascular anomalies.

Este trabalho discute a determinação de padrões respiratórios em sequências temporais de imagens obtidas por Ressonância Magnética (RM) e o seu uso no registro temporal de imagens coronais e sagitais. O registro é feito sem o uso de qualquer informação de sincronismo e qualquer gás especial para reforçar o contraste. As sequências temporais de imagens são adquiridas em respiração livre. O movimento real do pulmão nunca foi diretamente visto, pois é totalmente dependente dos músculos que o rodeiam. A visualização do pulmão em movimento é um tema atual de pesquisa na medicina. O movimento do pulmão não possui intervalos regulares e é suscetível a variações na respiração. Comparado à Tomografia Computadorizada (TC), a RM necessita de um maior tempo de aquisição e é preferível porque não envolve radiação. Como as sequências de imagens coronais e sagitais são ortogonais entre si, a sua intersecção corresponde a um segmento de reta no espaço tridimensional. O registro se baseia na análise deste segmento interseccional. A variação deste segmento de interseção no tempo pode ser enfileirada, definindo uma imagem espaço-temporal em duas dimensões (2DST). Supõe-se que o movimento diafragmático é o movimento principal de todas as estruturas do pulmão se movem quase que totalmente sincronicamente. A sincronização deste movimento é feita através de um padrão chamado função respiração. Este padrão é obtido através do processamento de uma imagem 2DST. Um algoritmo da transformada de Hough intervalar procura movimentos sincronizados na função respiração. O algoritmo de contornos ativos ajusta pequenas discrepâncias originadas por movimentos assíncronos nos padrões respiratórios . A saída é um conjunto de padrões respiratórios. Finalmente, a composição de imagens coronal e sagital que estão na mesma fase respiratória é realizada através da comparação de padrões respiratórios provenientes das superfícies de contorno superior e diafragmática. Quando disponíveis, os padrões respiratórios associados às estruturas internas do pulmão também são usados. Vários resultados e conclusões são apresentados. / This work addresses the determination of the breathing patterns in time sequence of images obtained from Magnetic Resonance (MR) and their use in the temporal registration of coronal and sagital images. The registration is done without the use of any triggering information and any special gas to enhance the contrast. The temporal sequences of images are acquired in free breathing. The real movement of the lung has never been seen directly, as it is totally dependent on its surrounding muscles and collapses without them. The visualization of the lung in motion is an actual topic of research in medicine. The lung movement is not periodic and it is susceptible to variations in the degree of respiration. Compared to Computerized Tomography (CT), MR imaging involves longer acquisition times and it is preferable because it does not involve radiation. As coronal and sagittal sequences of images are orthogonal to each other, their intersection corresponds to a segment in the three dimensional space. The registration is based on the analysis of this intersection segment. A time sequence of this intersection segment can be stacked, defining a two-dimension spatio-temporal (2DST) image. It is assumed that the diaphragmatic movement is the principal movement and all the lung structures move almost synchronously. The synchronization of this motion is performed through a pattern named respiratory function. This pattern is obtained by processing a 2DST image. An interval Hough transform algorithm searches for synchronized movements with the respiratory function. A greedy searches for synchronized movements with the respiratory function. A greedy active contour algorithm adjusts small discrepancies originated by asynchronous movements in the respiratory patterns. The output is a set of respiratory patterns. Finally, the composition of coronal and sagittal images that are in the same breathing phase is realized by comparing of respiratory patterns originated from diaphragmatic and upper boundary surfaces. When available, the respire tory patterns associated to lung internal structures are also used. Several results and conclusions are shown.

Cette thèse porte sur la segmentation supervisée d'un flux vidéo en fragments correspondant à des activités de la vie quotidienne. En différenciant geste, action et activité, cette thèse s'intéresse aux activités à haut niveau sémantique telles que "Cuisiner" ou "Prendre son repas" par opposition à des actions comme "Découper un aliment". Pour cela, elle s'appuie sur l'algorithme DOHT (Deeply Optimized Hough Transform), une méthode de l'état de l'art utilisant un paradigme de vote (par transformée de Hough). Dans un premier temps, nous adaptons l'algorithme DOHT pour fusionner les informations en provenance de différents capteurs à trois niveaux différents de l'algorithme. Nous analysons l'effet de ces trois niveaux de fusion et montrons son efficacité par une évaluation sur une base de données composée d'actions de la vie quotidienne. Ensuite, une étude des jeux de données existant est menée. Constatant le manque de vidéos adaptées à la segmentation et classification (détection) d'activités à haut niveau sémantique, une nouvelle base de données est proposée. Enregistrée dans un environnement réaliste et dans des conditions au plus proche de l'application finale, elle contient des vidéos longues et non découpées adaptées à un contexte de détection. Dans un dernier temps, nous proposons une approche hiérarchique à partir d'algorithmes DOHT pour reconnaître les activités à haut niveau sémantique. Cette approche à deux niveaux décompose le problème en une détection non-supervisée d'actions pour ensuite détecter les activités désirées. / This thesis focuses on supervised activity segmentation from video streams within application context of smart homes. Three semantic levels are defined, namely gesture, action and activity, this thesis focuses mainly on the latter. Based on the Deeply Optimized Hough Transform paridigm, three fusion levels are introduced in order to benefit from various modalities. A review of existing action based datasets is presented and the lack of activity detection oriented database is noticed. Then, a new dataset is introduced. It is composed of unsegmented long time range daily activities and has been recorded in a realistic environment. Finaly, a hierarchical activity detection method is proposed aiming to detect high level activities from unsupervised action detection.

<p>This thesis states that cracks and scars, referred to as Secondary Creases, in fingerprint images can be used as means for aiding and complementing fingerprint recognition, especially in cases where there is not enough clear data to use traditional methods such as minutiae based or correlation techniques. A Gabor filter bank is used to extract areas with linear patterns, where after the Hough Transform is used to identify secondary creases in a r, theta space. The methods proposed for Secondary Crease extraction works well, and provides information about what areas in an image contains usable linear pattern. Methods for comparison is however not as robust, and generates False Rejection Rate at 30% and False Acceptance Rate at 20% on the proposed dataset that consists of bad quality fingerprints. In short, our methods still makes it possible to make use of fingerprint images earlier considered unusable in fingerprint recognition systems.</p>

An Inverted double pendulum is a combination of two individual pendulums which represents an example of a nonlinear and unstable dynamic system and it is also a good example of a physical system which can exhibit chaotic behavior.This document contains a first analysis of the model and the control of this system. Also presented is the installation of the electrical materials needed to control the system contain instrumenting the motor, current measurement system, motor shaft angle sensor, vision systemand MYRIO which is an embedded hardware device created by National Instruments will be used for data acquisition and control the system

Steered Response Power based algorithms are widely used for finding sound source location using microphone array systems. SRCP-PHAT is one such algorithm that has a robust performance under noisy and reverberant conditions. The algorithm creates a likelihood function over the field of view. This thesis employs image processing methods on SRCP-PHAT images, to exploit the difference in power levels and pixel patterns to discriminate between sound source and background pixels. Hough Transform based ellipse detection is used to identify the sound source locations by finding the centers of elliptical edge pixel regions typical of source patterns. Monte Carlo simulations of an eight microphone perimeter array with single and multiple sound sources are used to simulate the test environment and area under receiver operating characteristic (ROCA) curve is used to analyze the algorithm performance. Performance was compared to a simpler algorithm involving Canny edge detection and image averaging and an algorithms based simply on the magnitude of local maxima in the SRCP image. Analysis shows that Canny edge detection based method performed better in the presence of coherent noise sources.