This study investigates methods that can be used for tracking features in computationalluid-dynamics datasets. The two approaches of overlap based feature tracking and attribute-based feature tracking are studied. Overlap based techniques use the actual degree of overlap between sucessive time steps to conclude a match. Attribute-based techniques use characteristics native to the feature being studied, like size, orientation, speed etc, to conclude a match between candidate features. Due to limitations on the number of time steps that can be held in a computer's memory, it may be possible to load only a time-subsampled data set. This might result in a decrease in the overlap obtained, and hence a subsequent decrease in the confidence of the match. This study looks into using specific attributes of features, like rotational velocity, linear velocity to predict the presence of that feature in a future time step. The use of predictive techniques is tested on swirling features, i.e., vortices. An ellipse-like representation is assumed to be a good approximation of any such feature. The location of a feature in previous time-steps are used to predict its position in a future time-step. The ellipse-like representation of the feature is translated over to the predicted location and aligned in the predicted orientation. An overlap test is then done. Use of predictive techniques will help increase the overlap, and subsequently the confidence in the match obtained. The techniques were tested on an artificial data set for linear velocity and rotation and on a real data set of simulation of flow past a cylinder. Regions of swirling flow, detected by computing the swirl parameter, were taken as features for study. The degree of overlap obtained by a basic overlap and by the use of predictive methods were tabulated. The results show that the use of predictive techniques improved the overlap.

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Cardiovascular disease is a leading cause of morbidity and mortality globally. Early diagnostic markers are gaining popularity for better patient care disease outcomes. There is an increasing interest in noninvasive cardiac imaging biomarkers to diagnose subclinical cardiac disease. Feature tracking cardiac magnetic resonance imaging is a novel post-processing technique that is increasingly being employed to assess global and regional myocardial function. This technique has numerous applications in structural and functional diagnostics. It has been validated in multiple studies, although there is still a long way to go for it to become routine standard of care.

When firing a handheld weapon, the shooter tries to aim at the point where he wants the bullet to hit. However, due to imperfections in the human body, this can be quite hard. The weapon moves relative to the target and the shooter has to use precise timing to fire the shot exactly when the weapon points to the intended target position. This can be very hard, especially when shooting at long range using a magnifying rifle scope. In this thesis, a solution to this problem using image analysis is described and tested. Using a digital video camera and software, the system helps the shooter to fire at the appropriate time. The system is designed to operate in real-time conditions on a PC. The tests carried out have shown that the solution is promising and helps to achieve better accuracy. However it needs to be optimized to run smoothly on a smaller scale embedded system. / Då en skytt avfyrar ett handhållet vapen försöker skytten sikta mot den punkt där han vill att kulan ska träffa. Eftersom den mänskliga kroppen inte är helt stabil kommer vapnet att röra sig runt denna punkt och skytten måste försöka avfyra skottet precis vid den tidpunkt då vapnet pekar mot rätt punkt. Detta är särskilt svårt vid stora avstånd, då små vinkelskillnader i vapnets pipa ger större utslag med ökande avstånd till målet.  I denna uppsats beskrivs och utvärderas ett system konstruerat för att minimera inverkan av de ofrivilliga rörelserna. Systemet använder sig av en videokamera monterad i siktet och en dator med mjukvara som utför analys och behandling av videoströmmen för att avgöra när vapnet bör avfyras. Tanken är att i ett färdigt system implementera algoritmen i ett portabelt inbyggt system som kan monteras i kikarsiktet tillsammans med kameran. Mjukvaran kan sedan styra avfyrningen elektroniskt efter att skytten gett sitt godkännande genom att lägga tryck på avtryckaren. Testen som genomförts visar att angreppssättet är lovande. Systemet fick i samtliga fall bättre resultat än då skyttarna avfyrade skott manuellt.

Made available in DSpace on 2014-06-12T15:55:49Z (GMT). No. of bitstreams: 2 arquivo2328_1.pdf: 4997289 bytes, checksum: 4012a5235a1aad082afe66cac56457eb (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A reconstrução 3D é uma área de pesquisa que consiste em recuperar modelos que representem com precisão e em 3D características de interesse de uma cena, através da extração de informações 3D a partir de imagens 2D. Estas informações podem ser relativas à estrutura de uma determinada cena, posicionamento e trajetória de câmeras, textura, dentre outras. Uma vez de posse de tais informações, podemos utilizá-las para os mais diversos fins, por exemplo, modelagem automática de objetos, sistemas de navegação autônoma de robôs, modelos computacionais de estruturas ou órgãos do corpo humano, posicionamento de elementos virtuais em cenas reais, dentre outros. Uma das formas mais difundidas de se realizar reconstrução 3D é utilizando sequências contíguas de imagens ou vídeos capturados por câmeras convencionais (monoculares). Neste tipo de reconstrução um dos desafios mais importantes é o rastreamento. Rastreamento é a capacidade de conseguir corresponder um conjunto de pontos em uma sequência de imagens, ou seja, dado um ponto A com coordenadas x e y, deve-se ser capaz de identificar o ponto A com coordenadas x e y na imagem seguinte da sequência, e que corresponde exatamente à mesma localidade da estrutura sendo rastreada. Neste contexto, o objetivo desta dissertação de mestrado foi avaliar os algoritmos de rastreamento mais utilizados para este propósito, ressaltando as características individuais de cada um deles e identificando as vantagens e limitações que possuem. Os resultados desta análise podem ser uma ferramenta de auxílio na escolha do algoritmo de rastreamento a ser utilizado quando do desenvolvimento de uma solução de reconstrução 3D, tendo como base o domínio do problema que se deseja atacar. Os três algoritmos analisados foram o SIFT, o KLT e outro Baseado em Similaridade. Foi desenvolvida uma ferramenta de reconstrução 3D baseada em SfM. Esta ferramenta foi utilizada para a coleta de resultados com o rastreamento sendo realizado com SIFT, KLT e Similaridade. Uma etapa importante deste processo foi a definição de um conjunto de métricas para a análise comparativa dos algoritmos. As características individuais de rastreamento de cada um deles trouxeram bons resultados em alguns dos cinco cenários utilizados. Porém, no geral, o rastreador que apresentou os melhores resultados foi o KLT. Uma análise detalhada sobre os resultados desses algoritmos quando empregados para reconstrução 3D é apresentada

In this thesis, findings in three areas of computer vision estimation are presented. First, an improvement to the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm is presented in which gyroscope data is incorporated to compensate for camera rotation. This improved algorithm is then compared with the original algorithm and shown to be more effective at tracking features in the presence of large rotational motion. Next, a deep neural network approach to depth estimation is presented. Equations are derived relating camera and feature motion to depth. The information necessary for depth estimation is given as inputs to a deep neural network, which is trained to predict depth across an entire scene. This deep neural network approach is shown to be effective at predicting the general structure of a scene. Finally, a method of passively estimating the position and velocity of constant velocity targets using only bearing and time-to-collision measurements is presented. This method is paired with a path planner to avoid tracked targets. Results are given to show the effectiveness of the method at avoiding collision while maneuvering as little as possible.

Computed tomographic colonography(CTC), also called virtual colonoscopy, uses CT scanning and computer post-processing to create two dimensional images and three dimensional virtual views inside of the colon. Computer-aided polyp detection(CAPD) automatically detects colonic polyps and presents them to the user in either a first or second reader paradigm, with a goal reducing examination time while increasing the detection sensitivity. During colonoscopy, the endoscopists use the colonoscope inside of a patient's colon to target potential polyps and validate CAPD found ones. However, there is no direct information linking between CT images and the real-time optical colonoscopy(OC) video provided during the operation, thus endoscopists need to rely largely on their past experience to locate and remove polyps. The goal of this research project is to study the feasibility of developing an image guided colonoscopy(IGC) system that combines CTC images, real-time colonoscope position measurements, and video stream to validate and guide the removal of polyps found in CAPD. System would ease polyp level validation of CTC and improve the accuracy and efficiency of guiding the endoscopist to the target polyps. In this research project, a centerline based matching algorithm has been designed to estimate, in real time, the relative location of the colonoscope in the virtual colonoscopy environment. Furthermore, the feasibility of applying online simultaneous localization and mapping(SLAM) into CT image guided colonoscopy has been evaluated to further improve the performance of localizing and removing the pre-defined target polyps. A colon phantom is used to provide a testing setup to assess the performance of the proposed algorithms. / Master of Science

This paper describes a complete algorithm to estimate the motion of a fixed-wing aircraft given a series of digitized flight images. The algorithm was designed for fixed-wing aircraft because carefully procured flight images and corresponding navigation data were available to us for testing. After image pre-processing, optic flow data is determined by automatically finding and tracking good features between pairs of images. The image coordinates of matched features are then processed by a rigid-object linear optic flow-motion estimation algorithm. Input factors are weighed to provide good testing techniques. Error analysis is performed with simulation data keeping these factors in mind to determine the effectiveness of the optic flow algorithm. The output of this program is an estimate of rotation and translation of the imaged environment in relation to the camera, and thereby the airplane. Real flight images from NASA test flights are used to confirm the accuracy of the algorithm. Where possible, the estimated motion parameters are compared with recorded flight instrument data to confirm the correctness of the algorithm. Results show that the algorithm is accurate to within a degree provided that enough optic flow feature points are tracked. / Master of Science