The Sun is of fundamental importance to life on earth and is studied by scientists from many disciplines. It exhibits phenomena on a wide range of observable scales, timescales and wavelengths and due to technological developments there is a continuing increase in the rate at which solar data is becoming available for study which presents both opportunities and challenges. Two satellites recently launched to observe the sun are STEREO (Solar TErrestrial RElations Observatory), providing simultaneous views of the SUN from two different viewpoints and SDO (Solar Dynamics Observatory) which aims to study the solar atmosphere on small scales and times and in many wavelengths. The STEREO and SDO missions are providing huge volumes of data at rates of about 15 GB per day (initially it was 30 GB per day) and 1.5 terabytes per day respectively. Accessing these huge data volumes efficiently at both high spatial and high time resolutions is important to support scientific discovery but requires increasingly efficient tools to browse, locate and process specific data sets. This thesis investigates the development of new technologies for processing information contained in multiple and overlapping images of the same scene to produce images of improved quality. This area in general is titled Super Resolution (SR), and offers a technique for reducing artefacts and increasing the spatial resolution. Another challenge is to generate 3D images such as Anaglyphs from uncalibrated pairs of SR images. An automated method to generate SR images is presented here. The SR technique consists of three stages: image registration, interpolation and filtration. Then a method to produce enhanced, near real-time, 3D solar images from uncalibrated pairs of images is introduced. Image registration is an essential enabling step in SR and Anaglyph processing. An accurate point-to-point mapping between views is estimated, with multiple images registered using only information contained within the images themselves. The performances of the proposed methods are evaluated using benchmark evaluation techniques. A software application called the SOLARSTUDIO has been developed to integrate and run all the methods introduced in this thesis. SOLARSTUDIO offers a number of useful image processing tools associated with activities highly focused on solar images including: Active Region (AR) segmentation, anaglyph creation, solar limb extraction, solar events tracking and video creation.

This thesis contributes to the topic of image-based feature localization and tracking in fluoroscopic (2D x-ray) image sequences. Such tracking is needed to automatically measure organ motion in cancer patients treated with radiation therapy. While the use of 3D cone-beam computed tomography (CBCT) images is a standard clinical practice for verifying the agreement of the patient's position to a plan, it is done before the treatment procedure. Hence, measurement of the motion during the procedure could improve plan design and the accuracy of treatment delivery. Using an existing CBCT imaging system is one way of collecting fluoroscopic sequences for such analysis. Since x-ray images of soft tissues are typically characterized with low contrast and high noise, radio-opaque fiducial markers are often inserted in or around the target. This thesis describes techniques that comprise a complete system for automated detection and tracking of the markers in fluoroscopic image sequences. One of the cornerstone design ideas in this thesis is the use of the 3D CBCT image of the patient, from which the markers can be extracted more easily, to initialize the tracking in the fluoroscopic image sequences. To do this, a specific marker-based image registration framework was proposed. It includes multiple novel techniques, such as marker segmentation and modelling, the marker enhancement filter, and marker-specific template image generation approaches. Through extensive experiments on testing data sets, these novel techniques were combined with appropriate state-of-the-art methods to produce a sleek, computationally efficient, fully automated system that achieved reliable marker localization and tracking. The accuracy of the system is sufficient for clinical implementation. The thesis demonstrates an application of the system to the images of prostate cancer patients, and includes examples of statistical analysis of organ motion that can be used to improve treatment planning. / Dissertation / Doctor of Philosophy (PhD) / This thesis presents the development of a software system that analyzes sequences of 2D x-ray images to automatically measure organ motion in patients undergoing radiation therapy for cancer treatment. The knowledge of motion statistics obtained from this system creates opportunities for patient-specific treatment design that may lead to a better outcome. Automated processing of organ motion is challenging due to the low contrast and high noise levels in the x-ray images. To achieve reliable detection, the proposed system was designed to make use of 3D cone-beam computed tomography images of the patient, where the features (markers) are easier to identify. This required the development of a specific image registration framework for aligning the images, including a number of novel feature modelling and image processing techniques. The proposed motion tracking approach was implemented as a complete software system that was extensively validated on phantom and patient studies. It achieved a level of accuracy and reliability that is suitable for clinical implementation.

Prior work has demonstrated that the sequential presentation of lineup members in eyewitness lineups can result in undesirable position effects. For example, some studies have shown that placing the suspect in later positions increases discriminability. However, the evidence for this late-position discriminability advantage is mixed and the processes by which the discriminability increase occurs are unclear. However, one theory in particular, diagnostic feature detection theory (DFDT) explicitly predicts a late-position discriminability increase. According to DFDT, because shared features across lineup members cannot be used as reliable recognition cues to guide identification, discounting these features from consideration improves recognition. In sequential lineups, when the suspect is in a later position, witnesses are exposed to more of these shared features and are expected to benefit from discounting. By contrast, when the suspect is in an earlier position, witnesses are exposed to fewer shared features, and hence do not have the same advantage under the assumptions of the DFDT framework. One reason for the mixed evidence across the literature might be due to variation in suspect-filler similarity relationship between lineup members across studies, which we expected would moderate late-position memory effects. With the above in mind, the primary goals of the present work were: (1) testing for position effects at different levels of suspect-filler (SF) similarity, which might help elucidate conflicting evidence from prior work, and (2) testing DFDT mechanisms by simultaneously manipulating both innocent-suspect-perpetrator (IS-P) similarity and SF similarity. We found no evidence for late-position increases in discriminability as predicted by DFDT; however, participants were more conservative in later positions, especially when SF similarity was low. Discriminability was most strongly influenced by IS-P similarity, and was maximized when both SF and IS-P similarity was low. Implications for theories of eyewitness memory, practical implications for policy recommendations, and future directions are discussed.

The wellbeing of horses is very important to their care takers, trainers, veterinarians, and owners. This thesis describes the development of a non-invasive image processing technique that allows for automatic detection and tracking of horse head and ear motion, respectively, in videos or camera feed, both of which may provide indications of horse pain, stress, or well-being. The algorithm developed here can automatically detect and track head motion and ear motion, respectively, in videos of a standing horse. Results demonstrating the technique for nine different horses are presented, where the data from the algorithm is utilized to plot absolute motion vs. time, velocity vs. time, and acceleration vs. time for the head and ear motion, respectively, of a variety of horses and ponies. Two-dimensional plotting of x and y motion over time is also presented. Additionally, results of pilot work in eye detection in light colored horses is also presented. Detection of pain in horses is particularly difficult because they are prey animals and have mechanisms to disguise their pain, and these instincts may be particularly strong in the presence of an unknown human, such as a veterinarian. Current state-of-the art for detecting pain in horses primarily involves invasive methods, such as heart rate monitors around the body, drawing blood for cortisol levels, and pressing on painful areas to elicit a response, although some work has been done for humans to sort and score photographs subjectively in terms of a "horse grimace scale." The algorithms developed in this thesis are the first that the author is aware for exploiting proven image processing approaches from other applications for development of an automatic tool for detection and tracking of horse facial indicators. The algorithms were done in common open source programs Python and OpenCV, and standard image processing approaches including Canny Edge detection Hue, Saturation, Value color filtering, and contour tracking were utilized in algorithm development. The work in this thesis provides the foundational development of a non -invasive and automatic detection and tracking program for horse head and ear motion, including demonstration of the viability of this approach using videos of standing horses. This approach lays the groundwork for robust tool development for monitoring horses non-invasively and without the required presence of humans in such applications as post-operative monitoring, foaling, evaluation of performance horses in competition and/or training, as well as for providing data for research on animal welfare, among other scenarios. / MS / There are many things that cause pain in horses, including improper saddle fit, inadequate care, laminitis, lameness, surgery, and colic, among others.The well-being of horses is very important to their care takers, trainers, veterinarians, and owners. Monitoring the well-being of horses is particularly important in many scenarios including post-operative monitoring, therapeutic riding programs, racing, dressage, and rodeo events, among numerous other activities. This thesis describes the development of a computer-based image processing technique for automatic detection and tracking of both horse head and ear motion, respectively, in videos of standing horses. The techniques developed here allow for the collection of data on head and ear motion over time, facilitating analysis of these motions that may provide reliable indicators of horse pain, stress, or well-being. Knowing if a horse is in pain is difficult because horses are prey animals that have mechanisms in place that minimize the display of pain so that they do not become easy targets for predators. Computer vision systems, like the one developed here, may be well suited to detect subtle changes in horse behavior for detecting distress in horses. The ability to remotely and automatically monitor horse well-being by exploiting computer-based image-processing techniques will create significant opportunities to improve the welfare of horses. The work presented here looks at the first use of image-processing approaches to detect and track facial features of standing horses in videos to help facilitate the development of automatic pain and stress detection in videos and camera feeds for owners, veterinarians, and horse-related organizations, among others.

The Sun is of fundamental importance to life on earth and is studied by scientists from many disciplines. It exhibits phenomena on a wide range of observable scales, timescales and wavelengths and due to technological developments there is a continuing increase in the rate at which solar data is becoming available for study which presents both opportunities and challenges. Two satellites recently launched to observe the sun are STEREO (Solar TErrestrial RElations Observatory), providing simultaneous views of the SUN from two different viewpoints and SDO (Solar Dynamics Observatory) which aims to study the solar atmosphere on small scales and times and in many wavelengths. The STEREO and SDO missions are providing huge volumes of data at rates of about 15 GB per day (initially it was 30 GB per day) and 1.5 terabytes per day respectively. Accessing these huge data volumes efficiently at both high spatial and high time resolutions is important to support scientific discovery but requires increasingly efficient tools to browse, locate and process specific data sets. This thesis investigates the development of new technologies for processing information contained in multiple and overlapping images of the same scene to produce images of improved quality. This area in general is titled Super Resolution (SR), and offers a technique for reducing artefacts and increasing the spatial resolution. Another challenge is to generate 3D images such as Anaglyphs from uncalibrated pairs of SR images. An automated method to generate SR images is presented here. The SR technique consists of three stages: image registration, interpolation and filtration. Then a method to produce enhanced, near real-time, 3D solar images from uncalibrated pairs of images is introduced. Image registration is an essential enabling step in SR and Anaglyph processing. An accurate point-to-point mapping between views is estimated, with multiple images registered using only information contained within the images themselves. The performances of the proposed methods are evaluated using benchmark evaluation techniques. A software application called the SOLARSTUDIO has been developed to integrate and run all the methods introduced in this thesis. SOLARSTUDIO offers a number of useful image processing tools associated with activities highly focused on solar images including: Active Region (AR) segmentation, anaglyph creation, solar limb extraction, solar events tracking and video creation.

Local feature detection and its applications in different problems are quite popular in vision research. In order to analyze a scene, its invariant features, which are distinguishable in many views of this scene, are used in pose estimation, object detection and augmented reality. However, required performance metrics might change according to the application type / in general, the main metrics are accepted as accuracy and computational complexity. The contributions in this thesis provide improving these metrics and can be divided into three parts, as local feature detection, local feature description and description matching in different views of the same scene. In this thesis an efficient feature detection algorithm with sufficient repeatability performance is proposed. This detection method is convenient for real-time applications. For local description, a novel local binary pattern outperforming state-of-the-art binary pattern is proposed. As a final task, a fuzzy decision tree method is presented for approximate nearest neighbor search. In all parts of the system, computational efficiency is considered and the algorithms are designed according to limited processing time. Finally, an overall system capable of matching different views of the same scene has been proposed and executed in a mobile platform. The results are quite promising such that the presented system can be used in real-time applications, such as augmented reality, object retrieval, object tracking and pose estimation.

In today's industry 3D cameras are often used to inspect products. The camera produces both a 3D model and an intensity image by capturing a series of profiles of the object using laser triangulation. In many of these setups a physical encoder is attached to, for example, the conveyor belt that the product is travelling on. The encoder is used to get an accurate reading of the speed that the product has when it passes through the laser. Without this, the output image from the camera can be distorted due to a variation in velocity. In this master thesis a method for integrating the functionality of this physical encoder into the software of the camera is proposed. The object is scanned together with a pattern, with the help of this pattern the object can be restored to its original proportions. / I dagens industri används ofta 3D-kameror för att inspektera produkter. Kameran producerar en 3D-modell samt en intensitetsbild genom att sätta ihop en serie av profilbilder av objektet som erhålls genom lasertriangulering. I många av dessa uppställningar används en fysisk encoder som återspeglar hastigheten på till exempel transportbandet som produkten ligger på. Utan den här encodern kan bilden som kameran fångar bli förvrängd på grund av hastighetsvariationer. I det här examensarbetet presenteras en metod för att integrera funktionaliteten av encodern in i kamerans mjukvara. För att göra detta krävs att ett mönster placeras längs med objektet som ska bli skannat. Mönstret återfinns i bilden fångad av kameran och med hjälp av detta mönster kan hastigheten bestämmas och objektets korrekta proportioner återställas.

Spectrum sensing is an important aspect of cognitive radio systems. In order to efficiently utilize the spectrum, the role of spectrum sensing is essential in cognitive radio networks. The transmitter detection based techniques: energy detection, cyclostationary feature detection, and matched filter detection, is most commonly used for the spectrum sensing. The Energy detection technique is implemented in the 2-hop cooperative cognitive radio network in which Orthogonal Space Time Block Coding (OSTBC) is applied with the Decode and Forward (DF) protocol at the cognitive relays. The Energy detection technique is simplest and gives good results at the higher Signal to Noise Ratio (SNR) values. However, at the low SNR values its performance degrades. Moreover, each transmitter detection technique has a SNR threshold, below which it fails to work robustly. This thesis aims to find the most reliable and accurate spectrum sensing technique in the 2-hop cooperative cognitive radio network. Using Matlab simulations, a comparative analysis of three transmitter detection techniques has been made in terms of higher probability of detection. In order to remove the shortcomings faced by all the three techniques, the Fuzzy-combined logic sensing approach is also implemented and compared with transmitter detection techniques. / Atti Ur Rehman (atti.rehmman@gmail.com) ph: +358-440458080

Ecocardiografia fetal é uma importante ferramenta para diagnóstico. Esta tese apresenta um método que provê localização automática de cavidades cardíacas em imagens ecocardiografias fetais, onde o diagnóstico de problemas congênitos do coração pode melhorar os resultados do tratamento. As estruturas de interesse são as quatro cavidades cardíacas (átrio direito, átrio esquerdo, ventrículo direito e ventrículo esquerdo). O método é baseado na busca por cavidades cardíacas através de uma molde de busca (template) para encontrar padrões de interesse. Este molde é calculado usando uma função densidade probabilidade que recebe como parâmetro os níveis de cinza de uma região representativa da cavidade, na imagem. Além disso, em alguns testes também foram utilizadas características espaciais da imagem para cálculo do molde de busca. Nesse sentido a busca é implementada de uma forma hierárquica: (i) primeiro, é localizada a região do coração; e (ii) em seguida, baseando na região do coração a cavidade de interesse á buscada. A comparação do molde de busca e as regiões de interesse na imagem é feita utilizando o Coeficiente de Bhattacharyya, o qual é analisado ao longo dos testes para justificar sua escolha. Uma das principais características do método é a invariância a rotação apresentada pelas estruturas. / Fetal echocardiography is an important tool for diagnosing. This thesis presents a method to provide automatic localization of cardiac cavities in fetal echocardiography images, where the early diagnostics of heart congenital diseases can greatly improve results from medical treatment. The structures of interest are the four cardiac cavities (left and right atrium, left and right ventricle). The method is based in the search of cardiac structures with a mold to find the pattern of interest. This mold is calculated using a probability density function that receives as parameter the gray level of a representative image and also uses spatial features of the images to calculate the mold. A hierarchical search is performed: (i) first, the region of interest is covered to locate the heart; and (ii) based on the position of the heart, the desired structure is found in the image. The comparison of the mold and the candidate image is made using the Bhattacharyya coefficient, which our experimental tests have shown good results. One of the main characteristics of the method is its rotation invariance.

Ecocardiografia fetal é uma importante ferramenta para diagnóstico. Esta tese apresenta um método que provê localização automática de cavidades cardíacas em imagens ecocardiografias fetais, onde o diagnóstico de problemas congênitos do coração pode melhorar os resultados do tratamento. As estruturas de interesse são as quatro cavidades cardíacas (átrio direito, átrio esquerdo, ventrículo direito e ventrículo esquerdo). O método é baseado na busca por cavidades cardíacas através de uma molde de busca (template) para encontrar padrões de interesse. Este molde é calculado usando uma função densidade probabilidade que recebe como parâmetro os níveis de cinza de uma região representativa da cavidade, na imagem. Além disso, em alguns testes também foram utilizadas características espaciais da imagem para cálculo do molde de busca. Nesse sentido a busca é implementada de uma forma hierárquica: (i) primeiro, é localizada a região do coração; e (ii) em seguida, baseando na região do coração a cavidade de interesse á buscada. A comparação do molde de busca e as regiões de interesse na imagem é feita utilizando o Coeficiente de Bhattacharyya, o qual é analisado ao longo dos testes para justificar sua escolha. Uma das principais características do método é a invariância a rotação apresentada pelas estruturas. / Fetal echocardiography is an important tool for diagnosing. This thesis presents a method to provide automatic localization of cardiac cavities in fetal echocardiography images, where the early diagnostics of heart congenital diseases can greatly improve results from medical treatment. The structures of interest are the four cardiac cavities (left and right atrium, left and right ventricle). The method is based in the search of cardiac structures with a mold to find the pattern of interest. This mold is calculated using a probability density function that receives as parameter the gray level of a representative image and also uses spatial features of the images to calculate the mold. A hierarchical search is performed: (i) first, the region of interest is covered to locate the heart; and (ii) based on the position of the heart, the desired structure is found in the image. The comparison of the mold and the candidate image is made using the Bhattacharyya coefficient, which our experimental tests have shown good results. One of the main characteristics of the method is its rotation invariance.