Thesis submitted in fulfilment of the requirements for the degree Doctor of Technology: Electrical Engineering in the Faculty of Engineering at the Cape Peninsula University of Technology 2014 / Three-dimensional (3D) artefact detection can provide the conception of vision and real time interaction of electronic products with devices. The orientation and interaction of electrical systems with objects can be obtained. The introduction of electronic vision detection can be used in multiple applications, from industry, in robotics and also to give orientation to humans to their immediate surroundings. An article covering holograms states that these images can provide information about an object that can be examined from different angles. The limitations of a hologram are that there must be absolute immobilization of the object and the image system. Humans are capable of stereoscopic vision where two images are fused together to provide a 3D view of an object. In this research, two digital images are used to determine the artefact position in space. The application of a camera is utilized and the 3D coordinates of the artefact are determined. To obtain the 3D position, the principles of the pinhole camera, a single lens as well as two image visualizations are applied. This study explains the method used to determine the artefact position in space. To obtain the 3D position of an artefact with a single image was derived. The mathematical formulae are derived to determine the 3D position of an artefact in space and these formulae are applied in the pinhole camera setup to determine the 3D position. The application is also applied in the X-ray spectrum, where the length of structures can be obtained using the mathematical principles derived. The XYZ coordinates are determined, a computer simulation as well as the experimental results are explained. With this 3D detection method, devices can be connected to a computer to have real time image updates and interaction of objects in an XYZ coordinate system. Keywords: 3D point, xyz-coordinates, lens, hologram

Cílem této práce je návrh nové techniky pro stabilizaci obrazu za pomoci hardwarové akcelerace prostřednictvím GPGPU. Využití této techniky umožnuje stabilizaci videosekvencí v reálném čase i pro video ve vysokém rozlišení. Toho je zapotřebí pro ulehčení dalšího zpracování v počítačovém vidění nebo v armádních aplikacích. Z důvodu existence vícerých programovacích modelů pro GPGPU je navrhnutý stabilizační algoritmus implementován ve třech nejpoužívanějších z nich. Jejich výkon a výsledky jsou následně porovnány a diskutovány.

On-board gimbal systems for camera stabilization in helicopters are typically based on linear models. Such models, however, are inaccurate due to system nonlinearities and complexities. As an alternative approach, artificial neural networks can provide a more accurate model of the gimbal system based on their non-linear mapping and generalization capabilities. This thesis investigates the applications of artificial neural networks to model the inertial characteristics (on the azimuth axis) of the inner gimbal in a gyro-stabilized multi-gimbal system. The neural network is trained with time-domain data obtained from gyro rate sensors of an actual camera system. The network performance is evaluated and compared with measured data and a traditional linear model. Computer simulation results show the neural network model fits well with the measured data and significantly outperforms a traditional model.

Podemos afirmar que deslocamentos da imagem em quadros consecutivos de uma seqüência de vídeo são causados por pequenas vibrações da câmera e/ou movimentos desejados pelo operador da câmera. A estabilização de imagem consiste no processo de remoção de pequenas vibrações que caracterizam movimentos indesejados de uma seqüência de imagens. Com este propósito, atualmente técnicas de processamento digital de vídeo vêm sendo comumente aplicadas na indústria eletrônica. No processo digital de estabilização de imagens são necessários métodos computacionais de estimação, de suavização e de correção de movimento, para os quais, existe uma grande variedade de técnicas de processamento. O emprego de uma técnica específica de processamento é determinado conforme o tipo de aplicação. Técnicas para a estimação de movimento como casamento de blocos (CB), e para a suavização de movimento como filtro de freqüência passa baixa, são freqüentemente encontradas na literatura. Este trabalho apresenta um sistema de estabilização digital de imagens em tempo real capturadas por uma câmera digital, estimando e compensando movimentos translacionais e rotacionais indesejados. / Undesirable shakes or jiggles, object motion within image or desirable motions caused by the camera operator causes image differences in consecutive frames of video sequences. The image stabilization consists of the process of removing inevitable and undesirable fluctuations, shakes and jiggles; with this purpose, nowadays digital processing techniques have been commonly applied in the electronic industry. On the digital processing of image stabilization, computational methods of estimation, smoothing and motion correction are necessary. In the literature various digital processing techniques for image stabilization are described, the most suitable technique should be chosen according to the kind of application. Techniques such as block matching used in motion estimation and low-pass filters used in motion smoothing are found in a great number of papers. This work presents a real time digital image stabilization system capable of stabilizing video sequences with undesirable translational and rotational displacements between frames.

Podemos afirmar que deslocamentos da imagem em quadros consecutivos de uma seqüência de vídeo são causados por pequenas vibrações da câmera e/ou movimentos desejados pelo operador da câmera. A estabilização de imagem consiste no processo de remoção de pequenas vibrações que caracterizam movimentos indesejados de uma seqüência de imagens. Com este propósito, atualmente técnicas de processamento digital de vídeo vêm sendo comumente aplicadas na indústria eletrônica. No processo digital de estabilização de imagens são necessários métodos computacionais de estimação, de suavização e de correção de movimento, para os quais, existe uma grande variedade de técnicas de processamento. O emprego de uma técnica específica de processamento é determinado conforme o tipo de aplicação. Técnicas para a estimação de movimento como casamento de blocos (CB), e para a suavização de movimento como filtro de freqüência passa baixa, são freqüentemente encontradas na literatura. Este trabalho apresenta um sistema de estabilização digital de imagens em tempo real capturadas por uma câmera digital, estimando e compensando movimentos translacionais e rotacionais indesejados. / Undesirable shakes or jiggles, object motion within image or desirable motions caused by the camera operator causes image differences in consecutive frames of video sequences. The image stabilization consists of the process of removing inevitable and undesirable fluctuations, shakes and jiggles; with this purpose, nowadays digital processing techniques have been commonly applied in the electronic industry. On the digital processing of image stabilization, computational methods of estimation, smoothing and motion correction are necessary. In the literature various digital processing techniques for image stabilization are described, the most suitable technique should be chosen according to the kind of application. Techniques such as block matching used in motion estimation and low-pass filters used in motion smoothing are found in a great number of papers. This work presents a real time digital image stabilization system capable of stabilizing video sequences with undesirable translational and rotational displacements between frames.

Avec l’intégration des appareils photos dans les appareils mobiles, leur démocratisation et la réduction de la taille de l’imageur, de l’optique et de la taille pixels, les photos sont de plus en plus sujettes au flou de bougé dû aux tremblements de la main. À cette tendance s’ajoute un accroissement constaté dans l’exigence de qualité d’image de la part des utilisateurs. Pour réduire ce flou, des systèmes de stabilisation d’image ont été développés. Néanmoins ceux-ci ne permettent pas de garantir la qualité de netteté des images et souffrent parfois d’une intégration limitée. En réponse à ces limitations, ces travaux de recherche proposent, d’une part, un modèle de tremblement physiologique permettant de simuler de manière fidèle les flous de bougé et, d’autre part, une étude sur la perception visuelle du flou permettant le développement d’une métrique de qualité. Enfin des architectures de stabilisations, exploitant ces nouveaux outils, sont proposées. Ces nouvelles architectures permettent de réduire le nombre de composants externes ainsi que de garantir la netteté des images stabilisées. / With the integration of cameras in mobile devices, their democratization and the reduction of the imager’s size, the optical system dimensions and the pixels miniaturization, the photos become more and more subject to motion blur due to the hand tremor. In addition, the requirements in terms of image quality become higher and higher. Hence, in order to reduce this blur, several image stabilization systems have been developed. Nevertheless, they cannot guarantee the sharpness quality of resulting images and in some cases, they show integration difficulties. In order to overcome these limitations, the research work presented in this thesis proposes, first of all, a physiological tremor model that aims to simulate realistic camera shake and secondly, presents a study on visual perception of blur. This study enables the development of a quality metric. Finally, stabilization algorithms and architectures exploiting these new tools are presented. These new architectures reduce the number of external components and ensure sharp stabilized images.

This thesis deals with digital image stabilization. It contains a brief overview of the problem and available methods for digital image stabilization. The aim was to design and implement image stabilization system in JAVA, which is designed for RapidMiner. Two new stabilization methods have been proposed. The first is based on the motion estimation and motion compensation using Full-search and Three-step search algorithms. The basis of the second method is the detection of object boundaries. The functionality of the proposed method was tested on video sequences with contain visible shake of the scene, which has beed created for this purpose. Testing results show that with the proper set of input parameters for the object border detection method, successful stabilization of the scene is achieved. The rate of error reduction between images is approximately about 65 to 85%. The output of the method is stabilized image sequence and a set of metadata collected during stabilization, which can be further processed in an environment of RapidMiner.

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