A method for real time tracking of non-rigid arbitrary objects is proposed in this study. The approach builds on and extends work on multidimensional color histogram based target representation, which is enhanced by spatial masking with a monotonically decreasing kernel profile prior to back-projection. The masking suppresses the influence of the background pixels and induces a spatially smooth target model representation suitable for gradient-based optimization. The main idea behind this approach is that an increase in the number of quantized feature spaces used to generate the target probability distribuition function during histogram back-projection can lead to improved target localization. Target localization is performed using the recursive Mean shift procedure, which climbs the underlying density graidients of the discrete data to find the mode (peak) of the distribution. Finally, the real time test cases, such as occlusion, target scale and orientation changes, varying illumination and background clutter, are demonstrated.

The aim of this thesis is a description and implementation of algorithms of the tracked objects in the video feed. This thesis introduces Mean shift and Continuously adaptive mean shift algorithms which represent category based on kernel tracking. For construction of a model is used a threedimensional color histogram whose construction is described in this thesis as well. The achievements of described algorithms are compared in the testing images sequences and evaluated in details.

In digital image correlation, an optical full-field analysis method that can determine displacements of an object under load, high-resolution images are preferable. One way to improve the resolution is to improve the camera hardware. This can be expensive, hence another way to enhance the image is by various image processing techniques increase the resolution of the image. There are several ways of doing this and these techniques are called super-resolution. In this thesisthe theory behind several different approaches to super-resolution is presented and discussed. The goal of this Thesis has been to investigate if super-resolutionis possible in a scene with moving objects as well as movement of the camera. It became clear early on that image registration, a step in many super-resolution methods that will be explained in this thesis, was of utmost importance, and a major part of the work became comparing image registration methods. Data has been recorded and then two different super-resolution algorithms have been evaluated on a data set showing that super-resolution is possible.

Indiana University-Purdue University Indianapolis (IUPUI) / The Electrical capacitance tomography (ECT) method has recently been adapted to obtain tomographic images of the cross section of a diesel particulate filter (DPF). However, a soot mass estimation algorithm is still needed to translate the ECT image pixel data to obtain soot load in the DPF. In this research, we propose an estimation method to quantify the soot load in a DPF through an inverse algorithm that uses the ECT images commonly generated by a back-projection algorithm. The grayscale pixel data generated from ECT is used in a matrix equation to estimate the permittivity distribution of the cross section of the DPF. Since these permittivity data has direct correlation with the soot mass present inside the DPF, a permittivity to soot mass distribution relationship is established first. A numerical estimation algorithm is then developed to compute the soot mass accounting for the mass distribution across the cross-section of the DPF as well as the dimension of the DPF along the exhaust flow direction. Firstly, ANSYS Electronic Desktop software is used to compute the capacitance matrix for different amounts of soot filled in the DPF, furthermore it also analyzed different soot distribution types applied to the DPF. The Analysis helped in constructing the sensitivity matrix which was used in the numerical estimation algorithm. Experimental data have been further used to verify the proposed soot estimation algorithm which compares the estimated values with the actual measured soot mass to validate the performance of the proposed algorithm.

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Void fraction is an essential parameter for understanding the interfacial structure, and heat and mass transfer mechanisms in various gas-liquid flow systems. It becomes critically important to accurately measure void fraction as advanced high fidelity two-phase flow models require high-quality validation data. However, void fraction measurement remains a challenging task to date due to the complexity and rapid-changing characteristic of the gas-liquid boundary flow structure. This study aims to develop an advanced void fraction measurement system based on x-ray and fast line detector technologies. The dissertation has covered the major components necessary to develop a complete measurement system. Spectral analysis of x-ray attenuation in two-phase flow has been performed, and a new void fraction model is developed based on the analysis. The newly developed pixel-to-radial conversion algorithm is capable of converting measured void fraction along with the detector array to the radial distribution in a circular pipe for a wide range of void fraction conditions. The x-ray system attains the radial distributions of key measurable factors such as void fraction and gas velocity. The data are compared with the double-sensor conductivity probe and gas flowmeter for various flow conditions. The results show reasonable agreements between the x-ray and the other measurement techniques. Finally, various 2-D tomography algorithms are implemented for the non-axisymmetric two-phase flow reconstruction. A comprehensive summary of classical absorption tomography for the two-phase flow study is provided. An in-depth sensitivity study is carried out using synthetic bubbles, aiming to investigate the effect of various uncertainty factors such as background noise, off-center shift, void profile effect, etc. The sensitivity study provides a general guideline for the performance of existing 2-D reconstruction algorithms. / Doctor of Philosophy / Gas-liquid flow phenomenon exists in an extensive range of natural and engineering systems, for example, hydraulic pipelines in a nuclear reactor, heat exchanger, pump cavitation, and boilers in the gas-fired power stations. Accurate measurement of the void fraction is essential to understand the behaviors of the two-phase flow phenomenon. However, measuring void fraction distribution in two-phase flow is a difficult task due to its complex and fast-changing interfacial structure. This study developed a comprehensive suite of the non-intrusive x-ray measurement techniques, and a pixel-to-radial conversion algorithm to process the line- and time-averaged void fraction information. The newly developed algorithm, called the Area-based Onion-Peeling (ABOP) method, can convert the pixel measurement to the radial void fraction distribution, which is more useful for studying and modeling axisymmetric flows. Various flow conditions are measured and evaluated for the benchmarking of the algorithm. Finally, classical 2-D reconstruction algorithms are investigated for the void fraction measurement in non-axisymmetric flows. A comprehensive summary of the performance of these algorithms for a two-phase flow study is provided. An in-depth sensitivity study using synthetic bubbles has been performed to examine the effect of uncertainty factors and to benchmark the algorithms for the non-axisymmetric flows.

X-Ray Computed Tomography is irreplaceable medical imaging system. Quantitative evaluation is day to day routine used for clean run of this imaging system. The master’s thesis is focused on quantitative evaluation of first and third generation X-Ray CT. First of all is about subjective and objective evaluation of space and energetic resolution. Space resolution is evaluated in space and frequency domain. Energetic resolution is represent by low contrast resolution method. Application “Kvantitativní hodnocení kvality CT RTG zobrazení” created for this thesis is used for creation of reconstruction image and quantitative evaluation. This application was created with consideration of its usage in subjects about image processing. The master’s thesis contains results of quantitative evaluation X-Ray CT created with this application and proposal of lab work.

Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the fatigue driver can be significant in the prevention of accidents. This thesis introduces three different methods towards the detection of drivers’ drowsiness based on yawning measurement. All three approaches involve several steps, including the real time detection of the driver’s face, mouth and yawning. The last approach, which is the most accurate, is based on the Viola-Jones theory for face and mouth detection and the back projection theory for measuring both the rate and the amount of changes in the mouth for yawning detection. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect the yawning state as a sign of a driver’s drowsiness.