Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 99-109). Also available in electronic version.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 55-60). Also available in electronic version.

Thesis (M.S.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 62-64)

Thesis (M.S.)--Oregon Graduate Center, 1985.

Thesis (Ph. D.)--University of Pennsylvania, 1982. / Includes index and bibliographic references.

Thesis (Ph. D.)--Biomedical Engineering, Georgia Institute of Technology, 2006. / Andrew Karellas, Ph.D., Committee Chair ; Ernest V. Garcia, Ph.D., Committee Member ; Xiaoping P. Hu, Ph.D., Committee Member ; John N. Oshinski, Committee Member.

Bibliography: leaves 100-110. This thesis addresses the structure from motion problem in computer vision.

Electroluminescence (EL) imaging is a fast and comparatively low-cost method for spatially resolved analysis of photovoltaic (PV) devices. A Silicon CCD or InGaAs camera is used to capture the near infrared radiation, emitted from a forward biased PV device. EL images can be used to identify defects, like cracks and shunts but also to map physical parameters, like series resistance. The lack of suitable image processing routines often prevents automated and setup-independent quantitative analysis. This thesis provides a tool-set, rather than a specific solution to address this problem. Comprehensive and novel procedures to calibrate imaging systems, to evaluate image quality, to normalize images and to extract features are presented. For image quality measurement the signal-to-noise ratio (SNR) is obtained from a set of EL images. Its spatial average depends on the size of the background area within the EL image. In this work the SNR will be calculated spatially resolved and as (background independent) averaged parameter using only one EL image and no additional information of the imaging system. This thesis presents additional methods to measure image sharpness spatially resolved and introduces a new parameter to describe resolvable object size. This allows equalising images of different resolutions and of different sharpness allowing artefact-free comparison. The flat field image scales the emitted EL signal to the detected image intensity. It is often measured through imaging a homogeneous light source such as a red LCD screen in close distance to the camera lens. This measurement however only partially removes vignetting the main contributor to the flat field. This work quantifies the vignetting correction quality and introduces more sophisticated vignetting measurement methods. Especially outdoor EL imaging often includes perspective distortion of the measured PV device. This thesis presents methods to automatically detect and correct for this distortion. This also includes intensity correction due to different irradiance angles. Single-time-effects and hot pixels are image artefacts that can impair the EL image quality. They can conceivably be confused with cell defects. Their detection and removal is described in this thesis. The methods presented enable direct pixel-by-pixel comparison for EL images of the same device taken at different measurement and exposure times, even if imaged by different contractors. EL statistics correlating cell intensity to crack length and PV performance parameters are extracted from EL and dark I-V curves. This allows for spatially resolved performance measurement without the need for laborious flash tests to measure the light I-V- curve. This work aims to convince the EL community of certain calibration- and imaging routines, which will allow setup independent, automatable, standardised and therefore comparable results. Recognizing the benefits of EL imaging for quality control and failure detection, this work paves the way towards cheaper and more reliable PV generation. The code used in this work is made available to public as library and interactive graphical application for scientific image processing.

No description available.

Repetitive low level image processing transformations can be performed at high speeds by SIMD arrays, DSP and dedicated VLSI devices. These strategies cannot be adppted with more complex and time consuming data dependent algorithms. A flexible and programmable component must be used, and the use of many such devices in parallel, using dynamic load balancing techniques, is necessary to enable acceptable execution performance to be obtained. The transputer is a powerful new microprocessor with unique on chip communications facilities. Together with the new parallel programming language, occam, the transputer was specifically designed for parallel processing applications. Large transputer networks can be used for computationally intensive applications. This work has investigated the use of transputers for performing image processing algorithms of all three levels of complexity. Techniques were devised and implemented for the execution of low, medium and high levels of image processing algorithms on a multi-transputer network. A software architecture using SUPPLY and DEMAND processes was designed, and dynamic work load balancing was achieved, operating on a ternary tree network of up to 32 transputers. Some 80 image processing algorithms were successfully implemented within the software architecture. In particular, the more complex operation of Feature Extraction was achieved using the multi-transputer system. The Features extracted, involving Convex Hull, Convex Hull Deficiencies, Areas and Perimeters, and Shape Factors were used to build a Feature Vector. The use of this Feature Vector in Scene Interpretation, to realise Learn and Recognise functions has been investigated. The results of the work clearly show that while the system proposed is not as effective at executing repetitive, data intensive transformations as methods mentioned earlier, it can execute more complex Feature Extraction and Scene Interpretation algorithms efficiently. An Efficiency of 85% was achieved for Convex Hull formation, using 32 transputers.