Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xvii, 170 p.; also includes graphics. Includes bibliographical references (p. 160-170). Available online via OhioLINK's ETD Center

Thesis (M.S.)--Ohio University, June, 2004. / Title from PDF t.p. Includes bibliographical references (p. 136-138)

Thesis (Ph.D.)--Montana State University--Bozeman, 2006. / Typescript. Chairperson, Graduate Committee: Steven R. Shaw. Includes bibliographical references (leaves 115-119).

Thesis (Ph. D.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 2, 2007) Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references (p.137-141). Also available online,

This thesis investigates the viability of applying a shape-from-shading technique to SAR imagery. A shape-from-shading algorithm is derived and tested on a single site for which both a Seasat SAR image and Digitial Elevation Model (DEM) were available. The shape-from-shading technique used in this thesis follows an approach proposed by Frankot and Chellappa for processing slant range SAR imagery. The algorithm incorporates a one-step technique for projecting non-integrable surface orientation estimates onto an integrable set in the frequency domain along with the iterative convergent shape-from-shading algorithm of Brooks and Horn. The significant issues and choices made in implementing the shape-from-shading algorithm and in preparing the SAR data and DEM are discussed. The shape-from-shading algorithm was applied to both the test site SAR image and images synthesized from the DEM. Reflectance models were derived from the SAR image and DEM. By quantitatively comparing the shape-from-shading results with the initial conditions used for the experiments, it was found that the algorithm produced substantially better results when applied to the synthesized images; however, when applied to the SAR image, there was no significant improvement over the initial conditions. / Science, Faculty of / Computer Science, Department of / Graduate

No description available.

Dissertation submitted for Masters of Science degree in Mathematical Statistics in the FacultyofScience, SchoolofStatisticsandActuarialScience, University of the Witwatersrand Johannesburg, January 2018 / This dissertation focuses on demonstrating the use of extreme value theory in modelling temperature in South Africa. The purpose of modelling temperature is to investigate the frequency of occurrences of extremely low and extremely high temperatures and how they influence the demand of electricity over time. The data comprise a time series of average hourly temperatures that are collected by the South African Weather Service over the period 2000−2010 and supplied by Eskom. The generalized extreme value distribution (GEVD) for r largest order statistics is fitted to the average maximum daily temperature (non-winter season) using the maximum likelihood estimation method and used to estimate extreme high temperatures which result in high demand of electricity due to use of cooling systems. The estimation of the shape parameter reveals evidence that the Weibull family of distributions is an appropriate fit to the data. A frequency analysis of extreme temperatures is carried out and the results show that most of the extreme temperatures are experienced during the months January, February, November and December of each year. The generalized Pareto distribution (GPD) is firstly used for modelling the average minimum daily temperatures for the period January 2000 to August 2010. A penalized regression cubic smoothing spline is used as a time varying threshold. We then extract excessesabovethecubicregressionsmoothingsplineandfitanon-parametricmixturemodel to get a sufficiently high threshold. The data exhibit evidence of short-range dependence and high seasonality which lead to the declustering of the excesses above the threshold and fit the GPD to cluster maxima. The estimate of the shape parameter shows that the Weibullfamilyofdistributionsisappropriateinmodellingtheuppertailofthedistribution. The stationary GPD and the piecewise linear regression models are used in modelling the influence of temperature above the reference point of 22◦C on the demand of electricity. The stationary and non-stationary point process models are fitted and used in determining the frequency of occurrence of extremely high temperatures. The orthogonal and the reparameterizationapproachesofdeterminingthefrequencyandintensityofextremeshave i been used to establish that, extremely hot days occur in frequencies of 21 and 16 days per annum, respectively. For the fact that temperature is established as a major driver of electricity demand, this dissertation is relevant to the system operators, planners and decision makers in Eskom and most of the utility and engineering companies. Our results are furtherusefultoEskomsinceitisduringthenon-winterperiodthattheyplanformaintenance of their power plants. Modelling temperature is important for the South African economy since electricity sector is considered as one of the most weather sensitive sectors of the economy. Over and above, the modelling approaches that are presented in this dissertation are relevant for modelling heat waves which impose several impacts on energy, economy and health of our citizens. / XL2018

Object localization is currently an active area of research in computer vision. The object localization task is to identify all locations of an object class within an image by drawing a bounding box around objects that are instances of that class. Object locations are typically found by computing a classification score over a small window at multiple locations in the image, based on some chosen criteria, and choosing the highest scoring windows as the object bounding-boxes. Localization methods vary widely, but there is a growing trend towards methods that are able to make localization more accurate and efficient through the use of context. In this thesis, I investigate whether contextual relationships between related objects can be leveraged to improve localization efficiency through a reduction in the number of windows considered for each localization task. I implement a context-driven localization model and evaluate it against two models that do not use context between objects for comparison. My model constrains the search spaces for the target object location and window size. I show that context-driven methods substantially reduce the mean number of windows necessary for localizing a target object versus the two models not using context. The results presented here suggest that contextual relationships between objects in an image can be leveraged to significantly improve localization efficiency by reducing the number of windows required to find the target object.

This thesis describes an approach for accomplishing a pattern recognition task using conceptual graph theory and neural networks (NNs). The set of patterns to be recognized are the capital letters of six different fonts of the English alphabet, plus two shifted and six rotated versions of each. The letters are represented to the neural network on a 16x16 input grid (256 "sensor lines"). A standard classification encoding for such patterns is to use a 26-bit vector (26 lines at the NN's output), one bit corresponding to each letter. Experiments with such an encoding yielded results with poor generalization capability. A new encoding scheme was developed, based on the conceptual graph formalism. This entailed designing a set of concepts and a set of associated relations appropriate to the upper case letters of the English alphabet. From these, the following were developed: a conceptual graph representation for each letter, a connection matrix for each, and finally, a C-vector and an R-vector representation for each. The latter were used as the output encoding (21 bits) of the NN pattern recognizer. A feed-forward neural network with 256 inputs, 21 outputs, and 2 hidden layers was trained using the back-propagation- of-error algorithm. Results were significantly better than with the more standard. encoding. Generalization on fonts improved from 74% to 96%, generalization on rotations improved from 83% to 94%, and finally, generalization on shifts improved from 2% to 93%.