Thesis--Georg-August-Universität, 1913.

Approved for public release; distribution is unlimited / This thesis begins with a general survey of different model based systems for object recognition. The advantage and disadvantage of those systems are discussed. A system is then selected for study because of its effective Affine invariant matching [Ref. 1] characteristic. This system involves two separate phases, the modeling and the recognition. One is done off-line and the other is done on-line. A Hashing technique is implemented to achieve fast accessing and voting. Different test data sets are used in experiments to illustrate the recognition capabilities of this system. This demonstrates the capabilities of partial match, recognizing objects under similarity transformation applied to the models, and the results of noise perturbation. The testing results are discussed, and related experiences and recommendations are presented. / http://archive.org/details/affineinvarianto00hsut / Captain, Taiwan Republic of China Army

The volume of cargo flowing through todays transportation system is growing at an ever increasing rate. Recent studies show that 90% of all international cargo that enters the United States flows through our vast seaport system. When this cargo enters the US, time is of the essence to quickly obtain and verify its identity, screen it against an ever increasingly wide variety of security concerns, and ultimately correctly direct the cargo towards its final destination. Over the past few years, new port and container security initiatives and regulations have generated huge interest in the need for accurate real-time identification and tracking of incoming and outgoing traffic of vehicles and cargo. On the contrary, the manually intensive identification and tracking processes, typically employed today, are inherently both inefficient and inadequate, and can be seen as a possible enabling factor for potential threats to our ports and therefore our national security. The contradiction between current and required processes coupled to the correlation with accelerated growth in container traffic, has clearly identified the need for a solution. One heavily researched option is the utilization of video based systems implementing Optical Character Recognition (OCR) processes for automatically extracting the unique container identification code to expedite the flow of cargo through various points in the seaport. The actual current process of how this occurs along with the opportunities and challenges for adding such a technological solution will be investigated in great detail. This thesis will investigate the feasibility of application of motion compensation algorithms as an enhancement to OCR systems specifically designed to address the challenges of OCR of cargo containers in a seaport environment. This motion compensation could offer a cost effective alternative to the sophisticated hardware systems currently being offered to US ports.

No description available.

Hamburg, Univ., Diss., 2001. / Computerdatei im Fernzugriff.

Hamburg, Univ., Diss., 2001. / Computerdatei im Fernzugriff.

Thesis (Ph. D.)--Ohio State University, 2009. / Title from first page of PDF file. Includes vita. Includes bibliographical references (p. 124-125).

Hamburg, Universiẗat, Diss., 2001.

No description available.

Despite its recent advent, value at risk (VaR) became the most widely used technique for measuring future expected risk for both financial and non-financial institutions. VaR, the measure of the worst expected loss over a given horizon at a given confidence level, depends crucially on the distributional aspects of trading revenues. Existing VaR models do not capture adequately some empirical aspects of financial data such as the tail thickness, which is vital in VaR calculations. Tail thickness in financial variables results basically from stochastic volatility and event risk (jumps). Those two sources are not totally separated; under event risk, volatility updates faster than under normal market conditions. Generally, tail thickness is associated with hyper volatility updating. Existing VaR literature accounts partially for tail thickness either by including stochastic volatility or by including jump diffusion, but not both. Additionally, this literature does not account for fast updating of volatility associated with tail thickness. This dissertation fills the gap by developing analytical VaR models account for the total (maximum) tail thickness and the associated fast volatility updating. Those aspects are achieved by assuming that trading revenues are evolving according to a mixed non-affine stochastic volatility-jump diffusion process. The mixture of stochastic volatility and jumps diffusion accounts for the maximum tail thickness, whereas the nonaffine structure of stochastic volatility captures the fast volatility updating. The non-affine structure assumes that volatility dynamics are non-linearly related to the square root of current volatility rather than the traditional linear (affine) relationship. VaR estimates are obtained by deriving the conditional characteristic function, and then inverting it numerically via the Fourier Inversion technique to infer the cumulative distribution function. The application of the developed VaR models on a sample that contains six U.S banks during the period 1995-2002 shows that VaR models based on the non-affine stochastic volatility and jump diffusion process produce more reliable VaR estimates compared with the banks' own VaR models. The developed VaR models could significantly predict the losses that those banks incurred during the Russian crisis and the near collapse of the LTCM in 1998 when the banks' VaR models fail.