With technological advances digital video and imaging are becoming more and more relevant. Medical, remote-learning, surveillance, conferencing and home monitoring are just a few applications of these technologies. Along with compression, there is now a need for analysis and extraction of data. During the days of film and early digital cameras the processing and manipulation of data from such cameras was transparent to the end user. This transparency has been decreasing and the industry is moving towards `smart users' - people who will be enabled to program and manipulate their video and imaging systems. Smart cameras can currently zoom, refocus and adjust lighting by sourcing out current from the camera itself to the headlight. Such cameras are used in the industry for inspection, quality control and even counting objects in jewelry stores and museums, but could eventually allow user defined programmability. However, all this will not happen without interactive software as well as capabilities in the hardware to allow programmability. In this research, compression, expansion and detail extraction from videos in the surveillance arena are addressed. Here, a video codec is defined that can embed contextual details of a video stream depending on user defined requirements creating a video summary. This codec also carries out motion based segmentation that helps in object detection. Once an object is segmented it is matched against a database using its shape and color information. If the object is not a good match, the user can either add it to the database or consider it an anomaly. RGB vector angle information is used to generate object descriptors to match objects to a database. This descriptor implicitly incorporates the shape and color information while keeping the size of the database manageable. Color images of objects that are considered `safe' are taken from various angles and distances (with the same background as that covered by the camera is question) and their RGB vector angle based descriptors constitute the information contained in the database. This research is a first step towards building a compression and detection system for specific surveillance applications. While the user has to build and maintain a database, there are no restrictions on the size of the images, zoom and angle requirements, thus, reducing the burden on the end user in creating such a database. This also allows use of different types of cameras and doesn't need a lot of up-front planning on camera location, etc.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1994. / Includes bibliographical references (leaves 159-166). / Microfiche. / xiv, 166 leaves, bound ill. 29 cm

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 83-85).

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Tsiotras, Panagiotis; Committee Member: Calise, Anthony J.; Committee Member: Egerstedt, Magnus; Committee Member: Prasad, J. V. R.; Committee Member: Russell, Ryan P.; Committee Member: Zhou, Hao-Min.

Thesis (M.S.)--Florida State University, 2005. / Advisor: Dr. Anke Meyer-Baese, Florida State University, College of Engineering, Dept. of Electrical Engineering. Title and description from dissertation home page (viewed June 15, 2005). Document formatted into pages; contains vii, 30 pages. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references.

Thesis (M.S. in Systems Technology (Joint Command, Control, Communications, Computers and Intelligence (JC4I))--Naval Postgraduate School, June 2005. / Thesis Advisor(s): William Kemple, John Osmundson. Includes bibliographical references (p. 59-60). Also available online.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 47-49). Also available in electronic version.

Orientador: Guido Costa Souza de Araujo / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-08T22:09:00Z (GMT). No. of bitstreams: 1 Borin_Edson_D.pdf: 1623538 bytes, checksum: 6e51b4bb1114ccaa088f88712c601000 (MD5) Previous issue date: 2007 / Resumo: Microprogramação é uma técnica comum no projeto de unidades de controle em processadores. Além de facilitar a implementação da unidade de controle, o microcódigo pode ser modificado para adicionar novas funcionalidades ou aplicar correções a projetos já existentes. À medida que novas funcionalidades são adicionadas à CPU, a área e o consumo de energia associados ao microcódigo também aumentam. Em um projeto recente de um processador da Intel, direcionado a baixo consumo de energia e área reduzida, estimou-se que a área e o consumo de energia associados ao microcódigo corresponderiam a 20% do total do chip. Neste trabalho, investigamos a utilização de técnicas de compressão para reduzir o tamanho do microcódigo. A partir das restrições impostas no projeto de processadores de alto desempenho, fizemos uma análise qualitativa das técnicas de compressão de código e microcódigo e mostramos que a compressão de microcódigo em dois níveis é a técnica mais adequada para se comprimir o microcódigo nesses processadores. Na compressão de microcódigo em dois níveis, as microinstruções são substituídas por apontadores para dicionários que armazenam os padrões de bits extraídos do microcódigo. Os apontadores são armazenados em uma ROM denominada ¿vetor de apontadores¿ e os padrões de bits residem em ROMs distintas, denominadas ¿dicionários¿. A técnica também permite que as colunas do microcódigo sejam agrupadas em conjuntos de forma a reduzir o número de padrões de bits nos dicionários. O agrupamento de colunas similares é fundamental para minimizar o número de padrões de bits nos dicionários e, conseqüentemente, maximizar a redução do tamanho do microcódigo. A principal contribuição desta tese é um conjunto de algoritmos para agrupar as colunas do microcódigo e maximizar a compressão. Resultados experimentais, com microcódigos extraídos de processadores em produção e em estágios avançados de desenvolvimento, mostram que os algoritmos propostosmelhoram de 6% a 20% os resultados obtidos com os outros algoritmos encontrados na literatura e comprimem o microcódigo em até 50% do seu tamanho original. Ainda neste trabalho, identificamos a necessidade de se comprimir o microcódigo com restrições no número de dicionários e na quantidade de colunas por dicionário. Também provamos que, com essas restrições, o agrupamento de colunas do microcódigo é um problema NP-Completo. Por fim, propomos um algoritmo para agrupar colunas sob estas restrições. Os resultados experimentaismostram que o algoritmo proposto é capaz de produzir bons resultados de compressão / Abstract: Microprogramming is a widely known technique used to implement processor control units. Microcode makes the control unit design process easier, as it can be modified to enhance functionality and to apply patches to an existing design. As more features get added to a CPU core, the area and power costs associated with the microcode increase. In a recent Intel internal design, targeted to low power and small footprint, the area and the power consumption costs associated with the microcode approached 20% of the total die. In this work, we investigate the use of compression techniques to reduce the microcode size. Based on the constraints imposed by high performance processor design, we analyze the existing microcode and code compression techniques and show that the two level microcode compression technique is the most appropriate to compress the microcode on high performance processor. This technique replaces the original microinstructions by pointers to dictionaries that hold bit patterns extracted from the microcode. The ¿pointer arrays¿ and the ¿dictionaries¿ are ROMs that store the pointers and the bit patterns, respectively. The technique allows the microcode columns to be grouped into clusters, so that the number of bit patterns inside the dictionaries is reduced. In order to maximize the microcode compression, similar columns must be grouped together. The main contribution of this thesis is a set of algorithms to group similar microcode columns into clusters, so as to maximize the microcode size reduction. Experimental results, using microcodes from production processors and processors in advanced development stages, show that the proposed algorithms improve from 6% to 20% the compression results found by previous works and compress the microcode to 50% of its original size. We show the importance of compressing microcode under design constraints such as the number of dictionaries and the number of columns per dictionary. We also prove that, under these constraints, the problem of grouping similar columns is NP-Complete. Finally, we propose an algorithm to group similar columns under such constraints. The experimental results show that the proposed algorithm provides good compression results / Doutorado / Arquitetura de Computadores / Doutor em Ciência da Computação