Gesture-based interaction has long been seen as a natural means of input for electronic presentation systems; however, gesture-based presentation systems have not been evaluated in real-world contexts, and the implications of this interaction modality are not known. This thesis describes the design and evaluation of Maestro, a gesture-based presentation system which was developed to explore these issues. This work is presented in two parts. The first part describes Maestro's design, which was informed by a small observational study of people giving talks; and Maestro's evaluation, which involved a two week field study where Maestro was used for lecturing to a class of approximately 100 students. The observational study revealed that presenters regularly gesture towards the content of their slides. As such, Maestro supports several gestures which operate directly on slide content (e.g., pointing to a bullet causes it to be highlighted). The field study confirmed that audience members value these content-centric gestures. Conversely, the use of gestures for navigating slides is perceived to be less efficient than the use of a remote. Additionally, gestural input was found to result in a number of unexpected side effects which may hamper the presenter's ability to fully engage the audience. The second part of the thesis presents a gesture recognizer based on discrete hidden Markov models (DHMMs). Here, the contributions lie in presenting a feature set and a factorization of the standard DHMM observation distribution, which allows modeling of a wide range of gestures (e.g., both one-handed and bimanual gestures), but which uses few modeling parameters. To establish the overall robustness and accuracy of the recognition system, five new users and one expert were asked to perform ten instances of each gesture. The system accurately recognized 85% of gestures for new users, increasing to 96% for the expert user. In both cases, false positives accounted for fewer than 4% of all detections. These error rates compare favourably to those of similar systems.

Pairwise sequence alignment is a fundamental problem in bioinformatics with wide applicability. This thesis presents three new algorithms for this well-studied problem. First, we present a new algorithm, RDA, which aligns sequences in small segments, rather than by individual bases. Then, we present two algorithms for aligning long genomic sequences: CAPE, a pairwise global aligner, and FEAST, a pairwise local aligner. RDA produces interesting alignments that can be substantially different in structure than traditional alignments. It is also better than traditional alignment at the task of homology detection. However, its main negative is a very slow run time. Further, although it produces alignments with different structure, it is not clear if the differences have a practical value in genomic research. Our main success comes from our local aligner, FEAST. We describe two main improvements: a new more descriptive model of evolution, and a new local extension algorithm that considers all possible evolutionary histories rather than only the most likely. Our new model of evolution provides for improved alignment accuracy, and substantially improved parameter training. In particular, we produce a new parameter set for aligning human and mouse sequences that properly describes regions of weak similarity and regions of strong similarity. The second result is our new extension algorithm. Depending on heuristic settings, our new algorithm can provide for more sensitivity than existing extension algorithms, more specificity, or a combination of the two. By comparing to CAPE, our global aligner, we find that the sensitivity increase provided by our local extension algorithm is so substantial that it outperforms CAPE on sequence with 0.9 or more expected substitutions per site. CAPE itself gives improved sensitivity for sequence with 0.7 or more expected substitutions per site, but at a great run time cost. FEAST and our local extension algorithm improves on this too, the run time is only slightly slower than existing local alignment algorithms and asymptotically the same.

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This thesis deals with scene level summarization of story-based videos. Two different approaches for story-based video summarization are investigated. The first approach probabilistically models the input video and identifies scene boundaries using the same model. The second approach models scenes and classifies scene types by evaluating likelihood values of these models. In both approaches, hidden Markov models are used as the probabilistic modeling tools. The first approach also exploits the relationship between video summarization and video production, which is briefly explained, by means of content types. Two content types are defined, dialog driven and action driven content, and the need to define such content types is emonstrated by simulations. Different content types use different hidden Markov models and features. The selected model segments input video as a whole. The second approach models scene types. Two types, dialog scene and action scene, are defined with different features and models. The system classifies fixed sized partitions of the video as either of the two scene types, and segments partitions separately according to their scene types. Performance of these two systems are compared against a iv deterministic video summarization method employing clustering based on visual properties and video structure related rules. Hidden Markov model based video summarization using content types enjoys the highest performance.

Thesis (M.Eng.(Computer Engineering)--University of Pretoria, 2008. / Summaries in Afrikaans and English. Includes bibliographical references.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Lee, Chin-Hui; Committee Member: Anthony Joseph Yezzi; Committee Member: Biing-Hwang (Fred) Juang; Committee Member: Mark Clements; Committee Member: Ming Yuan. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Dr. Chin-Hui Lee; Committee Member: Dr. Anthony Joseph Yezzi; Committee Member: Dr. Biing-Hwang (Fred) Juang; Committee Member: Dr. Mark Clements; Committee Member: Dr. Ming Yuan. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M. S.)--Music, Georgia Institute of Technology, 2009. / Committee Chair: Chordia, Parag; Committee Member: Freeman, Jason; Committee Member: Weinberg, Gil.

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.

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