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You searched for +publisher:"University of Georgia" +contributor:("Suchendra Bhandarkar"). Showing records 1 – 3 of 3 total matches.

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University of Georgia

1. Huang, Jinling. Parallel computing for reconstructing physical maps of chromosomes.

Degree: MS, Computer Science, 2002, University of Georgia

This study designs and implements the parallel algorithms with several optimization approaches including simulated annealing, large step Markov chains (LSMC), evolutionary programming, and genetic algorithms, for a physical mapping problem based on the maximum likelihood estimator model. The parallel algorithms are implemented using a combination of inter-process communication via message passing and shared memory multithreaded programming and have provided good performance. Genetic algorithms using a heuristic crossover operator yields better results in terms of both solution accuracy and performance compared to the simulated annealing, LSMC and evolutionary programming approaches. Advisors/Committee Members: Suchendra Bhandarkar.

Subjects/Keywords: Physical Mapping

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, J. (2002). Parallel computing for reconstructing physical maps of chromosomes. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/huang_jinling_200208_ms

Chicago Manual of Style (16th Edition):

Huang, Jinling. “Parallel computing for reconstructing physical maps of chromosomes.” 2002. Masters Thesis, University of Georgia. Accessed October 16, 2019. http://purl.galileo.usg.edu/uga_etd/huang_jinling_200208_ms.

MLA Handbook (7th Edition):

Huang, Jinling. “Parallel computing for reconstructing physical maps of chromosomes.” 2002. Web. 16 Oct 2019.

Vancouver:

Huang J. Parallel computing for reconstructing physical maps of chromosomes. [Internet] [Masters thesis]. University of Georgia; 2002. [cited 2019 Oct 16]. Available from: http://purl.galileo.usg.edu/uga_etd/huang_jinling_200208_ms.

Council of Science Editors:

Huang J. Parallel computing for reconstructing physical maps of chromosomes. [Masters Thesis]. University of Georgia; 2002. Available from: http://purl.galileo.usg.edu/uga_etd/huang_jinling_200208_ms


University of Georgia

2. Chen, Feng. Similarity analysis of video sequences using an artificial neural network.

Degree: MS, Computer Science, 2003, University of Georgia

Comparison of video sequences is an important operation in many multimedia information systems. The similarity measure for comparison is based on some measure of correlation with the perceptual similarity (or difference) among video sequences or with the similarity (or difference) in some measure of semantics associated with the video sequences. In content-based similarity analysis, the video data are expressed in terms of different features. The similarity matching is then performed by quantifying the feature relationships between target video and query video shots, with either an individual feature or with a feature combination. In this study, two approaches are proposed for the similarity analysis of video shots. In the first approach, mosaic images are created from video shots, and the similarity analysis is done by examining the similarity amongst the mosaic images. In the second approach, the key frames are extracted for each video shot, and the similarity amongst video shots is examined by comparing the key frames of the video shots. The features extracted include image histograms, slopes, edges, and wavelets. Both individual features and feature combinations are used in similarity matching using an artificial neural network models. The similarity rank of query video shots is determined based on the coefficients of determination and mean absolute errors. Advisors/Committee Members: Suchendra Bhandarkar.

Subjects/Keywords: Video similarity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chen, F. (2003). Similarity analysis of video sequences using an artificial neural network. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/chen_feng_200305_ms

Chicago Manual of Style (16th Edition):

Chen, Feng. “Similarity analysis of video sequences using an artificial neural network.” 2003. Masters Thesis, University of Georgia. Accessed October 16, 2019. http://purl.galileo.usg.edu/uga_etd/chen_feng_200305_ms.

MLA Handbook (7th Edition):

Chen, Feng. “Similarity analysis of video sequences using an artificial neural network.” 2003. Web. 16 Oct 2019.

Vancouver:

Chen F. Similarity analysis of video sequences using an artificial neural network. [Internet] [Masters thesis]. University of Georgia; 2003. [cited 2019 Oct 16]. Available from: http://purl.galileo.usg.edu/uga_etd/chen_feng_200305_ms.

Council of Science Editors:

Chen F. Similarity analysis of video sequences using an artificial neural network. [Masters Thesis]. University of Georgia; 2003. Available from: http://purl.galileo.usg.edu/uga_etd/chen_feng_200305_ms


University of Georgia

3. Ling, Yangrong. Statistical dimension reduction methods for appearance-based face recognition.

Degree: MS, Computer Science, 2003, University of Georgia

Two novel moment-based methods which are insensitive to large variation in lighting direction and facial expression are developed for appearance-based face recognition using dimension reduction methods in statistics. The two methods are based on Sliced Inverse Regression (SIR) (Li, 1991) and Sliced Average Variance Estimate (SAVE) (Cook and Weisberg, 1991) and termed as the Sirface method and the Saveface method, respectively. The Sirface method estimates the mean di®erence subspace while the Saveface method estimates the mean and covariance di®erence subspace. They produce well-separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expression. In the subspace sense, the Sirface is equivalent to the Fisherface (Belhumeur et al., 1997) and the Saveface is even more comprehensive. Since both methods produce the “optimal” (smallest) image subspaces, they can lower both the error rate and the computational expense. Advisors/Committee Members: Suchendra Bhandarkar.

Subjects/Keywords: Dimension-reduction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ling, Y. (2003). Statistical dimension reduction methods for appearance-based face recognition. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms

Chicago Manual of Style (16th Edition):

Ling, Yangrong. “Statistical dimension reduction methods for appearance-based face recognition.” 2003. Masters Thesis, University of Georgia. Accessed October 16, 2019. http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms.

MLA Handbook (7th Edition):

Ling, Yangrong. “Statistical dimension reduction methods for appearance-based face recognition.” 2003. Web. 16 Oct 2019.

Vancouver:

Ling Y. Statistical dimension reduction methods for appearance-based face recognition. [Internet] [Masters thesis]. University of Georgia; 2003. [cited 2019 Oct 16]. Available from: http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms.

Council of Science Editors:

Ling Y. Statistical dimension reduction methods for appearance-based face recognition. [Masters Thesis]. University of Georgia; 2003. Available from: http://purl.galileo.usg.edu/uga_etd/ling_yangrong_200305_ms

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