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Semi-Supervised Visual Texture Based Pattern Classification

Hudson, Richard Earl

Abstract Details

2012, Doctor of Philosophy, Case Western Reserve University, EECS - Electrical Engineering.
Automated visual classification becomes increasingly practical as cameras and computational power become more accessible. Already, many existing automated visual inspection methods are competitive with human levels of performance. This dissertation presents a semi-supervised visual classification algorithm based on Independent Component Analysis, Yan Karklin’s Hierarchical Bayesian Model algorithm and Bayes’ Theorem. Several problems of current interest, both in industry and the research literature, are used as example applications: mobile robotics, industrial inspection and mammography. Results are given demonstrating the effectiveness of this method.
Wyatt Newman, PhD (Advisor)
Frank Merat, PhD (Committee Member)
Michael Lewicki, PhD (Committee Member)
Peter Thomas, PhD (Committee Member)
157 p.

Recommended Citations

Citations

  • Hudson, R. E. (2012). Semi-Supervised Visual Texture Based Pattern Classification [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1339081444

    APA Style (7th edition)

  • Hudson, Richard. Semi-Supervised Visual Texture Based Pattern Classification. 2012. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1339081444.

    MLA Style (8th edition)

  • Hudson, Richard. "Semi-Supervised Visual Texture Based Pattern Classification." Doctoral dissertation, Case Western Reserve University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1339081444

    Chicago Manual of Style (17th edition)