The morphological image processing can modify the shapes of objects very efficiently by structure elements. Thus, the morphology processing has recently been applied to industry auto-inspection and medical image processing successfully. In this thesis, we incestigate the efficient processing of morphological image processing by two approaches: quadtree approach and paralell approach. By the quadtree decomposition, any binary image can be decomposed into black and white square blocks with some fixed size of power of 2. Thus, dilation of the whole image can be accomplished by dilating individual decomposed square blocks. On the other hand, any binary image can be presented by bit per pixel basis. Thus, we can exploit the parallel on a personal computer to speed up the set oriented morphological image processing. Experiments have revealed that both approach are much faster than the direct method. The quadtree approach are most advantageous for large structure elements. Whereas, the parralel approach are the fastest for the usual applications.

Morphological image processing is especially useful in the applications of medical image processing, pattern recognition, and industry auto-inspection. Special hardware for morphological image processing are very expensive. On the other hand, the speed of software are too slow. The purpose of this paper is to speed up the software computations of morphological image processing by parallel processing on Pentium machine. The morphological operation is similar to digital convolution. We can realize our parallel morphological operation on the Pentium machine by two different methods. They are output-decomposition and input-decomposition methods, similar to the procedure of overlap-and-save and overlap-and-add respectively. The above methods implemented on Pentium machine are proved very efficient with 64-bits parallelism. Our experimental results demonstrated they are twice faster than the 32-bits parallelism method. In addition to the simulation and the real time experiments, a set of theoretical formulas are derived to analyze our methods and are checking the actual measured time quite well.

Thesis (M. Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Nov. 27, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 104-108). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph.D.) -- University of Texas at Arlington, 2008.

Thesis (Ph.D.) -- University of Texas at Arlington, 2008.

Thesis (Ph. D.)--State University of New York at Buffalo, 1995. / "May 1995." Includes bibliographical references (p. 80-89). Also available in print.

Thesis (M.S.)--University of Florida, 2000. / Title from first page of PDF file. Document formatted into pages; contains ix, 41 p.; also contains graphics. Vita. Includes bibliographical references (p. 39-40).

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xvii, 172 p.; also includes graphics. Includes abstract and vita. Advisor: Dhabeleswar K. Panda, Dept. of Computer and Information Science. Includes bibliographical references (p. 167-172).

published_or_final_version / Computer Science / Master / Master of Philosophy