Comparison of Traditional Image Segmentation Techniques and Geostatistical Threshold

Kerwin, Matthew (2006) Comparison of Traditional Image Segmentation Techniques and Geostatistical Threshold. Other thesis, James Cook University.

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A general introduction to image segmentation is provided, including a detailed description of common classic techniques: Otsu’s threshold, k-means and fuzzy c-means clustering; and suggestions of ways in which these techniques have been subsequently modified for special situations.

Additionally, a relatively new approach is described, which attempts to address certain exposed failings of the classic techniques listed by incorporating a spatial statistical analysis technique commonly used in geological studies.

Results of different segmentation techniques are calculated for various images, and evaluated and compared, with deficiencies explained and suggestions for improvements made.

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ID Code: 99764
Item Type: Thesis (Other)
Refereed: No
Keywords: image segmentation, image processing, Otsu's threshold, k-means clustering, fuzzy c-means clustering, geostatistical threshold
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Institution: James Cook University
Copyright Owner: Matthew Kerwin
Deposited On: 07 Oct 2016 00:33
Last Modified: 09 Jan 2017 06:48

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