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Two preprocessing techniques based on grey level and geometric thickness to improve segmentation results
Uppsala University, Interfaculty Units, Centre for Image Analysis. Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2006 (Swedish)In: Pattern Recognition Letters, ISSN 0167-8655, Vol. 27, no 3, 160-166 p.Article in journal (Refereed) Published
Abstract [en]

Two different techniques of performing preprocessing of an image to improve segmentation results are presented. The methods use the grey level thickness of the objects, in order to find the resulting image, by varying the size of a neighbourhood depending on the sum of the included grey levels. The first method, RW, uses the random walk of a particle, defined in the neighbourhood of the position of the particle. The resulting image holds the number of times the particle visits a pixel. Instead of randomization to find the number of visits, the second method, IP, scans the image iteratively and calculates the expected value of the same number. Three different kinds of real world applications are demonstrated to get better segmentation results with the preprocessing techniques included than without.

Place, publisher, year, edition, pages
2006. Vol. 27, no 3, 160-166 p.
Keyword [en]
Preprocessing; Segmentation; Random walk; Grey level thickness
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:uu:diva-74826OAI: oai:DiVA.org:uu-74826DiVA: diva2:102736
Available from: 2005-11-29 Created: 2005-11-29 Last updated: 2011-01-11

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