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High-precision boundary length estimation by utilizing gray-level information
Faculty of Engineering, University of Novi Sad, Serbia.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2009 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 31, no 2, 357-363 p.Article in journal (Refereed) Published
Abstract [en]

We present a novel method that provides an accurate and precise estimate of the length of the boundary (perimeter) of an object by taking into account gray levels on the boundary of the digitization of the same object. Assuming a model where pixel intensity is proportional to the coverage of a pixel, we show that the presented method provides error-free measurements of the length of straight boundary segments in the case of nonquantized pixel values. For a more realistic situation, where pixel values are quantized, we derive optimal estimates that minimize the maximal estimation error. We show that the estimate converges toward a correct value as the number of gray levels tends toward infinity. The method is easy to implement; we provide the complete pseudocode. Since the method utilizes only a small neighborhood, it is very easy to parallelize. We evaluate the estimator on a set of concave and convex shapes with known perimeters, digitized at increasing resolution. In addition, we provide an example of applicability of the method on real images, by suggesting appropriate preprocessing steps and presenting results of a comparison of the suggested method with other local approaches.

Place, publisher, year, edition, pages
2009. Vol. 31, no 2, 357-363 p.
Keyword [en]
Gray level, Length estimate, Perimeter, Pixel coverage, Quantization, Size and shape
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-88012DOI: 10.1109/TPAMI.2008.184ISI: 000261846800012OAI: oai:DiVA.org:uu-88012DiVA: diva2:134143
Available from: 2009-01-19 Created: 2009-01-19 Last updated: 2017-12-14Bibliographically approved

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IEEE Transaction on Pattern Analysis and Machine Intelligence
Computer Vision and Robotics (Autonomous Systems)

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