Technique for Coherent Segmentation of Image and Applications
Abstract
In this paper we describe a software tool created according to an algorithm, which was proposed earlier by authors for coherent and multi-scale segmentation of an image. The algorithm is based on the finding of all connected segments, the pixels of which belong to the same, determined beforehand and adjustable levels of intensity. Various modes of operation of the software are described, which allows to separate segments or to carry out full segmentation, as well as to transfer the parameters of one segment to others and to estimate quality of the carried out segmentation. It is shown that the segmentation procedure is able to determine simultaneously edges and contours in the image. The results of the computing experiments showing efficiency of developed system are given.
References
W.Pratt. Digital Image Processing. 3-rd ed., J.Wiley & Sons, Inc., N.Y., 2001.
I. Pitas. Digital Image Processing Fundamentals. Thessaloniki, 1998.
R. Gonzalez, R.Woods. Digital Image Processing, 2-nd edition, Prentice-Hall, Englewood Cliffs, NJ, 2002.
J. Canny. “A computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, 1986.
D. Asatryan, J. Patera. “Edge Detection Algorithm Based on DCT Continuous Extension Technique”. Proc. of XII Int. Conf. on Symmetry Methods in Physics. Yerevan, 2006, 10p. (To be printed).
Д.Асатрян, Г. Сажумян. “Об одном методе пороговой локальной сегментации изображения”. Mathematical Problems of Computer Science, IIAP, Yerevan, Armenia, 26, 2006, pp. 15-20.
Г. Сажумян. “Программная система для пороговой сегментации изображения”. Материалы годичной конференции ГИУА, 2006 г., 4 стр.
Д. Асатрян, Г. Сажумян. “Метод когерентной сегментации и его приложение к восстановлению поврежденных изображений”. Вестник ГИУА, сер. Моделирование, оптимизация, управление, вып. 9, т. 2, 2006 г., с. 15-21.
P. Arbel´aez and L. Cohen. Energy partitions and image segmentation. Technical report, CEREMADE, 2003.
J. Melonakos, R. Al-hakim,, J. Fallon, A. Tannenbaum. “Knowledge-basedsegmentation of brain MRI scans using the insight toolkit”. Insight Journal, 2005,http://hdl.handle.net/1926/44.
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