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Abstrakty
A method has been proposed for the segmentation of monochromatic, digital images, which make direct reference to the topographic features of the image function surface. Extraction is based on local analysis of the image function surface by using an operator, which recognises topographical features of the image function surface and assigns appropriate labels for feature-carrying pixels. Modified Hough transform has been used for grouping these labels. Hough transform modification consists of adding mechanisms enabling to connect into more complex linear- and curvilinear structures the directly detected pieces of contours, which are formed by homogeneously labelled pixels (feature carriers).
Czasopismo
Rocznik
Tom
Strony
41--53
Opis fizyczny
Bibliogr. 9 poz.
Twórcy
autor
- Institute of Control and Information Engineering, Poznan University of Technology, 3a Piotrowo Street, 60-965 Poznan
autor
- Institute of Control and Information Engineering, Poznan University of Technology, 3a Piotrowo Street, 60-965 Poznan
Bibliografia
- [1] Gonzalez R. C., Woods R. E.: Digital image processing, Addison Wesley, 1992.
- [2] Kasiński A., Noriega L. A.: Image texture segmentation using microstructural features, Studia z Automatyki i Informatyki, vol. 23, 1998, pp. 125-145.
- [3] Zghal H., El Maghraby H. A.: Strong Extraction of brightness features for stereo analysis in robot vision, Proc. 5-th IASTED International Conference Robotics and Manufacturing May 29-31, 1997,Cancun, Mexico, pp. 171-174.
- [4] Bertrand G., Everat J. Ch., Couprie M.: Image segmentation through operators based on topology, J. Electronic Imaging, 6, 1997, pp. 395-405.
- [5] Yokoi S., Toriwaki J., Fukumura T.: An analysis of topological properties of digitized binary pictures using local features, Comp. Graphics and Image Processing, vol. 4, 1975, pp.63-73.
- [6] Kasiński A.: Smoothing noisy images without destroying predefined feature carriers, Computer Analysis of Images and Patterns, Proc. 7th International Conference, CAIP ’97, Kiel, Germany, September 1997, Springer, Berlin-Heidelberg, pp. 519-526.
- [7] Davies E. R: Machine Vision: Theory, Algoritms, Practicalities, Academic Press, 1990.
- [8] Gauch J., Pizer S.: Multiresolution analysis of ridges and valleys in grey-scale images, IEEE Transaction on Pattern Analysis and Machine Intelligence, 15(6), June 1993, pp. 635-646.
- [9] Haralick R.: Ridges and valleys on digital images, Computer Vision, Graphics and Image Processing, 1983, vol. 22, pp. 28-38.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD7-0027-0095