A Graph-Based Approach to the Segmentation of Images with Mould Filled Foam Matrices

• Autorzy: Gocławski J. , Sekulska-Nalewajko J.
• Języki publikacji: EN

Abstrakty

EN

In the paper a method of MST (minimum spanning tree) graph-based image segmentation supported by parallel GPUprocessing has been presented. It has been applied to images of foam matrices filled with mould fungi of Mucor genus. The aim of the segmentation was to extract the profiles of the matrix objects from image background. Parallel computing methods were used to the initial image filtering, graph edge array building and sorting. The results obtained with graph based method both with and without parallel processing have been compared with conventional analysis in MATLAB environment. It has been verified that GPU processing clearly reduce execution time of the algorithm.

Słowa kluczowe

EN

graphics processing unit (GPU); MST applications; image segmentation

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2012
• Tom: Vol. 17, no. 4
• Strony: 59--70
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Gocławski J.

• Institute of Applied Computer Science, Lodz University of Technology, Lodz, Poland

autor

Sekulska-Nalewajko J.

• Institute of Applied Computer Science, Lodz University of Technology, Lodz, Poland

Bibliografia

• [1] Y. Boykov, M.P. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, International Conference on Computer Vision, Vol. 70, No. 2, pp. 109-131, 2006
• [2] S.A. Broughton, K.M. Bryan, Discrete Fourier Analysis and Wavelets: Applications to Signal and Image Processing, Wiley, ISBN: 978-0-470-29466-6, 2008
• [3] CUDPP google group: CUDPP CUDA Data Parallel Primitives Library. http://code.google.com/p/cudpp/
• [4] P. Felsenszwalb, D. Huttenlocher, Efficient graph based image segmentation, International Journal of Computer Vision, Vol. 59, No. 2, pp. 167-181, 2004
• [5] E.R. Gonzalez, R.E. Woods, S.L. Eddins, Digital Image Processing Using MATLAB, Prentice Hall, Upper Saddle River, NJ, 2004
• [6] R.C. Gonzalez, R.E. Woods, Digital image processing. 3-rd edition, Prentice Hall, 2007
• [7] R. Haralick, K. Shanmugam, I. Dinstein, Textural features for image classification, IEEE Transactions on Systems Man and Cybernetics, Vol. SMC-3, No. 6, pp. 610-621, 1973
• [8] D. Knuth, The Art of Computer Programming, Volume 3: Sorting and Searching. Third Edition, Addison-Wesley, ISBN 0-201-89685-0. Section 5.2.5: Sorting by Distribution, pp. 168-179, 1997
• [9] B.J. Kruskal, On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem, Proceedings of the American Mathematical Society, Vol. 7, No. 1, pp. 48-50, 1956
• [10] W. Malina, M. Smiatacz, Metody cyfrowego przetwarzania obrazów, Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2005
• [11] Nvidia Corporation: Nvidia Developer zone. http: //developer.nvidia.com/cuda-downloads
• [12] N. Otsu, A Threshold Selection Method from Gray- Level Histograms IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979
• [13] J. Sanders, T. Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, NVIDIA Corporation, 2011
• [14] J. Serra, Introduction to Mathematical Morphology, Computer Vision Graphics and Image Processing, Vol. 35, pp. 283-305, 1986
• [15] J. Shi, J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern analysis and Machine Intelligence, Vol. 22, No. 8, pp. 888-905, 2001
• [16] The Mathworks Inc.: Image processing toolbox user’s guide. http://www.mathworks.com/help/toolbox/images/ref/f3-23960.html
• [17] The Mathworks Inc.: MEX-files Guide. http://www.mathworks.com/support/technotes/1600/1605.html
• [18] L. Vincent, P. Soille, Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, pp. 583-598, June 1991
• [19] J. Wassenberg, W. Middelmann, An efficient parallel algorithm for graph-based image segmentation, Computer Analysis of Images and Patterns, LNCS, Vol. 5702, pp. 1003-1010, 2010