International Journal of Intelligent Information Systems
Volume 5, Issue 3-1, May 2016, Pages: 28-31
Received: Dec. 19, 2015;
Accepted: Dec. 25, 2015;
Published: Jun. 30, 2016
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Saloua Senhaji, Department of Physics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed ben Abdellah University, Fes, Morocco
Abdellah Aarab, Department of Physics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed ben Abdellah University, Fes, Morocco
Edge detection is one of the most commonly used operations in image analysis, and there are more algorithms in the literature for enhancing and detecting edges. Natural images contain both textured and untextured regions, so the cues of contour and texture are exploited simultaneously. In this paper, we present a new edge detection method for natural images using decomposition model. The main idea is to decompose image in to two image components (geometric and texture) obtained by the PDE. The edge detection is performed not on the original image but on its geometric components. Experimental results on a wide range of images are shown.
A New Edge Detection Using Decomposition Model, International Journal of Intelligent Information Systems. Special Issue: Smart Applications and Data Analysis for Smart Cities.
Vol. 5, No. 3-1,
2016, pp. 28-31.
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D. Mumford, S. M. Kosslyn, L. A. Hillger, and R. J. Hernstein, “Discriminating figure from ground: The role of edge detection and region growing,” in Proc. Nat. Acad. Sci. USA, vol. 84, 1987, pp. 7354–7358.
Canny, J. A computational approach to edge detection. IEEE Trans. Pat. Anal. Mach. Intell., 8(6):679–698,1986.
N. Ahuja. A transform for multiscale image segmentation by integrated edge and region detection. IEEE TPAMI,18(12):1211–1235, 1996.
L. Wolf, X. Huang, I. Martin, and D. Metaxas. Patch-based texture edges and segmentation. In ECCV, pages II: 481–493, 2006. 3.
J. Matthews. “An introduction to edge detection: The sobel edge detector,” Available at http://www.generation5.org/content/2002/im01.asp, 2002.
M.C. Shin, D. Goldgof, and K.W. Bowyer.“Comparison of Edge Detector Performance through Use in an Object Recognition Task”. Computer Vision and Image Understanding, vol. 84, no. 1, pp. 160-178, Oct. 2001.
R, Raskar; Tan, K-H; Feris, R.; Yu, J.; Turk, M., "Non-photorealistic Camera:Depth Edge Detection and Stylized Rendering Using Multi-Flash Imaging", ACM SIGGRAPH, August 2004
Y. Meyer. OscillatingPatternsinImageProcessingandNonlinear Evolution Equations. The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures. Vol. 22 of University Lecture Series, AMS, Providence, 2001.
A. Chambolle. An algorithm for Total Variation Minimization and application. Journal of Mathematical Imaging and vision 20:89-97, 2004.
J.F. Aujol, G. Aubert, L. Blanc-Féraud, and A. Chambolle. Decomposing an image: Application to textured images and SAR images. Rapport technique, Université de Nice Sophia-Antipolis, 2003.
L. A. Vese and S. J. Osher. Modeling textures with total variation minimization and oscillating patterns in image processing. Journal of Scientific Computing, 19(1-3), 2003, pp. 553-572.
S.J. Osher, A. Sole, and L. A. Vese,Image decomposition and restoration using total variation minimization and the H(-1) norm.multiscalemodelling and simulation, ASIAM interdisciplinary journal, 1(3): 349-370, 2003.
E. Sobel, Camera Models and Machine Perception, PhD thesis, Stanford Univ., 1970.