A New Edge Detection Using Decomposition Model

Saloua Senhaji; Abdellah Aarab

doi:doi:10.11648/j.ijiis.s.2016050301.14

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A New Edge Detection Using Decomposition Model

Saloua Senhaji, Abdellah Aarab

Received: 19 December 2015 Accepted: 25 December 2015 Published: 30 June 2016

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Abstract

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.

DOI	10.11648/j.ijiis.s.2016050301.14
Published in	International Journal of Intelligent Information Systems (Volume 5, Issue 3-1, May 2016) This article belongs to the Special Issue Smart Applications and Data Analysis for Smart Cities
Page(s)	28-31
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Partial Differential Equations, EDP, Decomposition Model, Geometrical Component, Edge Detector

References

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[2]	Canny, J. A computational approach to edge detection. IEEE Trans. Pat. Anal. Mach. Intell., 8(6):679–698,1986.
[3]	N. Ahuja. A transform for multiscale image segmentation by integrated edge and region detection. IEEE TPAMI,18(12):1211–1235, 1996.
[4]	L. Wolf, X. Huang, I. Martin, and D. Metaxas. Patch-based texture edges and segmentation. In ECCV, pages II: 481–493, 2006. 3.
[5]	J. Matthews. “An introduction to edge detection: The sobel edge detector,” Available at http://www.generation5.org/content/2002/im01.asp, 2002.
[6]	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.
[7]	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
[8]	Y. Meyer. OscillatingPatternsinImageProcessingandNonlinear Evolution Equations. The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures. Vol. 22 of University Lecture Series, AMS, Providence, 2001.
[9]	A. Chambolle. An algorithm for Total Variation Minimization and application. Journal of Mathematical Imaging and vision 20:89-97, 2004.
[10]	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.
[11]	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.
[12]	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.
[13]	E. Sobel, Camera Models and Machine Perception, PhD thesis, Stanford Univ., 1970.

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APA Style

Saloua Senhaji, Abdellah Aarab. (2016). A New Edge Detection Using Decomposition Model. International Journal of Intelligent Information Systems, 5(3-1), 28-31. https://doi.org/10.11648/j.ijiis.s.2016050301.14

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ACS Style

Saloua Senhaji; Abdellah Aarab. A New Edge Detection Using Decomposition Model. Int. J. Intell. Inf. Syst. 2016, 5(3-1), 28-31. doi: 10.11648/j.ijiis.s.2016050301.14

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AMA Style

Saloua Senhaji, Abdellah Aarab. A New Edge Detection Using Decomposition Model. Int J Intell Inf Syst. 2016;5(3-1):28-31. doi: 10.11648/j.ijiis.s.2016050301.14

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@article{10.11648/j.ijiis.s.2016050301.14,
  author = {Saloua Senhaji and Abdellah Aarab},
  title = {A New Edge Detection Using Decomposition Model},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {3-1},
  pages = {28-31},
  doi = {10.11648/j.ijiis.s.2016050301.14},
  url = {https://doi.org/10.11648/j.ijiis.s.2016050301.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2016050301.14},
  abstract = {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.},
 year = {2016}
}

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TY  - JOUR
T1  - A New Edge Detection Using Decomposition Model
AU  - Saloua Senhaji
AU  - Abdellah Aarab
Y1  - 2016/06/30
PY  - 2016
N1  - https://doi.org/10.11648/j.ijiis.s.2016050301.14
DO  - 10.11648/j.ijiis.s.2016050301.14
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 28
EP  - 31
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2016050301.14
AB  - 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.
VL  - 5
IS  - 3-1
ER  -

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Author Information

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

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APA Style

Saloua Senhaji, Abdellah Aarab. (2016). A New Edge Detection Using Decomposition Model. International Journal of Intelligent Information Systems, 5(3-1), 28-31. https://doi.org/10.11648/j.ijiis.s.2016050301.14

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ACS Style

Saloua Senhaji; Abdellah Aarab. A New Edge Detection Using Decomposition Model. Int. J. Intell. Inf. Syst. 2016, 5(3-1), 28-31. doi: 10.11648/j.ijiis.s.2016050301.14

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AMA Style

Saloua Senhaji, Abdellah Aarab. A New Edge Detection Using Decomposition Model. Int J Intell Inf Syst. 2016;5(3-1):28-31. doi: 10.11648/j.ijiis.s.2016050301.14

Copy | Download

@article{10.11648/j.ijiis.s.2016050301.14,
  author = {Saloua Senhaji and Abdellah Aarab},
  title = {A New Edge Detection Using Decomposition Model},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {3-1},
  pages = {28-31},
  doi = {10.11648/j.ijiis.s.2016050301.14},
  url = {https://doi.org/10.11648/j.ijiis.s.2016050301.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2016050301.14},
  abstract = {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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - A New Edge Detection Using Decomposition Model
AU  - Saloua Senhaji
AU  - Abdellah Aarab
Y1  - 2016/06/30
PY  - 2016
N1  - https://doi.org/10.11648/j.ijiis.s.2016050301.14
DO  - 10.11648/j.ijiis.s.2016050301.14
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 28
EP  - 31
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2016050301.14
AB  - 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.
VL  - 5
IS  - 3-1
ER  -

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