T. Karthikeyan,N. P. Revathy
The number of edges detected by proposed method has also been compared with existing methodologies such as cluster algorithms, WSVM,SVM and represented graphically in Fig. 6. The performance of the proposed methodology using HFCM-FNN is better than the existing edge detection algorithms such as clustering, SVM and WSVM.
A ground-truth image helps for deciding whether an edge is truly present or absent. The results of the edge detection algorithm will further decide the extent of detection of edge. The results of the low level pixel based comparison between the ground truth and the number of images of the images based on the following values is mentioned in the Table II.
TABLEII VARIABLE DEFINITION
Parameters Edge Non edge Edge True Positive (TP) False Positive (FP)
Non edge False Negative (FN) True Negative(TN)
From these measures the following measures were used to measure the result of the edge detection methods:
=
+ (26)
=
+ (27)
The sensitivity results of the proposed HFCM-FNN have also been compared with the edge detection methods such as WSVM and SVM. The graphical representation of comparison of sensitivity results is shown in Fig. 7. The results showed that the proposed HFCM-FNN is less sensitive than the existing edge detection methods such as WSVM and SVM. This is because of the ABF applied to this edge detection method thus resulting in high edge detection results.
Fig. 7. Sensitivity results comparison for edge detection methods
The specificity results of the proposed HFCM-FNN are higher than the existing edge detection methods such as WSVM and SVM. The reason is also attributed to the application of ABF and the higher performance of the edge detection results of the proposed system. Fig. 8 shows the graphical representation of comparison of specificity results with other edge detection methods such as HFCM-FNN, WSVM and SVM.
Fig. 8. Specificity results comparison for edge detection methods
V.
Conclusion
The improved edge detection method operating on Hybrid Fuzzy Cognitive Map based Fuzzy Neural Network(HFCM-FNN) was presented in this work to perform the pixel classification between edge and no edge. The disadvantages of the edge detection based on SVM and WSVM such as low efficiency, lack of transparency of results have been overcome by this method. The experimental results obtained by Hybrid Fuzzy Cognitive Map based Fuzzy Neural Network (HFCM-FNN) method of edge detection were proved to be efficient. The comparison of the proposed HFCM- FNN edge detection with existing edge detection algorithms such as SVM and WSVM confirmed the better performance of HFCM-FNN algorithm .
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Mathematical Sciences, Vol. 2
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W. Dai and K. Wang, An image edge detection algorithm based on local entropy, in Proceedings
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I. E. Sobel, Camera Models and Machine Perception, Ph.D. Thesis, Electrical Engineering Department, Stanford University, California, United States, 1970.
Wang , Li Wang ,Application of Improved Fuzzy Cognitive Map Based on Fuzzy Neural Network in Intrusion Detection , Journal of Information & Computational Science,
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667,2011).
Liu, H., Liang, L., Zhang, L., Submarine hydraulic rudder control system based on fuzzy logic algorithm, (2012)
Review on Computers and Software (IRE
Copyright © 2014 Praise Worthy Prize S.r.l. Information Sciences, Vol. XL
Ehsan Nadernejad, Sara Sharifzadeh, Hamid Hassanpour, Edge Detection Techniques: Evaluation
Mathematical Sciences, Vol. 2
Peter Meer, and Bogdan Georgescu, Edge Detection with Embedded Confidence,
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on Computer Scienc
Bing Wang ; DeptShaosheng Fan ,
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W. Dai and K. Wang, An image edge detection algorithm based on local entropy, in Proceedings
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JianWu, Qingwei Cao, An OWA operator based approach to aggregate group opinion by similarity degree, Fourth International Conference on Business Intelligence and Financial Engineering
667,2011).
Liu, H., Liang, L., Zhang, L., Submarine hydraulic rudder control system based on fuzzy logic algorithm, (2012)
Review on Computers and Software (IRE
Copyright © 2014 Praise Worthy Prize S.r.l. Information Sciences, Vol. XL
Ehsan Nadernejad, Sara Sharifzadeh, Hamid Hassanpour, Edge Detection Techniques: Evaluation
Mathematical Sciences, Vol. 2
Peter Meer, and Bogdan Georgescu, Edge Detection with Embedded Confidence,
and Machine Intelligence
Mamta Juneja , Parvinder Singh Sandhu ,Performance Evaluation of Edge Detection Techniques for Images in Spatial Domain
International Journal of Computer Theory and Engineering,
1, No. 5, December, 2009 pp.1793
a, Krishna Kant Singh and M.J.Nigam. Article: A Novel Algorithm for Impulse Noise Removal and Edge
International Journal of Computer Applications
,pp.30-34, January 2012. Published by Foundation of Computer Science, New York, USA.
atika Pradhan, Prasanna Pradhan, Reshmi Bhattacharjee, Divyendra Singh ,Edge Detection using Morphological Operator: A New Approach , International Journal of Advanced Research
in Computer Science and Software Engineering ,Vol.4,
bruary 2014.
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Jing Tian ; Weiyu Yu ; Shengli Xie ,An ant colony optimization lgorithm for image edge detection,
Evolutionary Computation
Marius Leordeanu
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ECCV 2008 pp 43 Chuan Tai ; Shih
noise estimation using Laplacian operator and adaptive edge
International Symposium on Communications, Control and Signal Processing ( pp. 1077
Wenshuo Gao ; Xiaoguang Zhang ; Lei Yang ; Huizhong Liu ,
improved Sobel edge detection,
on Computer Science and Information Technology (Vol.5 ,pp. 67 Bing Wang ; DeptShaosheng Fan ,
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Zuofeng, , Liu Jing3,c , Cao Jianzhong , Wang Hao, Yan Aqi , Wu Dengshan , Zhang Hui and Tang Linao,
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W. Dai and K. Wang, An image edge detection algorithm based on local entropy, in Proceedings
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667,2011).
Liu, H., Liang, L., Zhang, L., Submarine hydraulic rudder control system based on fuzzy logic algorithm, (2012)
Review on Computers and Software (IRE
Copyright © 2014 Praise Worthy Prize S.r.l. Information Sciences, Vol. XL
Ehsan Nadernejad, Sara Sharifzadeh, Hamid Hassanpour, Edge Detection Techniques: Evaluation
Mathematical Sciences, Vol. 2
Peter Meer, and Bogdan Georgescu, Edge Detection with Embedded Confidence, IEEE Transactions on Pattern Analysis
and Machine Intelligence
Mamta Juneja , Parvinder Singh Sandhu ,Performance Evaluation of Edge Detection Techniques for Images in Spatial Domain
International Journal of Computer Theory and Engineering,
1, No. 5, December, 2009 pp.1793
a, Krishna Kant Singh and M.J.Nigam. Article: A Novel Algorithm for Impulse Noise Removal and Edge
International Journal of Computer Applications
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atika Pradhan, Prasanna Pradhan, Reshmi Bhattacharjee, Divyendra Singh ,Edge Detection using Morphological Operator:
International Journal of Advanced Research in Computer Science and Software Engineering ,Vol.4,
bruary 2014.
Sheng Yi, Labate, D. ; Easley, G.R. ; Krim, H ,A Shearlet Approach to Edge Analysis and Detection,
, Vol.1
Jing Tian ; Weiyu Yu ; Shengli Xie ,An ant colony optimization lgorithm for image edge detection,
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International Symposium on Communications, Control and Signal Processing ( pp. 1077
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Zuofeng, , Liu Jing3,c , Cao Jianzhong , Wang Hao, Yan Aqi , Wu Dengshan , Zhang Hui and Tang Linao,
denoising algorithm via spatially adaptive bilateral filtering
Proceedings of the 2nd International Conference On Systems Engineering and Modeling (ICSEM
M. Zhang, B.K. Gunturk, Multiresolution Bilateral Filtering for
IEEE Trans. Image Process.
W. Dai and K. Wang, An image edge detection algorithm based on local entropy, in Proceedings
Conference on Integration Technology (ICIT’07) ( pp. 418 I. E. Sobel, Camera Models and Machine Perception, Ph.D. Thesis, Electrical Engineering Department, Stanford University, California, United States, 1970.
Wang , Li Wang ,Application of Improved Fuzzy Cognitive Map Based on Fuzzy Neural Network in Intrusion
Journal of Information & Computational Science,
278, 2013.
JianWu, Qingwei Cao, An OWA operator based approach to aggregate group opinion by similarity degree, Fourth International Conference on Business Intelligence and Financial Engineering Liu, H., Liang, L., Zhang, L., Submarine hydraulic rudder control system based on fuzzy logic algorithm, (2012)
Review on Computers and Software (IRE
Copyright © 2014 Praise Worthy Prize S.r.l. Information Sciences, Vol. XL-1/W3,
Ehsan Nadernejad, Sara Sharifzadeh, Hamid Hassanpour, Edge Detection Techniques: Evaluation
Mathematical Sciences, Vol. 2, No. 31, pp.1507
Peter Meer, and Bogdan Georgescu, Edge Detection with
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23
Mamta Juneja , Parvinder Singh Sandhu ,Performance Evaluation of Edge Detection Techniques for Images in Spatial Domain
International Journal of Computer Theory and Engineering,
1, No. 5, December, 2009 pp.1793
a, Krishna Kant Singh and M.J.Nigam. Article: A Novel Algorithm for Impulse Noise Removal and Edge
International Journal of Computer Applications
34, January 2012. Published by Foundation of Computer Science, New York, USA.
atika Pradhan, Prasanna Pradhan, Reshmi Bhattacharjee, Divyendra Singh ,Edge Detection using Morphological Operator:
International Journal of Advanced Research