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Research and analysis of Image edge detection algorithm Based on the MATLAB
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Procedia Engineering
Procedia Engineering 00 (2011) 000–000 Procedia Engineering 15 (2011) 1313 – 1318 www.elsevier.com/locate/procedia
Advanced in Control Engineering and Information Science
Research and analysis of Image edge detection algorithm Based on the MATLAB Guowei Yang, Fengchang Xu * (School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China)
Abstract Image edge detection is one of the important contents of image processing. In this paper, we show a new edge detection operator, which is Log_Sobel. This operator processes image with logarithm of luminosity (luminous density) not with luminosity. It processes fast, and suits online detection. What’s more, the detect result is in dependent of luminosity. At the same time, it is applied to the online detection. The experiment results show that the effect for image processed by Log_Sobel operator is better than those operators, including Roberts algorithm, Prewitt algorithm and Sobel algorithm, proposed by predecessor. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011] Key words: edge detection; Log_Sobel operator; simulation
1. Introduction Edge detection is the important basis of image analysis, including image segmentation, texture feature extraction and shape feature extraction. Therefore, edge detection becomes particularly important. In recent years, it becomes the most active field of the image processing. Predecessors have developed many edge detection operations, such as Roberts operator, Sobel operator, Prewitt operator, Kirsch operator and so on, which are directly on the image intensity of the light. In the Fig.1, we describe the relationship of imaging among incident light intensity f ( x, y ) , reflected light intensity g ( x, y ) and object reflectivity e( x, y ) . The relationship formula is as follows:
*
Corresponding author: Guowei Yang. Tel.: +86-791-3953432; +86-791-3953432. E-mail address: [email protected]
1877-7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2011.08.243
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GuoweiYang, Yang Fengchang and Fengchang / Procedia Engineering 15 (2011) 1313 – 1318 Guowei Xu / Xu Procedia Engineering 00 (2011) 000–000
g ( x , y ) = e( x , y ) f ( x , y )
(1)
Fig.1. Reverberating of optics
If every part of the object is uneven illumination intensity during the processing of the imaging, or images may image under the different light intensity. Uneven illumination intensity has the serious influence on the edge detection. In photograph technology, logarithm of light intensity is called light intensity, which is proportional to the logarithm of the objects reflectivity. So it is usually the logarithm function of the exposure. During the both sides of formula (1)’s make the logarithmic transformation, then we will gain light intensity formulation (2): log( g ( x, y )) = log(e( x, y )) + log( f ( x, y )) (2) The incident light f ( x, y ) is slowly changed in a small local area within weight evenly, so the
adjacent points of light intensity f ( x, y ) can be regarded as phase equivalence. Adopting logarithmic subtraction can eliminate the light intensity factor, and then only retains the change of objects reflectivity. This paper puts forward the operator of Log_Sobel based on this one principle for Sobel operator improvement, which not only processes optical density directly to eliminate the effect caused by the uneven illumination intensity, but also inherits smooth function of Sobel operator and improves antijamming of the detection. In addition, the two boundary threshold values which can be preset have only contacted with the contrast sensitivity. Therefore, two values of detection area on the borders can finish simultaneously during the testing processing. When the Log_Sobel algorithm is used to process the image, the texture is clear and the effect is obvious. This algorithm is compared with those algorithms proposed by the predecessor, including Roberts algorithm, Prewitt algorithm and Sobel algorithm. The effect of the former is much better, and the processing speed is much quicker. Meanwhile, it has certain realistic application, and also suits in online detection. 2. Log_Sobel operator 2.1. Sobel operator Sobel operator is formed by two convolution kernels which shows in the table 1 and table 2. In image processing, these two kernels are used to convolute each point of the image. Then takes the biggest one as output: Table 1. Sobel horizon operator
-1 0 1
-2 0 2
-1 0 1
Guowei YangYang, and Fengchang – 1318 Guowei FengchangXu Xu/ /Procedia ProcediaEngineering Engineering1500(2011) (2011)1313 000–000 Table 2. Sobel vertical operator
-1 -2 -1
0 0 0
1 2 1
2.2. Log_Sobel operator Log_Sobel operator adopts the convolution kernels of Sobel operator, which processes the object that is not image gray-scale, but the logarithm of image gray-scale. This can retain the insensitivity for the original method of optical strength changes, simultaneously through the smooth to overcome the shortcomings of anti-jamming weakness. It can enhance the signal-to-noise ratio. The computation formula is as follows:
(3)
(4) (5)
A = (log(g ( x + 1, y − 1)) + log(2 * g (x + 1, y )) + log(g (x + 1, y + 1))) − (log(g (x − 1, y − 1)) + log(2 * g (x − 1, y )) + log(g ( x − 1, y + 1)))
B = (log( g ( x − 1, y + 1)) + log(2 * g ( x, y + 1)) + log( g (x + 1, y + 1))) −
(log( g ( x − 1, y − 1)) + log(2 * g (x, y − 1)) + log(g ( x − 1, y + 1))) G ( x, y ) =| A | + | B |
2.3. experiment result Fig. 2a is the original image of the illumination uneven, which is taken by the digital camera. Fig. 2b is the Roberts operator test result. Fig. 2c is the Sobel operator test result. Fig.2d is the Pr ewitt operator test result. Fig. 2e is the Log_Sobel operator test result. In order to enhance the speed reduction floating point calculation, we process the light density of each pixel. Which can be obtained by using the table look-up law.
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GuoweiYang, Yang Fengchang and Fengchang / Procedia Engineering 15 (2011) 1313 – 1318 Guowei Xu / Xu Procedia Engineering 00 (2011) 000–000
(a) Original figure
(c) Result of Sobel operator
(b) Result of Roberts operator
(d) Result obtained by Prewitt operator
Guowei YangYang, and Fengchang – 1318 Guowei FengchangXu Xu/ /Procedia ProcediaEngineering Engineering1500(2011) (2011)1313 000–000
(e) Result obtained by Log_Sobel operator Fig.2. Image of edge detection
3. Conclusions The Log_Sobel algorithm is proposed in this article. When it is used to process the image, the texture is clear and the effect is obvious. This algorithm is compared with those algorithms proposed by the predecessor, including Roberts algorithm, Prewitt algorithm and Sobel algorithm. The effect of the former is much better, the processing speed is much quicker. Meanwhile, it has certain realistic application, and also suits in online detection. Acknowledgements This program is sponsored by the National Natural Science Foundation of China under Grant (No.60973048), the Natural Science Foundation of Jiangxi Province under Grant (No.2009GZS0084), the Research Fundation of Nanchang Hangkong University under Grant (No.EA200906012), and the Postgraduate Innovates Fundation of Nanchang Hangkong University under Grant (No.YC2010030).
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GuoweiYang, Yang Fengchang and Fengchang / Procedia Engineering 15 (2011) 1313 – 1318 Guowei Xu / Xu Procedia Engineering 00 (2011) 000–000
References [1] Wang Lu. The license plate identification system based on MATLAB [D]. Shanghai Jiao tong university, 2009. [2] Xue Hongquan. The research of the license plate identification system Based on the wavelet analysis [D]. University of electronic science and technology, 2009. [3] Yun Tao, Cui Qianhuang. Automatic license extraction from moving vehicles. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1999, 7 (4); 126-130 [4] Wang HuiYan. The image edge detection and the image matching research and application (PH.D, Thesis [D]). Hangzhou, Zhejiang university, 2003. [5] Xu Guobao, Wang Ji, Zhao Guiyan, Yin Yixin, Xie Shiyi. A new operator of adaptive edge detection based on mathematical morphology [J]. Computer application, 2009, (04). [6] Gan Jinlai. Comparative study of the image edge detection algorithm [D]. University of electronic science and technology, 2005. [7] L. Dlagnekov. Video-based car surveillance: License plate make and model recognition. Masters Thesis, University of California at San Diego, 2005. [8] Chen Fen. Edge detection based on the multi-scale wavelet transformation [J]. Computer engineering and application, 2009, (27). [9] Kang Mu, Wang Baoshu. Self-adaptive Kirsch edge detection algorithm [J]. Journal of Huazhong technology university (natural science edition), 2009, (04). [10] Ming Zhang, Bahadir Gunturk. A New Image Denoising Method Based on the Bilateral Filter. IEEE, 2008, 929-923. [11] Y. Gao and M. K. H. Leung. Face recognition using line edge map. In IEEE Trans. on Pattern Analysis and Machine Intelligence Vol. 24, 764-779, 2002. [12] R. O. Duda P. E. Hart and D. G. Stork. Pattern Classification. John Wiley & Sons, Inc. New York, second edition, 2004. [13] M. K. H. Leung and Y. H. Yang. Dynamic two-strip algorithm in curve fitting. In Pattern Recognition, vol. 23, 69-79, 1990. [14] K. Reumann and A. P. Witkam. Optimizing curve segmentation in computer graphics. Int. Comput. Symp. 244–256, 1974.
Procedia Engineering
Procedia Engineering 00 (2011) 000–000 Procedia Engineering 15 (2011) 1313 – 1318 www.elsevier.com/locate/procedia
Advanced in Control Engineering and Information Science
Research and analysis of Image edge detection algorithm Based on the MATLAB Guowei Yang, Fengchang Xu * (School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China)
Abstract Image edge detection is one of the important contents of image processing. In this paper, we show a new edge detection operator, which is Log_Sobel. This operator processes image with logarithm of luminosity (luminous density) not with luminosity. It processes fast, and suits online detection. What’s more, the detect result is in dependent of luminosity. At the same time, it is applied to the online detection. The experiment results show that the effect for image processed by Log_Sobel operator is better than those operators, including Roberts algorithm, Prewitt algorithm and Sobel algorithm, proposed by predecessor. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011] Key words: edge detection; Log_Sobel operator; simulation
1. Introduction Edge detection is the important basis of image analysis, including image segmentation, texture feature extraction and shape feature extraction. Therefore, edge detection becomes particularly important. In recent years, it becomes the most active field of the image processing. Predecessors have developed many edge detection operations, such as Roberts operator, Sobel operator, Prewitt operator, Kirsch operator and so on, which are directly on the image intensity of the light. In the Fig.1, we describe the relationship of imaging among incident light intensity f ( x, y ) , reflected light intensity g ( x, y ) and object reflectivity e( x, y ) . The relationship formula is as follows:
*
Corresponding author: Guowei Yang. Tel.: +86-791-3953432; +86-791-3953432. E-mail address: [email protected]
1877-7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2011.08.243
1314 2
GuoweiYang, Yang Fengchang and Fengchang / Procedia Engineering 15 (2011) 1313 – 1318 Guowei Xu / Xu Procedia Engineering 00 (2011) 000–000
g ( x , y ) = e( x , y ) f ( x , y )
(1)
Fig.1. Reverberating of optics
If every part of the object is uneven illumination intensity during the processing of the imaging, or images may image under the different light intensity. Uneven illumination intensity has the serious influence on the edge detection. In photograph technology, logarithm of light intensity is called light intensity, which is proportional to the logarithm of the objects reflectivity. So it is usually the logarithm function of the exposure. During the both sides of formula (1)’s make the logarithmic transformation, then we will gain light intensity formulation (2): log( g ( x, y )) = log(e( x, y )) + log( f ( x, y )) (2) The incident light f ( x, y ) is slowly changed in a small local area within weight evenly, so the
adjacent points of light intensity f ( x, y ) can be regarded as phase equivalence. Adopting logarithmic subtraction can eliminate the light intensity factor, and then only retains the change of objects reflectivity. This paper puts forward the operator of Log_Sobel based on this one principle for Sobel operator improvement, which not only processes optical density directly to eliminate the effect caused by the uneven illumination intensity, but also inherits smooth function of Sobel operator and improves antijamming of the detection. In addition, the two boundary threshold values which can be preset have only contacted with the contrast sensitivity. Therefore, two values of detection area on the borders can finish simultaneously during the testing processing. When the Log_Sobel algorithm is used to process the image, the texture is clear and the effect is obvious. This algorithm is compared with those algorithms proposed by the predecessor, including Roberts algorithm, Prewitt algorithm and Sobel algorithm. The effect of the former is much better, and the processing speed is much quicker. Meanwhile, it has certain realistic application, and also suits in online detection. 2. Log_Sobel operator 2.1. Sobel operator Sobel operator is formed by two convolution kernels which shows in the table 1 and table 2. In image processing, these two kernels are used to convolute each point of the image. Then takes the biggest one as output: Table 1. Sobel horizon operator
-1 0 1
-2 0 2
-1 0 1
Guowei YangYang, and Fengchang – 1318 Guowei FengchangXu Xu/ /Procedia ProcediaEngineering Engineering1500(2011) (2011)1313 000–000 Table 2. Sobel vertical operator
-1 -2 -1
0 0 0
1 2 1
2.2. Log_Sobel operator Log_Sobel operator adopts the convolution kernels of Sobel operator, which processes the object that is not image gray-scale, but the logarithm of image gray-scale. This can retain the insensitivity for the original method of optical strength changes, simultaneously through the smooth to overcome the shortcomings of anti-jamming weakness. It can enhance the signal-to-noise ratio. The computation formula is as follows:
(3)
(4) (5)
A = (log(g ( x + 1, y − 1)) + log(2 * g (x + 1, y )) + log(g (x + 1, y + 1))) − (log(g (x − 1, y − 1)) + log(2 * g (x − 1, y )) + log(g ( x − 1, y + 1)))
B = (log( g ( x − 1, y + 1)) + log(2 * g ( x, y + 1)) + log( g (x + 1, y + 1))) −
(log( g ( x − 1, y − 1)) + log(2 * g (x, y − 1)) + log(g ( x − 1, y + 1))) G ( x, y ) =| A | + | B |
2.3. experiment result Fig. 2a is the original image of the illumination uneven, which is taken by the digital camera. Fig. 2b is the Roberts operator test result. Fig. 2c is the Sobel operator test result. Fig.2d is the Pr ewitt operator test result. Fig. 2e is the Log_Sobel operator test result. In order to enhance the speed reduction floating point calculation, we process the light density of each pixel. Which can be obtained by using the table look-up law.
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GuoweiYang, Yang Fengchang and Fengchang / Procedia Engineering 15 (2011) 1313 – 1318 Guowei Xu / Xu Procedia Engineering 00 (2011) 000–000
(a) Original figure
(c) Result of Sobel operator
(b) Result of Roberts operator
(d) Result obtained by Prewitt operator
Guowei YangYang, and Fengchang – 1318 Guowei FengchangXu Xu/ /Procedia ProcediaEngineering Engineering1500(2011) (2011)1313 000–000
(e) Result obtained by Log_Sobel operator Fig.2. Image of edge detection
3. Conclusions The Log_Sobel algorithm is proposed in this article. When it is used to process the image, the texture is clear and the effect is obvious. This algorithm is compared with those algorithms proposed by the predecessor, including Roberts algorithm, Prewitt algorithm and Sobel algorithm. The effect of the former is much better, the processing speed is much quicker. Meanwhile, it has certain realistic application, and also suits in online detection. Acknowledgements This program is sponsored by the National Natural Science Foundation of China under Grant (No.60973048), the Natural Science Foundation of Jiangxi Province under Grant (No.2009GZS0084), the Research Fundation of Nanchang Hangkong University under Grant (No.EA200906012), and the Postgraduate Innovates Fundation of Nanchang Hangkong University under Grant (No.YC2010030).
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GuoweiYang, Yang Fengchang and Fengchang / Procedia Engineering 15 (2011) 1313 – 1318 Guowei Xu / Xu Procedia Engineering 00 (2011) 000–000
References [1] Wang Lu. The license plate identification system based on MATLAB [D]. Shanghai Jiao tong university, 2009. [2] Xue Hongquan. The research of the license plate identification system Based on the wavelet analysis [D]. University of electronic science and technology, 2009. [3] Yun Tao, Cui Qianhuang. Automatic license extraction from moving vehicles. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1999, 7 (4); 126-130 [4] Wang HuiYan. The image edge detection and the image matching research and application (PH.D, Thesis [D]). Hangzhou, Zhejiang university, 2003. [5] Xu Guobao, Wang Ji, Zhao Guiyan, Yin Yixin, Xie Shiyi. A new operator of adaptive edge detection based on mathematical morphology [J]. Computer application, 2009, (04). [6] Gan Jinlai. Comparative study of the image edge detection algorithm [D]. University of electronic science and technology, 2005. [7] L. Dlagnekov. Video-based car surveillance: License plate make and model recognition. Masters Thesis, University of California at San Diego, 2005. [8] Chen Fen. Edge detection based on the multi-scale wavelet transformation [J]. Computer engineering and application, 2009, (27). [9] Kang Mu, Wang Baoshu. Self-adaptive Kirsch edge detection algorithm [J]. Journal of Huazhong technology university (natural science edition), 2009, (04). [10] Ming Zhang, Bahadir Gunturk. A New Image Denoising Method Based on the Bilateral Filter. IEEE, 2008, 929-923. [11] Y. Gao and M. K. H. Leung. Face recognition using line edge map. In IEEE Trans. on Pattern Analysis and Machine Intelligence Vol. 24, 764-779, 2002. [12] R. O. Duda P. E. Hart and D. G. Stork. Pattern Classification. John Wiley & Sons, Inc. New York, second edition, 2004. [13] M. K. H. Leung and Y. H. Yang. Dynamic two-strip algorithm in curve fitting. In Pattern Recognition, vol. 23, 69-79, 1990. [14] K. Reumann and A. P. Witkam. Optimizing curve segmentation in computer graphics. Int. Comput. Symp. 244–256, 1974.