Extended Study on Removing Gaussian Noise in Mechanical Engineering Drawing Images using Median Filters
In this paper, an extended study is performed on the
effect of different factors on the quality of vector data based on a
previous study. In the noise factor, one kind of noise that appears in
document images namely Gaussian noise is studied while the previous
study involved only salt-and-pepper noise. High and low levels of
noise are studied. For the noise cleaning methods, algorithms that were
not covered in the previous study are used namely Median filters and
its variants. For the vectorization factor, one of the best available
commercial raster to vector software namely VPstudio is used to
convert raster images into vector format. The performance of line
detection will be judged based on objective performance evaluation
method. The output of the performance evaluation is then analyzed
statistically to highlight the factors that affect vector quality.
[1] H.S.M. Al-Khaffaf , A.Z. Talib, and R. Abdul Salam, (2008) A Study on
the effects of noise level, cleaning method, and vectorization software on
the quality of vector data, Lecture Notes in Computer Science 5046, pp.
299-309.
[2] W.Y. Liu, and D. Dori, (1997) A protocol for performance evaluation of
line detection algorithms, Machine Vision and Applications, 9(5-6):
240-250.
[3] VPstudio _ver_8. Raster to Vector Conversion Software, Softelec,
Munich, Germany, [Online]. (Accessed. 10 Feb 2008) available for
(http://www.softelec.com)
[4] S.-J Ko and Y.-H Lee, (1991) Center weighted median filters and their
applications to image enhancement, IEEE Trans. Circuits Syst., 38:
984-993.
[5] F. Shafait, D. Keysers, T.M. Breuel, (2008) GREC 2007 Arc
Segmentation Contest, Evaluation of Four Participating Algorithms.
Lecture Notes in Computer Science 5046, pp. 310-320.
[6] L. Wenyin, Performance Evaluation tool (accessed on 2008)
http://www.cs.cityu.edu.hk/˜liuwy/ArcContest/
[7] W. Liu, (2004) Report of the Arc Segmentation Contest, in Graphics
Recognition, Lecture Notes in Computer Science: 363ÔÇö366.
[8] L. O-Gorman, (1992) Image and Document Processing Techniques for the
RightPages Electronic Library System. Proc. 11th IAPR Int-l Conf.
Pattern Recognition, 2: 260-263.
[9] K. Chinnasarn, Y. Rangsanseri, and P. Thitimajshima, (1998) Removing
salt-and-pepper noise in text/graphics images, Proc. Asia-Pacific Conf. on
Circuits and Systems. (APCCAS), Chiangmai, Thailand,: 459-462.
[10] P.Y. Simard and H. Malvar,(2004) An Efficient Binary Image Activity
Detector Based on Connected Components, Proc. International
Conference on Accoustic, Speech and Signal Processing (ICASSP), vol.
3: 229-232.
[11] H.S.M. Al-Khaffaf, A.Z. Talib, and R. Abdul Salam, (2006), Internal
Report, Enhancing salt-and-pepper noise removal in binary images of
engineering drawing. Artificial Intelligence Research Group, School of
Computer Sciences, Universiti Sains Malaysia.
[1] H.S.M. Al-Khaffaf , A.Z. Talib, and R. Abdul Salam, (2008) A Study on
the effects of noise level, cleaning method, and vectorization software on
the quality of vector data, Lecture Notes in Computer Science 5046, pp.
299-309.
[2] W.Y. Liu, and D. Dori, (1997) A protocol for performance evaluation of
line detection algorithms, Machine Vision and Applications, 9(5-6):
240-250.
[3] VPstudio _ver_8. Raster to Vector Conversion Software, Softelec,
Munich, Germany, [Online]. (Accessed. 10 Feb 2008) available for
(http://www.softelec.com)
[4] S.-J Ko and Y.-H Lee, (1991) Center weighted median filters and their
applications to image enhancement, IEEE Trans. Circuits Syst., 38:
984-993.
[5] F. Shafait, D. Keysers, T.M. Breuel, (2008) GREC 2007 Arc
Segmentation Contest, Evaluation of Four Participating Algorithms.
Lecture Notes in Computer Science 5046, pp. 310-320.
[6] L. Wenyin, Performance Evaluation tool (accessed on 2008)
http://www.cs.cityu.edu.hk/˜liuwy/ArcContest/
[7] W. Liu, (2004) Report of the Arc Segmentation Contest, in Graphics
Recognition, Lecture Notes in Computer Science: 363ÔÇö366.
[8] L. O-Gorman, (1992) Image and Document Processing Techniques for the
RightPages Electronic Library System. Proc. 11th IAPR Int-l Conf.
Pattern Recognition, 2: 260-263.
[9] K. Chinnasarn, Y. Rangsanseri, and P. Thitimajshima, (1998) Removing
salt-and-pepper noise in text/graphics images, Proc. Asia-Pacific Conf. on
Circuits and Systems. (APCCAS), Chiangmai, Thailand,: 459-462.
[10] P.Y. Simard and H. Malvar,(2004) An Efficient Binary Image Activity
Detector Based on Connected Components, Proc. International
Conference on Accoustic, Speech and Signal Processing (ICASSP), vol.
3: 229-232.
[11] H.S.M. Al-Khaffaf, A.Z. Talib, and R. Abdul Salam, (2006), Internal
Report, Enhancing salt-and-pepper noise removal in binary images of
engineering drawing. Artificial Intelligence Research Group, School of
Computer Sciences, Universiti Sains Malaysia.
@article{"International Journal of Information, Control and Computer Sciences:51302", author = "Low Khong Teck and Hasan S. M. Al-Khaffaf and Abdullah Zawawi Talib and Tan Kian Lam", title = "Extended Study on Removing Gaussian Noise in Mechanical Engineering Drawing Images using Median Filters", abstract = "In this paper, an extended study is performed on the
effect of different factors on the quality of vector data based on a
previous study. In the noise factor, one kind of noise that appears in
document images namely Gaussian noise is studied while the previous
study involved only salt-and-pepper noise. High and low levels of
noise are studied. For the noise cleaning methods, algorithms that were
not covered in the previous study are used namely Median filters and
its variants. For the vectorization factor, one of the best available
commercial raster to vector software namely VPstudio is used to
convert raster images into vector format. The performance of line
detection will be judged based on objective performance evaluation
method. The output of the performance evaluation is then analyzed
statistically to highlight the factors that affect vector quality.", keywords = "Performance Evaluation, Vectorization, Median
Filter, Gaussian Noise.", volume = "2", number = "9", pages = "2928-4", }