Abstract
In this paper, a novel adaptive noise reduction method for engineering drawings is proposed based on the assessment of both primitives and noise. Unlike the current approaches, our method takes into account the special features of engineering drawings and assesses the characteristics of primitives and noise such that adaptive procedures and parameters are applied for noise reduction. For this purpose, we first analyze and categorize various types of noise in engineering drawings. The algorithms for average linewidth assessment, noise distribution assessment and noise level assessment are then proposed. These three assessments are combined to describe the features of the noise of each individual engineering drawing. Finally, median filters and morphological filters, which can adjust their template size and structural element adaptively according to different noise level and type, are used for adaptive noise reduction. Preliminary experimental results show that our approach is effective for noise reduction of engineering drawings.
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References
Andrews, H.C.: Monochrome Digital Image Enhancement. Applied Optics 15(2), 495–503 (1976)
Lev, A., Zucker, S.W., Rosenfeld, A.: Interactive Enhancement of Noisy Images. IEEE Trans. on Systems, Man and Cybernetics 7(6), 422–435 (1977)
Mastin, G.A.: Adaptive Filters for Digital Image Noise Smoothing: An Evaluation. Computer Vision, Graphics and Image Processing 31(1), 103–121 (1985)
Ishihara, J., Meguro, M., Hamada, N.: Adaptive Weighted Median Filter Utilizing Impulsive Noise Detection. Application of Digital Image Processing, Proc. SPIE 3808, 406–414 (1999)
Miloslavski, M., Choi, T.S.: Application of LUM Filter with Automatic Parameter Selection to Edge Detection. Applications of Digital Image Processing, Proc. SPIE 3460, 865–871 (1998)
Oktem, H., Egizarian, K., Katkvnik, V.: Adaptive De-noising of Images by Locally Switching Wavelet Transforms. ICIP (1999)
Russo, F., Ramponi, G.: A Fuzzy Filter for Images Corrupted by Impulse Noise. IEEE Signal Processing Letters 3(6), 168–170 (1996)
Kong, H., Guan, L.: A Neural Network Adaptive Filter for the Removal of Impulse Noise in Digital Images. Neural Network 9(3), 373–378 (1996)
Pavlidis, T.: Recognition of Printed Text Under Realistic Conditions. Pattern Recognition Letters 14(4), 226–317 (1993)
Kanungo, T., Haralick, R.M., Phillips, I.: Global and Local Document Degradation Models. In: Proc. ICDAR, pp. 730–734 (1993)
Jian, Z., Wenyin, L., Dori, D., Qing, L.: A Line Drawings Degradation Model for Performance Characterization. In: Proc. ICDA, pp. 1020–1024 (2003)
Mott-Smith, J.C.: Medial Axis Transforms. In: Picture Processing and Psychopictoris. Academic Press, New York (1970)
GREC 2003 Symbol Recognition Contest, http://www.cvc.uab.es/grec2003/SymRecContest/images.htm
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Zhang, J., Zhang, W., Wenyin, L. (2006). Adaptive Noise Reduction for Engineering Drawings Based on Primitives and Noise Assessment. In: Liu, W., Lladós, J. (eds) Graphics Recognition. Ten Years Review and Future Perspectives. GREC 2005. Lecture Notes in Computer Science, vol 3926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11767978_13
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DOI: https://doi.org/10.1007/11767978_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34711-8
Online ISBN: 978-3-540-34712-5
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