Abstract
The approach based on the Mahalanobis Distance Value (MDV) is introduced for appearance enhancement of objects included in images; and especially for study cases dealing with historical images. In those cases, this approach allows an automatically reducing of the noise pixels and distortion parameters associated with an image. First of all, an image is divided into Seed Regions (SRs) based on watershed transformation. Each SR created is divided into non-overlapping subregions based on the Intensity Values (IVs) associated with (MDV). Subregions which have the same MDV and different intensity values have to be separated. Therefore, the subregion with the minimum MDV is considered as Reference Partition (RP) used for the separation process. IVs of a final generated subregion are replaced by the IV which has the largest frequency associated with. As a result, each subregion takes a new color which is relatively close to its original color but more clear and low gradient. The performance of the MDV-based approach is expressed through a comparison to other approaches used for appearance enhancement of images (like: Gaussian filter).
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References
G. Agam, G. Bal, G. Frieder, and O. Frieder. Degraded document image enhancement. Vol. 6500, pp. 65000C–65000C–11. SPIE, 2007.
A. Bin Mansoor, Z. Khan, and A. Khan. An application of fuzzy morphology for enhancement of aerial images. In Advances in Space Technologies, 2008. ICAST 2008. 2nd International Conference on, pp. 143–148, November 2008.
J. Geoffrey. Discriminant Analysis and Statistical Pattern Recognition. Wiley-Interscience, 1992.
Rafael C. Gonzalez, Steven L. Eddins, and Richard E. Woods. Digital Image Processing Using MATLAB. Prentice Hall, February 2004.
M. Gruber and F. Leberl. High Quality Photogrammetric Scanning for Mapping. ISPRS Journal for Photogrammetry and Remote Sensing, 55:3113–329, 2001. Elsevier Publishers, The Netherlands.
R.C. Gonzalez and R.E. Woods. Digital Image Processing. Prentice Hall International. Prentice Hall International, 2002.
Zhixin Shi and Venu Govindaraju. Historical Document Image Enhancement Using Background Light Intensity Normalization. In Pattern Recognition, International Conference on, Vol. 1, pp. 473–476, Los Alamitos, CA, USA, 2004. IEEE Computer Society.
Yuzhong Shen and S.K. Jakkula. Aerial Image Enhancement Based on Estimation of Atmospheric Effects. In Image Processing, 2007. ICIP 2007. IEEE International Conference on, Vol. 3, pp. III –529 –III –532. IEEE Xplore, October 2007.
Z. Shi, S. Setlur, and V. Govindaraju. Digital Image Enhancement using Normalization Techniques and their application to Palm Leaf Manuscripts. 2005.
Zhixin Shi, Srirangaraj Setlur, and Venu Govindaraju. Digital Image Enhancement of Indic Historical Manuscripts. In Venu Govindaraju and Ranga Srirangaraj Setlur, eds., Guide to OCR for Indic Scripts, Advances in Pattern Recognition, pp. 249–267. Springer London, 2010.
Qian Wang, Tao Xia, Lida Li, and Chew Lim Tan. Document image enhancement using directional wavelet. In Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, Vol. 2, pp. II–534 – II–539 vol.2. IEEE Xplore, June 2003.
S.R. Yahya, S.N.H.S. Abdullah, K. Omar, M.S. Zakaria, and C.Y. Liong. Review on image enhancement methods of old manuscript with the damaged background. In Electrical Engineering and Informatics, 2009. ICEEI '09. International Conference on, Vol. 1, pp. 62 –67. IEEE Xplore, August 2009.
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Alfraheed, M., Alamouri, A., Jeschke, S. (2013). A MDV - Based approach for appearance enhancement of historical images. In: Jeschke, S., Isenhardt, I., Hees, F., Henning, K. (eds) Automation, Communication and Cybernetics in Science and Engineering 2011/2012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33389-7_43
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DOI: https://doi.org/10.1007/978-3-642-33389-7_43
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