Abstract
Restoration and segmentation in corrupted text images are very important processing steps in digital image processing and several different methods were proposed in the open literature. In this paper, the restoration and segmentation problem in corrupted color text images are addressed by tensor voting and statistical method. In the proposed approach, we assume to have corruptions in text images. Our approach consists of two steps. The first one uses the tensor voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to decompose the pointness, edgeness and surfaceness of the data points. And then noises in a corrupted region are removed and restored by generalized adaptive vector sigma filters iteratively. In the second step, density mode detection and segmentation using statistical method based on Gaussian mixture model are performed in values according to hue and intensity components in the image. The experimental results show that proposed approach is efficient and robust in terms of restoration and segmentation corrupted text images.
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References
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(9), 1277–1294 (1993)
Moghaddamzadeh, A., Bourbakis, N.: A fuzzy region growing approach for segmentation of color images. Pattern Recognition 30(6), 867–881 (1997)
Zhang, C., Wang, P.: A new method of color image segmentation based on intensity and hue clustering. In: IEEE Int. Conference on Pattern Recognition, vol.3, pp. 3617–3621 (2000)
Jie, X., Peng-fei, S.: Natural color image segmentation. In: IEEE Int. conference on Image Processing, vol. 1, pp. 14–17 (2003)
Lucchese, L., Mitra, S.K.: Color image segmentation: a state-of-the-art survey. Pro. of the Indian National Science Academy 67, 207–221 (2001)
Zhong, Y., Karu, K., Jain, A.K.: Locating text in complex color images. Pattern Recognition 28, 1523–1536 (1995)
Jain, K., Yu, B.: Automatic Text location in images and video frames. Pattern Recognition 31, 2055–2076 (1998)
Haritaoglu, I.: Scene text extraction and translation for handheld devices. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 408–413 (2001)
Ye, Q., Gao, W., Huang, Q.: Automatic text segmentation from complex background. In: IEEE Int. Conference on Image Processing, vol. 5, pp. 2905–2908 (2004)
Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR: 2003 robust reading competitions. In: IEEE Int. Conference on Document Analysis and Recognition, pp. 682–687 (2003)
Medioni, G., Lee, M.S., Tang, C.K.: A Computational Framework for Segmentation and Grouping. Elsevier, Amsterdam (2000)
Lee, M.-S., Medioni, G.: Grouping.,-, \({\tiny{\rightarrow}}\), ,into regions, curves, and junctions. Computer Vision and Image Understanding 76(1), 54–69 (1999)
Lim, J.G., Park, J.H., Medioni, G.G.: Text segmentation in color images using tensor voting. Image and Vision Computing 25, 671–685 (2007)
Lukac, R., Smolka, B., Plataniotis, K.N., Venetsanopoulos, A.N.: Vector sigma filters for noise detection and removal in color images. Journal of Visual Communication and Image Representation 17, 1–26 (2006)
Lukac, R., Plataniotis, K.N., Venetsanopoulos, A.N., Smolka, B.: A statistically-switched adaptive vector median filter. J. Intell. Robotic System 42, 361–391 (2005)
Comaniciu, D., Meer, P.: Mean shift: a robust approach towards feature space analysis. IEEE Tran. on Pattern Analysis and Machine Intelligence 24(5), 1–18 (2001)
Park, J.H., Cho, W.H., Park, S.Y.: Deterministic annealing EM and its application in natural image segmentation. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 639–644. Springer, Heidelberg (2004)
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Park, J., Kien, N.T., Lee, G. (2007). Noise Removal and Restoration Using Voting-Based Analysis and Image Segmentation Based on Statistical Models. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_19
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DOI: https://doi.org/10.1007/978-3-540-74198-5_19
Publisher Name: Springer, Berlin, Heidelberg
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