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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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
Print ISBN: 978-3-540-74195-4
Online ISBN: 978-3-540-74198-5
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