Abstract
Most computer vision applications often require reliable segmentation of objects when they are mixed with corrupted text images. In the presence of noise, graffiti, streaks, shadows and cracks, this problem is particularly challenging. We propose a tensor voting framework in 3D for the analysis of candidate features. The problem has been formulated as an inference of hue and intensity layers from a noisy and possibly sparse point set in 3D. Accurate region layers are extracted based on the smoothness of color features by generating candidate features with outlier rejection and text segmentation. The proposed method is non-iterative and consistently handles both text data and background without using any prior information on the color space.
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© 2006 Springer-Verlag Berlin Heidelberg
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Park, J., Yoo, J., Lee, G. (2006). A Tensor Voting for Corrupted Region Inference and Text Image Segmentation. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_73
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DOI: https://doi.org/10.1007/978-3-540-69423-6_73
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