Abstract
We present a general mathematical model for multiple forms of stripes. Based on the model, we propose a method to detect stripes built on scale-space. This method generates difference of Gaussian (DoG) maps by subtracting neighbor Gaussian layers, and reserves extremal responses in each DoG map by comparing to its neighbors. Candidate stripe regions are then formed from connected extremal responses. After that, approximate centerlines of stripes are extracted from candidate stripe regions using non-maximum suppression, which eliminates undesired edge responses simultaneously. And stripe masks could be restored from those centerlines with the estimated stripe width. Owing to the ability of extracting candidate regions, our method avoids traversing to do costly directional calculation on all pixels, so it is very efficient. Experiments show the robustness and efficiency of the proposed method, and demonstrate its ability to be applied to different kinds of applications in the image processing stage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gonzalez, R.C., Woods, R.E.: Digital image processing, 3rd edn., pp. 719–722. Prentice-Hall Publisher (2008)
Hough, P.V.C.: Method and means for recognizing complex patterns. U.S. Patent 3,069,654 (December 18, 1962)
Koller, T.M., Gerig, G., Szekely, G., Dettwiler, D.: Multiscale detection of curvilinear structures in 2-D and 3-D image data. In: Proc. ICCV, pp. 864–869 (1995)
Steger, C.: An unbiased detector of curvilinear structures. IEEE Trans. PAMI 20(2), 113–125 (1998)
Eberly, D., Gardner, R., Morse, B., Pizer, S., Scharlach, C.: Ridges for image analysis. J. Math. Imaging Vis. 4(4), 353–373 (1994)
Koenderink, J.J., van Doorn, A.J.: Two-plus-one-dimensional differential geometry. Pattern Recognition Letters 15(5), 439–444 (1995)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. In: Proc. CVPR, pp. 465–470 (1996)
Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Proc. CVPR, pp. 2963–2970 (2010)
Lindeberg, T.: Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(2), 224–270 (1994)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Lindeberg, T.: Generalized Gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space. J. Math. Imaging Vis. 40(1), 36–81 (2011)
Shahab, A., Shafait, F., Dengel, A.: ICDAR 2011 robust reading competition challenge 2: reading text in scene images. In: Proc. ICDAR, pp. 1491–1496 (2011)
Wang, W.Y., Zhang, D.M., Zhang, Y.D., Li, J.T., Gu, X.G.: Robust spatial matching for object retrieval and its parallel implementation on GPU. IEEE Trans. on Multimedia 13(6), 1308–1318 (2011)
Xie, H.T., Gao, K., Zhang, Y.D., Tang, S., Li, J.T., Liu, Y.Z.: Efficient feature detection and effective post-verification for large scale near-duplicate image search. IEEE Trans. on Multimedia 13(6), 1319–1332 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Y., Zhang, D., Guo, J., Lin, S. (2013). Stripe Model: An Efficient Method to Detect Multi-form Stripe Structures. In: Li, S., et al. Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35725-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-35725-1_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35724-4
Online ISBN: 978-3-642-35725-1
eBook Packages: Computer ScienceComputer Science (R0)