Abstract
In this paper, we present an efficient segmentation technique that extracts piecewise linear patterns from hand-drawn maps. The user is only required to place the starting and end points and the method is capable of extracting the route that connects the two, which closely colocates with the hand-drawn map. It provides an effective approach to interactively process and understand those historical maps. The proposed method employs supervised learning to evaluate at every pixel location the probability that such a lineage pattern exists, followed by shortest path segmentation to extract the border of interest.
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References
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. IJCV 70(1), 109–131 (2006)
Breiman, L.: Random forests. Mach. Learn. 5(1), 5–32 (2001)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE T-PAMI 24(5), 603–619 (2002)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. CVPR. 1, 886–893 (2005)
Dijkstra, E.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)
Essa, E., Xie, X., Sazonov, I., Nithiarasu, P., Smith, D.: Shape prior model for media-adventitia border segmentation in IVUS using graph cut. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds.) MCV 2012. LNCS, vol. 7766, pp. 114–123. Springer, Heidelberg (2013)
Falcão, A.X., et al.: User-steered image segmentation paradigms: live wire and live lane. Graph. Models Image Process. 60(4), 233–260 (1998)
Jones, J.-L., Essa, E., Xie, X., Smith, D.: Interactive segmentation of media-adventitia border in IVUS. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 466–474. Springer, Heidelberg (2013)
Jones, J.L., Xie, X., Essa, E.: Combining region-based and imprecise boundary-based cues for interactive medical image segmentation. IJNMBE 30(12), 1649–1666 (2014)
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. IJCV 43(1), 7–27 (2001)
Mortensen, E.N., Barrett, W.A.: Interactive segmentation with intelligent scissors. Graph. Models Image Process. 60(5), 349–384 (1998)
Rotem, O., Greenspan, H., Goldberger, J.: Combining region and edge cues for image segmentation in a probabilistic gaussian mixture framework. In: CVPR, pp. 1–8 (2007)
Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: CVPR, pp. 3982–3991 (2015)
Xie, X., Mirmehdi, M.: Texture exemplars for defect detection on random textures. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 404–413. Springer, Heidelberg (2005)
Xie, X., Mirmehdi, M.: Magnetostatic field for the active contour model: a study in convergence. In: BMVC, pp. 127–136 (2006)
Xie, X., Mirmehdi, M.: TEXEMS: random texture representation and analysis. In: Handbook of Texture Analysis, Chapter 4 (2008)
Yeo, S., Xie, X., Sazonov, I., Mirmehdi, M.: Geometric potential force for the deformable model. In: BMVC (2009)
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Essa, E., Xie, X., Turner, R., Stevens, M., Power, D. (2016). Extracting Lineage Information from Hand-Drawn Ancient Maps. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_30
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DOI: https://doi.org/10.1007/978-3-319-41501-7_30
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