Advertisement

Segmentation of Colour Layers in Historical Maps Based on Hierarchical Colour Sampling

  • Stefan Leyk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6020)

Abstract

A colour image segmentation (CIS) process for scanned historical maps is presented to overcome common problems associated with segmentation of old documents such as (1) variation in colour values of the same colour layer within one map page, (2) differences in typical colour values between homogeneous areas and thin line-work, which belong both to the same colour layer, and (3) extensive parameterization that results in a lack of robustness. The described approach is based on a two-stage colour layer prototype search using a constrained sampling design. Global colour layer prototypes for the identification of homogeneous regions are derived based on colour similarity to the most extreme colour layer values identified in the map page. These global colour layer prototypes are continuously adjusted using relative distances between prototype positions in colour space until a reliable sample is collected. Based on this sample colour layer seeds and directly connected neighbors of the same colour layer are determined resulting in the extraction of homogeneous colour layer regions. In the next step the global colour layer prototypes are recomputed using a new sample of colour values along the margins of identified homogeneous coloured regions. This sampling step derives representative prototypes of map layer sections that deviate significantly from homogeneous regions of the same layers due to bleaching, mixed or false colouring and ageing of the original scanned documents. A spatial expansion process uses these adjusted prototypes as start criterion to assign the remaining colour layer parts. The approach shows high robustness for map documents that suffer from low graphical quality indicating some potential for general applicability due to its simplicity and the limited need for preliminary information. The only input required is the colours and number of colour layers present in the map.

Keywords

Colour image segmentation two-stage colour sampling historical maps homogeneity cartographic pattern recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Khotanzad, A., Zink, E.: Contour line and geographic feature extraction from USGS colour topographical paper maps. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 18–31 (2003)CrossRefGoogle Scholar
  2. 2.
    Chiang, Y.-Y., Knoblock, C.A., Shahabi, C., Chen, C.-C.: Automatic and Accurate Extraction of Road Intersections from Raster. GeoInformatica 12(2), 121–157 (2009)CrossRefGoogle Scholar
  3. 3.
    Gamba, P., Mecocci, A.: Perceptual grouping for symbol chain tracking in digitized topographic maps. Pattern Recognit. Lett. 20, 355–365 (1999)CrossRefGoogle Scholar
  4. 4.
    Leyk, S., Boesch, R., Weibel, R.: Saliency and semantic processing—extracting forest cover from historical topographic maps. Pattern Recognition 39(5), 953–968 (2006)CrossRefGoogle Scholar
  5. 5.
    Cao, R., Tan, C.: Text/graphics separation in maps. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 167–177. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Llados, J., Valveny, E., Sanchez, G., Marti, E.: Symbol recognition: Current advances and perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 104–128. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Watanabe, T.: Recognition in maps and geographic documents: Features and approach. In: Chhabra, A.K., Dori, D. (eds.) GREC 1999. LNCS, vol. 1941, pp. 39–49. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Bajcsy, P.: Automatic Extraction Of Isocontours From Historical Maps. In: 7th World Multiconference on Systemics, Cybernetics and Informatics Proceedings (SCI 2003), Orlando, Florida, vol. 4, pp. 99–104 (2003)Google Scholar
  9. 9.
    Leyk, S., Boesch, R.: Colours of the past: Colour Image Segmentation in Historical Topographic Maps Based on Homogeneity. GeoInformatica 14(1), 1–21 (2010)Google Scholar
  10. 10.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Colour image segmentation: advances and prospects. Pattern Recognit. 34, 2259–2281 (2001)zbMATHCrossRefGoogle Scholar
  11. 11.
    Lucchese, L., Mitra, S.K.: Colour image segmentation: a state-of-the-art survey, Image processing, vision, and pattern recognition. Proc. of the Indian National Science Academy (INSA-A) 67A(2), 207–221 (2001)Google Scholar
  12. 12.
    Centeno, J.: Segmentation of thematic maps using colour and spatial attributes. In: Chhabra, A.K., Tombre, K. (eds.) GREC 1997. LNCS, vol. 1389, pp. 221–230. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    den Hartog, J., ten Kate, T., Gebrands, J.: Knowledge based segmentation for automatic map interpretation. In: Kasturi, R., Tombre, K. (eds.) Graphics Recognition 1995. LNCS, vol. 1072, pp. 159–178. Springer, Heidelberg (1996)Google Scholar
  14. 14.
    Santos, R., Ohashi, T., Yoshida, T., Ejima, T.: Filtering and segmentation of digitized land use map images. Int’l. J. Doc. Anal. Recognit. 1, 167–174 (1998)CrossRefGoogle Scholar
  15. 15.
    Chen, Y., Wang, R., Qian, J.: Extracting contour lines from common-conditioned topographic maps. IEEE Trans. Geosci. Rem. Sens. 44(4), 1048–1057 (2006)CrossRefGoogle Scholar
  16. 16.
    Leyk, S., Boesch, R.: Extracting Composite Cartographic Area Features in Low-Quality Maps. Cartography and Geographical Information Science 36(1), 71–79 (2009)CrossRefGoogle Scholar
  17. 17.
    U.S. Geological Survey, USGS 15 and 30 minute Historical Maps for San Francisco Bay Area. San Francisco Bay Area Regional Database (BARD), Menlo Park, CA (2004), http://bard.wr.usgs.gov/

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stefan Leyk
    • 1
  1. 1.Department of GeographyUniversity of Colorado, 260 UCBBoulderUSA

Personalised recommendations