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Map image binarization and stitching using extraction of regions

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Binarisation methods already reported are inadequate for binarisation of complex documents such as maps due to large intensity variations across the regions and entangled texts with lines representing borders, rivers, roads and similar other components. This paper proposes a new binarisation technique for coloured land map images by extracting the regions and analysing the hue, saturation spread and within class kurtosis. This is a region-wise adaptive algorithm to cope up with the sharp changes of the discriminating features across different regions. Here, local regions are selected as clusters having the same hue and saturation. The regions are individually binarised using the spread of their degree of within class kurtosis and finally combined together. The regions extracted are further utilised for stitching of map documents which contain some portion in common. We use a simple greedy technique using correlation matching to join two or more map images such that information from both can be viewed and compared. Our experiments include 446 colour maps from the map image database created for this purpose and made freely available at website. This work is an extended version of our previous work on map image binarisation [1].
Słowa kluczowe
Rocznik
Strony
28--40
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Department of CSE, Indian Institute of Technology, Kharagpur, India
autor
  • CST Department, Indian Institute of Engineering Science and Technology, Shibpur, India
autor
autor
  • ECS Unit, Indian Statistical Institute, Kolkata, India
Bibliografia
  • [1] Mandal, S., Biswas, S., Das, A., Chanda, B.: Binarisation of Colour Map Images through Extraction of Regions. In: Chmielewski, L., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.), Computer Vision and Graphics, volume 8671 of Lecture Notes in Computer Science, pp. 418–427. Springer International Publishing, 2014. ISBN 978-3-319-11330-2.
  • [2] Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. Ieee Transactions on Systems, Man, And Cybernetics, 1979.
  • [3] Niblack, W.: An Introduction to Image Processing, Prentice-Hall, Englewood Cliffs. 1986.
  • [4] Sauvola, J. J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognition, 33(2), pp. 225–236, 2000.
  • [5] Gatos, B., Pratikakis, I., Perantonis, S.: Adaptive degraded document image binarization. Pattern Recognition, 2006.
  • [6] Lu, S., Su, B., Tan, C. L.: Document image binarization using background estimation and stroke edges. IJDAR, 13(4), pp. 303–314, 2010.
  • [7] Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition, 1986.
  • [8] Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004.
  • [9] Wolf, C., Jolion, J.-M.: Extraction and Recognition of Artificial Text in Multimedia Documents. Pattern Analysis and Applications, 2003.
  • [10] Feng, M.-L., Tan, Y.-P.: Contrast adaptive binarization of low quality document images.
  • [11] M.A., R.-O., E., T., Ramirez-Ramirez, L.L., R., R., Cuevas, E.: Transition pixel: A concept for binarization based on edge detection and gray-intensity histograms. Pattern Recognition, 2010.
  • [12] Kherada, S., Namboodiri, A. M.: An ICA based Approach for Complex Color Scene Text Binarization.
  • [13] Thillou, B., Cline; Gosselin: Color binarization for complex camera-based images. In: Color Imaging X: Processing, Hardcopy, and Applications.
  • [14] Biswas, S., Das, A. K.: Text Extraction from Scanned Land Map Images. In: International Conference on Informatics, Electronics and Vision.
  • [15] Hai, T., lu Bao, Y.: Study on Extracting the Colors of Road Network Based on the Highway Traffic Map Images. In: .
  • [16] Watanabe, T., Zhang, R.: Recognition of Character Strings from Color Urban Map Images on the Basis of Validation Mechanism.
  • [17] Mandal, S., Biswas, S., Das, A., Chanda, B.: Land Map Image Dataset: Ground-Truth And Classification Using Visual And Textural Features. Image Processing & Communications, 2015.
  • [18] Gatos, B., Ntirogiannis, K., Pratikakis, I.: ICDAR 2009 Document Image Binarization Contest (DIBCO 2009). In: ICDAR, pp. 1375–1382. 2009.
  • [19] Ramírez-Ortegón, M. A., Tapia, E., Ramírez-Ramírez, L. L., Rojas, R., Cuevas, E.: Transition pixel: A concept for binarization based on edge detection and gray-intensity histograms. Pattern Recognition, 43, pp. 1233 – 1243, 2010.
  • [20] G. Lazzara, T. G.: Efficient Multiscale Sauvola’s Binarization. International Journal of Document Analysis and Recognition, 2013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-52f8bd58-c4e6-463f-851b-781194db055f
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