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Numpy/Scipy implementations of state-of-the-art image thresholding algorithms

Project description

PyThreshold

PyThreshold is a python package featuring Numpy/Scipy implementations of state-of-the-art image thresholding algorithms.

Installing

PyThreshold can be easily installed by typing the following command

pip install pythreshold

Usage

from pythreshold.utils import test_thresholds
from scipy.misc import ascent

# Testing all the included thresholding algorithms
test_thresholds()

# Testing all the included thresholding algorithms using a custom image
img = ascent()
test_thresholds(img)

Included Algorithms

  • Global thresholding
    • Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons. (Two peaks)
    • Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons. (p-tile)
    • Otsu, Nobuyuki. "A threshold selection method from gray-level histograms." IEEE transactions on systems, man, and cybernetics 9.1 (1979): 62-66.
    • Kittler, J. and J. Illingworth. "On Threshold Selection Using Clustering Criteria,"" IEEE Transactions on Systems, Man, and Cybernetics 15, no. 5 (1985): 652–655.
    • Entropy thresholding
      • Johannsen, G., and J. Bille "A Threshold Selection Method Using Information Measures,"" Proceedings of the Sixth International Conference on Pattern Recognition, Munich, Germany (1982): 140–143.
      • Kapur, J. N., P. K. Sahoo, and A. K. C.Wong. "A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram,"" Computer Vision, Graphics, and Image Processing 29, no. 3 (1985): 273–285.
      • Pun, T. "A New Method for Grey-Level Picture Thresholding Using the Entropy of the Histogram,"" Signal Processing 2, no. 3 (1980): 223–237.
  • Local thresholding
    • Bernsen, J (1986), "Dynamic Thresholding of Grey-Level Images", Proc. of the 8th Int. Conf. on Pattern Recognition
    • Bradley, D., & Roth, G. (2007). Adaptive thresholding using the integral image. Journal of Graphics Tools, 12(2), 13-21.
    • Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons. (Contrast thresholding)
    • Meng-Ling Feng and Yap-Peng Tan, "Contrast adaptive thresholding of low quality document images", IEICE Electron. Express, Vol. 1, No. 16, pp.501-506, (2004).
    • Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons. (Local mean thresholding)
    • Niblack, W.: "An introduction to digital image processing" (Prentice- Hall, Englewood Cliffs, NJ, 1986), pp. 115–116
    • Sauvola, J., Seppanen, T., Haapakoski, S., and Pietikainen, M.: "Adaptive document thresholding". Proc. 4th Int. Conf. on Document Analysis and Recognition, Ulm Germany, 1997, pp. 147–152.
    • Singh, O. I., Sinam, T., James, O., & Singh, T. R. (2012). Local contrast and mean based thresholding technique in image binarization. International Journal of Computer Applications, 51, 5-10.
    • C. Wolf, J-M. Jolion, "Extraction and Recognition of Artificial Text in Multimedia Documents", Pattern Analysis and Applications, 6(4):309-326, (2003).

Additional Information

Do you find PyThreshold useful? You can collaborate with us:

GitHub

Additional materials and information can be found at:

ResearchGate

Project details


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