Numpy/Scipy implementations of state-of-the-art image thresholding algorithms
PyThreshold is a python package featuring Numpy/Scipy implementations of state-of-the-art image thresholding algorithms.
PyThreshold can be easily installed by typing the following command
pip install pythreshold
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)
- 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).
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