Binarization

Last modified: February 18, 2022

Contents

RegionInformation

gatos_background

Image [GreyScale] gatos_background (Image [OneBit] binarization, int region size = 15)

Operates on:Image [GreyScale]
Returns:Image [GreyScale]
Category:Binarization/RegionInformation
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Estimates the background of an image according to Gatos et al.'s method. See:

Gatos, Basilios, Ioannis Pratikakis, and Stavros J. Perantonis. 2004. An adaptive binarization technique for low quality historical documents. Lecture Notes in Computer Science 3163: 102--113.

region_size
Region size for interpolation.
binarization
A preliminary binarization of the image.

image_mean

float image_mean ()

Operates on:Image [GreyScale|Grey16|Float]
Returns:float
Category:Binarization/RegionInformation
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Returns the mean over all pixels of an image as a FLOAT.

image_variance

float image_variance ()

Operates on:Image [GreyScale|Grey16|Float]
Returns:float
Category:Binarization/RegionInformation
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Returns the variance over all pixels of an image as a FLOAT.

mean_filter

Image [Float] mean_filter (int region size = 5)

Operates on:Image [GreyScale|Grey16|Float]
Returns:Image [Float]
Category:Binarization/RegionInformation
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Returns the regional mean of an image as a FLOAT.

region_size
The size of the region in which to calculate a mean.

Example 1: mean_filter()

images/GreyScale_generic.png images/mean_filter_plugin_00.png

Example 2: mean_filter()

images/Grey16_generic.png images/mean_filter_plugin_01.png

Example 3: mean_filter()

images/GreyScale_generic.png images/mean_filter_plugin_02.png

variance_filter

Image [Float] variance_filter (Image [Float] means, int region size = 5)

Operates on:Image [GreyScale|Grey16|Float]
Returns:Image [Float]
Category:Binarization/RegionInformation
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Returns the regional variance of an image as a FLOAT.

means
Pre-calculated means for each region.
region_size
The size of the region in which to calculate the variance.

abutaleb_threshold

Image [OneBit] abutaleb_threshold (Choice [dense|rle] storage format)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:threshold.py
Author:Michael Droettboom and Karl MacMillan

Creates a binary image by using the Abutaleb locally-adaptive thresholding algorithm.

storage_format (optional)

specifies the compression type for the result:

DENSE (0)
no compression
RLE (1)
run-length encoding compression

Example 1: abutaleb_threshold()

images/GreyScale_generic.png images/abutaleb_threshold_plugin_00.png

bernsen_threshold

Image [OneBit] bernsen_threshold (Choice [dense|rle] storage format, int(1, 50) region size = 11, int(0, 255) contrast limit = 80, bool doubt_to_black = False)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:threshold.py
Author:Michael Droettboom and Karl MacMillan

Creates a binary image by using the Bernsen algorithm.

Each point is thresholded by the mean between the maximum and minimum value in the surrounding region of size region_size. When the difference between maximum and minimum is below contrast_limit the pixel is set to black in case of doubt_to_black = True, otherwise to white.

Reference: J. Bernsen: Dynamic thresholding of grey-level images. Proc. 8th International Conference on Pattern Recognition (ICPR8), pp. 1251-1255, 1986.

storage_format

specifies the compression type for the result:

DENSE (0)
no compression
RLE (1)
run-length encoding compression
region_size
The size of each region in which to calculate a threshold
contrast_limit
The minimum amount of contrast required to threshold.
doubt_to_black
When True, 'doubtful' values are set to black, otherwise to white.

Example 1: bernsen_threshold()

images/GreyScale_generic.png images/bernsen_threshold_plugin_00.png

brink_threshold

Image [OneBit] brink_threshold ()

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:binarization.py
Author:Johanna Devaney, Brian Stern

Calculates threshold for image with Brink and Pendock's minimum-cross entropy method and returns corrected image. It is best used for binarising images with dark, near-black foreground and significant bleed-through. To that end, it generally predicts lower thresholds than other thresholding algorithms.

Reference: A.D. Brink, N.E. Pendock: Minimum cross-entropy threshold selection. Pattern Recognition 29 (1), 1996. 179-188.


Example 1: brink_threshold()

images/GreyScale_generic.png images/brink_threshold_plugin_00.png

djvu_threshold

Image [OneBit] djvu_threshold (float(0.00, 1.00) smoothness = 0.20, int max_block_size = 512, int min_block_size = 64, int(1, 8) block_factor = 2)

Operates on:Image [RGB]
Returns:Image [OneBit]
Category:Binarization
Defined in:threshold.py
Author:Michael Droettboom and Karl MacMillan

Creates a binary image by using the DjVu thresholding algorithm.

See Section 5.1 in:

Bottou, L., P. Haffner, P. G. Howard, P. Simard, Y. Bengio and Y. LeCun. 1998. High Quality Document Image Compression with DjVu. AT&T Labs, Lincroft, NJ.

http://research.microsoft.com/~patrice/PDF/jei.pdf

This implementation features an additional extension to the algorithm described above. Once the background and foreground colors are determined for each block, the image is thresholded by interpolating the foreground and background colors between the blocks. This prevents "blockiness" along boundaries of strong color change.

smoothness
The amount of effect that parent blocks have on their children blocks. Higher values will result in more smoothness between blocks. Expressed as a percentage between 0.0 and 1.0.
max_block_size
The size of the largest block to determine a threshold.
min_block_size
The size of the smallest block to determine a threshold.
block_factor
The number of child blocks (in each direction) per parent block. For instance, a block_factor of 2 results in 4 children per parent.

Example 1: djvu_threshold(0.5, 512, 64, 2)

images/RGB_generic.png images/djvu_threshold_plugin_00.png

gatos_threshold

Image [OneBit] gatos_threshold (Image [GreyScale] background, Image [OneBit] binarization, float q = 0.60, float p1 = 0.50, float p2 = 0.80)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Thresholds an image according to Gatos et al.'s method. See:

Gatos, Basilios, Ioannis Pratikakis, and Stavros J. Perantonis. 2004. An adaptive binarization technique for low quality historical documents. Lecture Notes in Computer Science 3163: 102-113.

background
Estimated background of the image.
binarization
A preliminary binarization of the image.

Use the default settings for the other parameters unless you know what you are doing.

niblack_threshold

Image [OneBit] niblack_threshold (int region size = 15, float sensitivity = -0.20, int(0, 255) lower bound = 20, int(0, 255) upper bound = 150)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Creates a binary image using Niblack's adaptive algorithm.

Niblack, W. 1986. An Introduction to Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall.

Like the QGAR library, there are two extra global thresholds for the lightest and darkest regions.

region_size
The size of the region in which to calculate a threshold.
sensitivity
The sensitivity weight on the variance.
lower bound
A global threshold beneath which all pixels are considered black.
upper bound
A global threshold above which all pixels are considered white.

Example 1: niblack_threshold()

images/GreyScale_generic.png images/niblack_threshold_plugin_00.png

otsu_find_threshold

int otsu_find_threshold ()

Operates on:Image [GreyScale]
Returns:int
Category:Binarization
Defined in:threshold.py
Author:Michael Droettboom and Karl MacMillan

Finds a threshold point using the Otsu algorithm. Reference:

N. Otsu: A Threshold Selection Method from Grey-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics (9), pp. 62-66 (1979)


Example 1: otsu_find_threshold()

images/GreyScale_generic.png

result = 143

otsu_threshold

Image [OneBit] otsu_threshold (Choice [dense|rle] storage format)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:threshold.py
Author:Michael Droettboom and Karl MacMillan

Creates a binary image by splitting along a threshold value determined using the Otsu algorithm.

Equivalent to image.threshold(image.otsu_find_threshold()).

storage_format (optional)

specifies the compression type for the result:

DENSE (0)
no compression
RLE (1)
run-length encoding compression

Example 1: otsu_threshold()

images/GreyScale_generic.png images/otsu_threshold_plugin_00.png

sauvola_threshold

Image [OneBit] sauvola_threshold (int region size = 15, float sensitivity = 0.50, int(1, 255) dynamic range = 128, int(0, 255) lower bound = 20, int(0, 255) upper bound = 150)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:binarization.py
Author:John Ashley Burgoyne and Ichiro Fujinaga

Creates a binary image using Sauvola's adaptive algorithm.

Sauvola, J. and M. Pietikainen. 2000. Adaptive document image binarization. Pattern Recognition 33: 225--236.

Like the QGAR library, there are two extra global thresholds for the lightest and darkest regions.

region_size
The size of the region in which to calculate a threshold.
sensitivity
The sensitivity weight on the adjusted variance.
dynamic_range
The dynamic range of the variance.
lower bound
A global threshold beneath which all pixels are considered black.
upper bound
A global threshold above which all pixels are considered white.

Example 1: sauvola_threshold()

images/GreyScale_generic.png images/sauvola_threshold_plugin_00.png

shading_subtraction

Image [OneBit] shading_subtraction (int k = 7, int threshold = None)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:binarization.py
Author:Christoph Dalitz

Thresholds an image after subtracting a -possibly shaded- background.

First the background image is extracted with a maximum filter with a k*k window, and this image is subtracted from the original image. On the difference image, a threshold is applied, and the inverted image thereof is the binarization result.

Parameters:

k
Window size of the maximum filter. Must be odd. For decent results, it must be chosen so large that every window includes at least one background pixel.
threshold
Threshold applied to the difference image. A possibly reasonable value might lie around 20. When None, the threshold is determined automatically with otsu_find_threshold.

Reference: K.D. Toennies: Grundlagen der Bildverarbeitung. Pearson Studium, 2005, p.202


Example 1: shading_subtraction()

images/GreyScale_generic.png images/shading_subtraction_plugin_00.png

soft_threshold

Image [GreyScale] soft_threshold (int t = None, float sigma = 0.00, Choice [logistic|normal|uniform] dist = logistic)

Operates on:Image [GreyScale]
Returns:Image [GreyScale]
Category:Binarization
Defined in:threshold.py
Author:Christoph Dalitz

Does a greyscale transformation that "smears out" the threshold t by a choosable amount sigma. This has the effect of a "soft" thresholding.

Each grey value x is transformed to F(x,t,sigma), where F is the CDF probability distribution with mean t and variance sigma^2. The parameter dist determines the type of probability distribution: 0 = logistic, 1 = normal (gaussian), 2 = uniform.

As the choice sigma = 0 is useless (it is the same as normal thresholding), this special value is reserved for an automatic selection of sigma with soft_threshold_find_sigma.

When t is not given, it is automatically computed with otsu_find_threshold.

Reference: C. Dalitz: "Soft Thresholding for Visual Image Enhancement." Technischer Bericht Nr. 2014-01, Hochschule Niederrhein, Fachbereich Elektrotechnik und Informatik, 2014


Example 1: soft_threshold(128, 25)

images/GreyScale_generic.png images/soft_threshold_plugin_00.png

soft_threshold_find_sigma

float soft_threshold_find_sigma (int t = None, Choice [logistic|normal|uniform] dist = logistic)

Operates on:Image [GreyScale]
Returns:float
Category:Binarization
Defined in:threshold.py
Author:Christoph Dalitz

For the CDF probability distribution given by dist (0 = logistic, 1 = normal (gaussian), 2 = uniform), sigma is determined such that F(m,t,sigma) = 0.99, where m is the mean grey value of all pixels with a grey value greater than t.

Reference: C. Dalitz: "Soft Thresholding for Visual Image Enhancement." Technischer Bericht Nr. 2014-01, Hochschule Niederrhein, Fachbereich Elektrotechnik und Informatik, 2014

threshold

Image [OneBit] threshold (int threshold, Choice [dense|rle] storage format)

Operates on:Image [GreyScale|Grey16|Float]
Returns:Image [OneBit]
Category:Binarization
Defined in:threshold.py
Author:Michael Droettboom and Karl MacMillan

Creates a binary image by splitting along a given global threshold value.

Pixels that are greater than the given value become white. Pixels less than or equal to the given value become black.

storage_format (optional)

specifies the compression type for the result:

DENSE (0)
no compression
RLE (1)
run-length encoding compression.

Example 1: threshold(128)

images/GreyScale_generic.png images/threshold_plugin_00.png

tsai_moment_preserving_find_threshold

int tsai_moment_preserving_find_threshold ()

Operates on:Image [GreyScale]
Returns:int
Category:Binarization
Defined in:threshold.py
Author:Uma Kompella

Finds a threshold point using the Tsai Moment Preserving threshold algorithm. Reference:

W.H. Tsai: Moment-Preserving Thresholding: A New Approach. Computer Vision Graphics and Image Processing (29), pp. 377-393 (1985)


Example 1: tsai_moment_preserving_find_threshold()

images/GreyScale_generic.png

result = 153

tsai_moment_preserving_threshold

Image [OneBit] tsai_moment_preserving_threshold (Choice [dense|rle] storage format)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:threshold.py
Author:Uma Kompella

Creates a binary image by splitting along a threshold value determined using the Tsai Moment Preserving Threshold algorithm.

Equivalent to image.threshold(image.tsai_moment_preserving_find_threshold()).

storage_format (optional)

specifies the compression type for the result:

DENSE (0)
no compression
RLE (1)
run-length encoding compression

Example 1: tsai_moment_preserving_threshold()

images/GreyScale_generic.png images/tsai_moment_preserving_threshold_plugin_00.png

white_rohrer_threshold

Image [OneBit] white_rohrer_threshold (int x lookahead = 8, int y lookahead = 1, int bias mode = 0, int bias factor = 100, int f factor = 100, int g factor = 100)

Operates on:Image [GreyScale]
Returns:Image [OneBit]
Category:Binarization
Defined in:binarization.py
Author:Uma Kompella (using code from the XITE library)

Creates a binary image using White and Rohrer's dynamic thresholding algorithm. It is the first of the two algorithms described in:

J. M. White and G. D. Rohrer. 1983. Image thresholding for optical character recognition and other applications requiring character image extraction. IBM J. Res. Dev. 27(4), pp. 400-411

The algorithm uses a 'running' average instead of true average of the gray values in the neighborhood. The lookahead parameter gives the number of lookahead pixels used in the biased running average that is used in deciding the threshold at each pixel location.

x_lookahead
the number of lookahead pixels in the horizontal direction for computing the running average. White and Rohrer suggest a value of 8 for a 240 dpi scanning resolution.
y_lookahead
number of lines used for further averaging from the horizontal averages.

The other parameters are for calculating biased running average. Without bias the thresholding decision would be determined by noise fluctuations in uniform areas.

This implementation uses code from XITE.

Note

Permission to use, copy, modify and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that this copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation and that the name of B-lab, Department of Informatics or University of Oslo not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission.

B-LAB DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL B-LAB BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.


Example 1: white_rohrer_threshold()

images/GreyScale_generic.png images/white_rohrer_threshold_plugin_00.png