Thresholding (image processing): Difference between revisions

Revision as of 23:39, 28 April 2019

Revision as of 17:59, 8 July 2019

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* '''[[Histogram]] shape'''-based methods, where, for example, the peaks, valleys and curvatures of the smoothed histogram are analyzed

* '''Clustering'''-based methods, where the gray-level samples are clustered in two parts as background and foreground (object), or alternately are modeled as a mixture of two Gaussians

* '''[[Entropy (information theory)|Entropy]]'''-based methods result in algorithms that use the entropy of the foreground and background regions, the cross-entropy between the original and binarized image, etc.<ref>{{cite journal|last1=Zhang|first1=Y.|title=Optimal multi-level Thresholding based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach|journal=Entropy|date=2011|volume=13|issue=4|pages=841–859|url=http://www.mdpi.com/1099-4300/13/4/841|doi=10.3390/e13040841|bibcode=2011Entrp..13..841Z}}</ref>

* '''Object Attribute'''-based methods search a measure of similarity between the gray-level and the binarized images, such as fuzzy shape similarity, edge coincidence, etc.

* '''Spatial''' methods [that] use higher-order probability distribution and/or correlation between pixels

* '''Local''' methods adapt the threshold value on each pixel to the local image characteristics. In these methods, a different T is selected for each pixel in the image.

*'''Hybrid''' methods use both global and local threshold value and adapt each pixel value based on both local and global image characteristics.<ref>{{Citation|last=Sokratis|first=Vavilis|title=A Hybrid Binarization Technique for Document Images|date=2011|url=http://dx.doi.org/10.1007/978-3-642-22913-8_8|work=Learning Structure and Schemas from Documents|pages=165–179|publisher=Springer Berlin Heidelberg|isbn=9783642229121|access-date=2019-04-28|last2=Kavallieratou|first2=Ergina|last3=Paredes|first3=Roberto|last4=Sotiropoulos|first4=Kostas|doi=10.1007/978-3-642-22913-8_8}}</ref>

==Multiband thresholding==

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== Automatic thresholding ==

Automatic thresholding is a great way to extract useful information encoded into pixels while minimizing background noise. This is accomplished by utilizing a feedback loop to optimize the threshold value before converting the original grayscale image to binary. The idea is to separate the image into two parts; the background and foreground.<ref>{{Cite book|url=https://www.worldcat.org/oclc/1016899766|title=Digital Image Processing and Analysis with MATLAB and CVIPtools, Third Edition|last=E.,|first=Umbaugh, Scott|isbn=9781498766074|edition=3rd|oclc=1016899766|date = 2017-11-30}}</ref>

# Select initial threshold value, typically the mean 8-bit value of the original image.

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# If the difference between the previous threshold value and the new threshold value are below a specified limit, you are finished. Otherwise apply the new threshold to the original image keep trying.

To assist the automatic threshold, optimization tools can be applied.<ref>{{Cite journal|last=Sokratis|first=Vavilis|last2=Kavallieratou|first2=Ergina|date=September 2011|title=A Tool for Tuning Binarization Techniques|url=http://dx.doi.org/10.1109/icdar.2011.10|journal=2011 International Conference on Document Analysis and Recognition|pages=1–5|publisher=IEEE|doi=10.1109/icdar.2011.10|isbn=9781457713507}}</ref>

=== Note about limits and threshold selection ===