Abstract
Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.
Highlights
Thresholding is used in separating the foreground from the background of an image
The binarization of the image is accomplished based solely on some fuzzy inclusion and entropy measurements and we don’t use any information regarding the histogram of our input
In the previous two sections, we saw how fuzzy inclusion and entropy measures could efficiently be used as inputs of an inference system and how someone could benefit from setting the thresholding targets on his own
Summary
Thresholding is used in separating the foreground (which contains one or more objects) from the background of an image. In an attempt to prove the applicability of these measures as well as to support our belief that they could offer us a different approach to the solution of a particular problem, we introduced a general algorithm of global image thresholding. This method efficiently used some of our indicators and was based entirely on them. The first one tries to enhance specific binarizations of Otsu’s method on a particular set of images and the second one attempts to approach some more specialized results which are mainly focused on edge detection These examples are only indicative and a visual expert could estimate this whole process better and more accurately. ANFIS, Global Thresholding Techniques and the Main Parts of Our Previous Research
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