The Study on the Image Thresholding Segmentation Algorithm
Yue Liu,Jia-mei Xue,Hua Li +2 more
- 01 Apr 2015
- pp 2306-2310
TL;DR: The workings of several common thresholding segmentation methods are discussed and their respective strength and weakness are summarized from the perspective of experiments.
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Abstract: Image segmentation is an important branch of image processing. Among numerous segmentation techniques, thresholding is a very important and effective one which segments different objects using a threshold. This paper discusses workings of several common thresholding segmentation methods and summarizes their respective strength and weakness from the perspective of experiments.
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References
•Journal Article
Achieve and Comparison of Image Segmentation Thresholding Method
TL;DR: Several commonly used image segmentation algorithms and theory are discussed and to study the aggregate asphalt mixture characteristics of the background, experimental results are shown to compare histogram threshold, iteration method and the Otsu.
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Connectivity in Digital Pictures
TL;DR: It is shown that every simply-connected object in such a picture has elements which can be deleted without destroying its simple- connectedness, which makes it easy to prove that a well-known "shrinking" algorithm always works--that is, shrinks any simply- connected object down to a single element.