A Fast Algorithm for Multilevel Thresholding

By Ping-Sung Liao, Tse-Sheng Chen, Pau-Choo Julia Chung. Journal of information science and engineering, 2001

1,151 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/b80914dbbee9f6b2455742d8117417731e6ecf12

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Automatic thresholding for defect detection (2004, methodology): “An equivalent, but simpler formulation for the Otsu method is given in Liao et al. (2001). The simplified formula for obtaining optimal threshold t* is computed as follows:”
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm (2006, methodology): “Otsu’s method (1979) chooses optimal thresholds by maximizing the between class variance. Sahoo et al. (1988) found that in global thresholding, Otsu’s method is one of the better threshold selection methods for general real world images with regard to uniformity and shape measures.”
An Efficient Algorithm for Fractal Analysis of Textures (2012, methodology): “This is achieved by selecting pairs of thresholds from T and applying a two-threshold segmentation as follows: Ib(x, y) = { 1 if t` I(x, y) ≤ tu 0, otherwise.”
HTPheno: An image analysis pipeline for high-throughput plant phenotyping (2011, methodology): “Hence tests with an automatic segmentation method, the ImageJ plugin multi otsu threshold [8], an implementation of the otsu threshold algorithm to find up to 5 optimal threshold level (multilevel) of an image [9], does not deliver the required thresholds.”
Otsu Method and K-means (2009, methodology): “So there are many multilevel thresholding methods [2] [3] [6] [8] [13] [14] [15], which perform better than bi-level thresholding method when the picture has complex objects or background.”
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, methodology): “Classical thresholding methods, such as [9] and [10], have been applied to determine the threshold in an unsupervised manner.”
Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “Therefore, the methods are implemented in an efficient way by using lookup tables to store each term of a specific objective function for every possible pair of class boundaries, and employ these tables to calculate the objective function for an arbitrary number of classes [Liao et al., 2001].”
9) TM4 Microarray Software Suite (2006, methodology): “Spotfinder introduced a segmentation method based on the Otsu algorithm (Liao et al., 2001; Otsu, 1979) to address this problem.”