Image thresholding using type II fuzzy sets

By Hamid R. Tizhoosh. Pattern Recognition, 2005

322 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Intuitionistic fuzzy information - Applications to pattern recognition (2007, methodology): “(a) Test image (adopted from Tizhoosh (2005)) and (b) ideal man information measure (Topt = 35) and (d) its proposed intuitionistic counterpar”
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation (2022, methodology): “Existing classic segmentationalgorithmsare based on edge detection, thresholding, morphology, distances between two objects and pixel energy, such as Otsu thresholding [6], Snake [7] and Fuzzy algorithms [8].”
Opposition-Based Differential Evolution (2008, methodology): “Numerous thresholding techniques have already been proposed [Sezgin and Sankur 2004; Cheng et al. 2001; Tizhoosh 2005a; Rahnamayan et al. 2006h].”
A Historical Account of Types of Fuzzy Sets and Their Relationships (2016, methodology): “In this case, The use of IVFSs leads to improved segmentation or detection of features [11], [30], [31], [32], [59], [109], [136], [162].”