An interval type 2 fuzzy approach to multilevel image segmentation

Neog, Debanga Raj; Raza, Muhammad Amjad; Rhee, Frank Chung-Hoon

doi:10.1109/fuzzy.2011.6007728

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…Since, data analysis and computer vision problems, analyzing and dealing the uncertainties are a very important issue, FCM is being widely used in these fields. Several methods of other IT2 approach for pattern recognition algorithms have been successfully reported [35][36][37][38][39][40][41]. Type-1 fuzzy sets cannot deal uncertainties therefore; type-2 fuzzy sets were defined to represent the uncertainties associated with type-1 fuzzy sets.…”

Section: Advanced Fuzzy Clustering Algorithmmentioning

confidence: 99%

Data Clustering for Fuzzyfier Value Derivation

Cho¹

2022

Fuzzy Systems - Theory and Applications

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The fuzzifier value m is improving significant factor for achieving the accuracy of data. Therefore, in this chapter, various clustering method is introduced with the definition of important values for clustering. To adaptively calculate the appropriate purge value of the gap type −2 fuzzy c-means, two fuzzy values m1 and m2 are provided by extracting information from individual data points using a histogram scheme. Most of the clustering in this chapter automatically obtains determination of m1 and m2 values that depended on existent repeated experiments. Also, in order to increase efficiency on deriving valid fuzzifier value, we introduce the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), as one of advanced fuzzy clustering method to classify a fixed pattern. In Efficient IT2PFCM method, proper fuzzifier values for each data is obtained from an algorithm including histogram analysis and Gaussian Curve Fitting method. Using the extracted information form fuzzifier values, two modified fuzzifier value m1 and m2 are determined. These updated fuzzifier values are used to calculated the new membership values. Determining these updated values improve not only the clustering accuracy rate of the measured sensor data, but also can be used without additional procedure such as data labeling. It is also efficient at monitoring numerous sensors, managing and verifying sensor data obtained in real time such as smart cities.

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Section: Advanced Fuzzy Clustering Algorithmmentioning

confidence: 99%

Data Clustering for Fuzzyfier Value Derivation

Cho¹

2022

Fuzzy Systems - Theory and Applications

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“…In [32], new modified thresholding measures for MRI brain images using type 1 and type 2 fuzzy sets are presented. An interval type 2 (IT2) fuzzy entropy based approach is used to compute optimum thresholds for multistage gray scale image segmentation in [33]. An automatic leukocyte segmentation using intuitionistic fuzzy and interval Type II fuzzy set theory in pathological blood cell images is presented in [34].…”

Section: Related Workmentioning

confidence: 99%

Color Image Segmentation Using Fuzzy C-Regression Model

Chen

Ludwig

2017

Advances in Fuzzy Systems

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Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms.

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Interval type-2 approach to kernel possibilistic C-means clustering

Raza

Rhee

2012

2012 IEEE International Conference on Fuzzy Systems

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An interval type 2 fuzzy approach to multilevel image segmentation

Cited by 5 publications

References 41 publications

Data Clustering for Fuzzyfier Value Derivation

Data Clustering for Fuzzyfier Value Derivation

Color Image Segmentation Using Fuzzy C-Regression Model

Interval type-2 approach to kernel possibilistic C-means clustering

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