Low-Contrast Image Segmentation by Using of the Type-2 Fuzzy Clustering Based on the Membership Function Statistical Characteristics

2019 ◽  
pp. 689-700
Author(s):  
Lyudmila Akhmetshina ◽  
Artyom Yegorov
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Fuzzy Clustering ◽  
Statistical Characteristics ◽  
Low Contrast ◽  
Contrast Image ◽  
The Membership Function

Fuzzy Clustering-Based Approaches in Automatic Lip Segmentation from Color Images

2006 ◽  
pp. 292-317
Author(s):  
Shilin Wang ◽  
Wing Hong Lau ◽  
Alan Wee-Chung Liew ◽  
Shu Hung Leung
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Visual Information ◽  
Spatial Information ◽  
Noisy Environments ◽  
Low Contrast ◽  
Speaker Authentication ◽  
Lip Segmentation ◽  
Traditional Image

Recently, lip image analysis has received much attention because the visual information extracted has been shown to provide significant improvement for speech recognition and speaker authentication, especially in noisy environments. Lip image segmentation plays an important role in lip image analysis. This chapter will describe different lip image segmentation techniques, with emphasis on segmenting color lip images. In addition to providing a review of different approaches, we will describe in detail the state-of-the-art classification-based techniques recently proposed by our group for color lip segmentation: “Spatial fuzzy c-mean clustering” (SFCM) and “fuzzy c-means with shape function” (FCMS). These methods integrate the color information along with different kinds of spatial information into a fuzzy clustering structure and demonstrate superiority in segmenting color lip images with natural low contrast in comparison with many traditional image segmentation techniques.

FUZZY CLUSTERING OF FEATURE VECTORS WITH SOME ORDINAL VALUED ATTRIBUTES USING GRADIENT DESCENT FOR LEARNING

2008 ◽  
Vol 16 (02) ◽  
pp. 195-218
Author(s):  
ROELOF K. BROUWER
Keyword(s):  
Objective Function ◽  
Membership Function ◽  
Fuzzy Clustering ◽  
Gradient Descent ◽  
Ratio Scale ◽  
Interval Scale ◽  
Real Numbers ◽  
Fuzzy C Means Clustering ◽  
Feature Values ◽  
The Membership Function

There are well established methods for fuzzy clustering especially for the cases where the feature values are numerical of ratio or interval scale. Not so well established are methods to be applied when the feature values are ordinal or nominal. In that case there is no one best method it seems. This paper discusses a method where unknown numeric variables are assigned to the ordinal values. Part of minimizing an objective function for the clustering is to find numeric values for these variables. Thus real numbers of interval scale and even ratio scale for that matter are assigned to the original ordinal values. The method uses the same objective function as used in fuzzy c-means clustering but both the membership function and the ordinal to real mapping are determined by gradient descent. Since the ordinal to real mapping is not known it cannot be verified for its legitimacy. However the ordinal to real mapping that is found is best in terms of the clustering produced. Simulations show the method to be quite effective.

scholarly journals Uncertainty of Interval Type-2 Fuzzy Sets Based on Fuzzy Belief Entropy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1265
Author(s):  
Sicong Liu ◽  
Rui Cai
Keyword(s):  
Fuzzy Sets ◽  
Membership Function ◽  
Specific Objective ◽  
Open Issue ◽  
Belief Entropy ◽  
Interval Type ◽  
Fuzzy Subsets ◽  
The Membership Function

Interval type-2 fuzzy sets (IT2 FS) play an important part in dealing with uncertain applications. However, how to measure the uncertainty of IT2 FS is still an open issue. The specific objective of this study is to present a new entropy named fuzzy belief entropy to solve the problem based on the relation among IT2 FS, belief structure, and Z-valuations. The interval of membership function can be transformed to interval BPA [Bel,Pl]. Then, Bel and Pl are put into the proposed entropy to calculate the uncertainty from the three aspects of fuzziness, discord, and nonspecificity, respectively, which makes the result more reasonable. Compared with other methods, fuzzy belief entropy is more reasonable because it can measure the uncertainty caused by multielement fuzzy subsets. Furthermore, when the membership function belongs to type-1 fuzzy sets, fuzzy belief entropy degenerates to Shannon entropy. Compared with other methods, several numerical examples are demonstrated that the proposed entropy is feasible and persuasive.

A Type-2 Fuzzy Clustering and Quantum Optimization Approach for Crops Image Segmentation

2021 ◽  
Author(s):  
Yo-Ping Huang ◽  
Pritpal Singh ◽  
Wen-Lin Kuo ◽  
Hung-Chi Chu
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Optimization Approach ◽  
Quantum Optimization

Combined Probabilistic and Possibilistic Used to a Build Type-2 Fuzzy Clustering Algorithm Model

2013 ◽  
Vol 284-287 ◽  
pp. 3060-3069
Author(s):  
Leehter Yao ◽  
Kuei Sung Weng
Keyword(s):  
Fuzzy Sets ◽  
Membership Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Clustering Algorithm ◽  
Noise Data ◽  
Membership Value ◽  
Will Force

In a noise environment probabilistic fuzzy clustering will force the noise into one or more clusters, seriously influencing the main dataset structure. We extend Type-1 membership values to Type-2 by assigning a possibilistic-membership function to each Type-1 membership value. The idea in building the Type-2 fuzzy sets is based simply on the fact that, for the same Type-1 membership value, the secondary membership function should make the larger possibility value greater than the smaller possibility value. This paper presents an efficient combined probabilistic and possibilistic method for building Type-2 fuzzy sets. Utilizing this concept we present a Type-2 FCM (T2FCM) that is an extension of the conventional FCM. The experimental results show that the T2FCM is less susceptible to noise than the Type-1 FCM. The T2FCM can ignore the inlier and outlier interrupt. The clustering results show the robustness of the proposed T2FCM because a reasonable amount of noise data does not affect its clustering performance.

Environmental data analysis based on fuzzy clustering method

2020 ◽  
pp. 002072092092854
Author(s):  
Yongyi Li ◽  
Zhongqiang Yang ◽  
Kaixu Han
Keyword(s):  
Membership Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Theory ◽  
Living Environment ◽  
Environmental Data ◽  
Fuzzy Evaluation ◽  
Clustering Method ◽  
Fuzzy Clustering Method ◽  
The Membership Function

With the growing complexity of the human living environment, the environment-related industries have also been flourishing. Clustering of environmental data is a key task of environment research. The environmental data are characterized by diversification, boundary fuzzification, incompleteness, etc. This paper conducts a cluster analysis of environmental data based on fuzzy theory. To start with, the basic principle of fuzzy theory is analyzed, and the focus is on studying the membership function and the [Formula: see text]-cut-set knowledge. Following that, the fuzzy clustering method and its process are studied. Finally, fuzzy evaluation is used to build the membership function after experiment on the MATLAB platform to evaluate the environmental quality. The fuzzy C-means clustering algorithm is used to realize the target identification of environmental data. In the process of fuzzy clustering, fuzzy evaluation of the seawater quality is realized, and the redundant data of the monitoring station are removed. Through experiment and analysis, experimental results are in line with the practical situations and show a high consistency with the data characteristics. Compared with the traditional clustering algorithm, fuzzy clustering is more suitable for environmental data processing in environmental data research and analysis.

A Novel Method to Detect the Tumor Using Low-Contrast Image Segmentation Technique

2021 ◽  
pp. 161-170
Author(s):  
Priyanka Kaushik ◽  
Rajeev Ratan
Keyword(s):  
Image Segmentation ◽  
Low Contrast ◽  
Segmentation Technique ◽  
Novel Method ◽  
Contrast Image

The Application and Improve of Fuzzy Clustering in Classifying Control Targets

2014 ◽  
Vol 496-500 ◽  
pp. 1333-1339
Author(s):  
Chang Liu ◽  
Jie Li ◽  
Ji Zhao ◽  
Zhi Dong Zhang
Keyword(s):  
Membership Function ◽  
Fuzzy Clustering ◽  
Control Object ◽  
Object Model ◽  
Experiment Data ◽  
Data Simulation ◽  
Simulation Data ◽  
Aerial Vehicle ◽  
Clustering Approach ◽  
The Membership Function

In this paper, a new clustering method is designed to solve the problem that control object model of a small aerial vehicle (SAV) will be changing accompany the changing magnetic field. The method is based on the establishment of fuzzy clustering approach. In this method, the variation of object model is used to establish the membership function to reflect the changing speed of the targets, and use experimental data to determine the parameters of the function which can ensure the authenticity of the membership function; using the robustness of the control system as the criteria for determining the cut-off matrix in order to ensure the reliability of the classification results. According to the test, the method can be classify the objects, according to prior experiment data, simulation data and the criteria of classification.

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