A narrow band interval type-2 fuzzy approach for image segmentation

2016 ◽  
Vol 64 ◽  
pp. 86-99 ◽  
Author(s):  
Jiao Shi ◽  
Yu Lei ◽  
Ying Zhou
Keyword(s):  
Image Segmentation ◽  
Narrow Band ◽  
Fuzzy Approach ◽  
Interval Type

Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means

2007 ◽  
Vol 15 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Cheul Hwang ◽  
Frank Chung-Hoon Rhee
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Approach ◽  
Interval Type

scholarly journals INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 385-391 ◽  
Author(s):  
Chunyan Wang ◽  
Aigong Xu ◽  
Chao Li ◽  
Xuemei Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Fuzzy Model ◽  
Remote Sensing Image ◽  
Fuzzy Membership ◽  
Fuzzy Membership Functions ◽  
Interval Type

Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

scholarly journals An Interval Type-2 Possibilistic C-Means Clustering Algorithm and Its Application

2021 ◽  
Vol 2132 (1) ◽  
pp. 012016
Author(s):  
Haihua Xing ◽  
Huannan Chen ◽  
Hongyan Lin ◽  
Xinghui Wu
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Clustering Algorithm ◽  
High Order ◽  
Data Sets ◽  
Fuzzy Uncertainty ◽  
Penalty Factor ◽  
Interval Type

Abstract In this paper, we aim at the fuzzy uncertainty caused by noise in pattern data. The advantages of PCM algorithm to deal with noise and interval type-2 fuzzy sets to deal with high-order uncertainties are used, respectively. An interval type-2 probability C-means clustering (IT2-PCM) based on penalty factor is proposed. The performance of the algorithm is evaluated by two sets of data sets and two groups of images segmentation experiments. The results show that IT2-PCM algorithm can assign proper membership degrees to clustering samples with noise, and it can detect noise points effectively, and it has good performance in image segmentation.

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