scholarly journals Unsupervised change detection method in SAR images based on deep belief network using an improved fuzzy C‐means clustering algorithm

2020 ◽  
Author(s):  
Sanae Attioui ◽  
Said Najah
Keyword(s):  
Change Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Deep Belief Network ◽  
Sar Images ◽  
Belief Network ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

SAR image change detection method based on intuitionistic fuzzy C-means clustering algorithm

2020 ◽  
Vol 38 (4) ◽  
pp. 3595-3604
Author(s):  
Deshuai Yin ◽  
Rui Hou ◽  
Junchao Du ◽  
Liang Chang ◽  
Hongxuan Yue ◽  
...  
Keyword(s):  
Change Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Sar Image ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Image Change Detection

scholarly journals Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering

2021 ◽  
Vol 21 (2) ◽  
pp. 45-57
Author(s):  
J. Thrisul Kumar ◽  
B. M. S. Rani ◽  
M. Satish Kumar ◽  
M. V. Raju ◽  
K. Maria Das
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Geographical Area ◽  
Ground Truth ◽  
Sar Images ◽  
Fuzzy C Means ◽  
Difference Image ◽  
Multi Temporal ◽  
Fuzzy C Means Clustering ◽  
Log Ratio

Abstract In this paper, the main objective is to detect changes in the geographical area of Ottawa city in Canada due to floods. Two multi-temporal Synthetic Aperture Radar (SAR) images have been taken to evaluate the un-supervised change detection process. In this process, two ratio operators named as Log-Ratio and Mean-Ratio are used to generate a difference image. Performing image fusion based on DWT by selecting optimum filter coefficients by satisfying the wavelet filter coefficient properties through a novel image fusion technique is named as ADWT. GA, PSO, AntLion Optimization algorithms (ALO) and Hybridized AntLion Algorithm (HALO) have been adapted to perform the ADWT based image fusion. Segmentation has been performed based on fuzzy c-Means clustering to detect changed and unchanged pixels. Finally, the performance of the proposed method will be analysed by comparing the segmented image with the ground truth image in terms of sensitivity, accuracy, specificity, precision, F1-score.

scholarly journals Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network

2020 ◽  
Vol 8 (18) ◽  
pp. 1165-1165
Author(s):  
He-Hua Zhang ◽  
Li Yang ◽  
An-Hai Wei ◽  
Ao-Wen Duan ◽  
Yong-Ming Li ◽  
...  
Keyword(s):  
Deep Belief Network ◽  
Automatic Identification ◽  
Belief Network ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

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