Coal gangue image segmentation method based on edge detection theory of star algorithm

Author(s):  
Xinquan Wang ◽  
Shuang Wang ◽  
Yongcun Guo ◽  
Kun Hu ◽  
Wenshan Wang
2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.


2010 ◽  
Vol 97-101 ◽  
pp. 4408-4411
Author(s):  
Tian Hou Zhang ◽  
Chang Chun Li ◽  
Shi Feng Wang

According to the features of material bag image, the paper compares an analyzes the detection effects of different edge detection operators detecting material bag image. A new image segmentation method is proposed to combine Sobel edge detection operator and iterative threshold. The method can extract edge information of material bag image efficiently and provide a theoretical basis for the robot automatic recognition of material bags technique.


2018 ◽  
Vol 76 ◽  
pp. 246-254 ◽  
Author(s):  
Tomislav Matić ◽  
Ivan Aleksi ◽  
Željko Hocenski ◽  
Dieter Kraus

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.


2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.


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