Investigation on optical surface defect extraction algorithm based on background correction and image segmentation method

2015 ◽  
Author(s):  
Bo Zhang ◽  
Fanyu Kong ◽  
Zhouling Wu ◽  
Shijie Liu
Keyword(s):  
Image Segmentation ◽  
Surface Defect ◽  
Background Correction ◽  
Optical Surface ◽  
Segmentation Method ◽  
Extraction Algorithm ◽  
Defect Extraction

Image segmentation method for bullet's primer surface defect

2013 ◽  
Vol 32 (8) ◽  
pp. 2320-2323 ◽  
Author(s):  
Jin-wei SHI ◽  
Chao-yong GUO ◽  
Hong-ning LIU
Keyword(s):  
Image Segmentation ◽  
Surface Defect ◽  
Segmentation Method

New Algorithm of Detecting Optical Surface Imperfection Based on Background Correction and Image Segmentation

2016 ◽  
Vol 36 (9) ◽  
pp. 0911004
Author(s):  
张博 Zhang Bo ◽  
倪开灶 Ni Kaizao ◽  
王林军 Wang Linjun ◽  
刘世杰 Liu Shijie ◽  
吴伦哲 Wu Lunzhe
Keyword(s):  
Image Segmentation ◽  
Background Correction ◽  
Optical Surface

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

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.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

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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