An effective image segmentation method for noisy low-contrast unbalanced background in Mura defects using balanced discrete-cosine-transfer (BDCT)

2013 ◽  
Vol 37 (2) ◽  
pp. 336-344 ◽  
Author(s):  
Liang-Chia Chen ◽  
Chih-Hung Chien ◽  
Xuan-Loc Nguyen
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
Low Contrast

Vein Image Segmentation Based on One-Dimentional Gray and Filter Erosion Method

2014 ◽  
Vol 635-637 ◽  
pp. 1049-1055 ◽  
Author(s):  
Xun Zhang ◽  
Yong Hong Guo ◽  
Gang Li ◽  
Jin Long He
Keyword(s):  
Image Segmentation ◽  
Test Results ◽  
Segmentation Method ◽  
Low Contrast ◽  
One Dimensional ◽  
Hand Vein ◽  
Binary Morphology

For the low contrast and serious noises, a fast image segmentation method based on one-dimensional gray segmentation, binary morphology erosion and area elimination is proposed. Since veins are thin and long, the vein image can be easily distinguished from background by judging the gray difference from nearby pixels when they are vertically or horizontally scanned. Then the processed image is diposed with erosion and area elimination to filter the noise. According to test results on the hand vein images which got from the equipment constructed by ourselves, it is proved that the method is more suitable for hand vein image segmentation than others and clear vein images can be botained quickly.

An Effective X-Ray Image Segmentation Method for the Pharmaceutical Industry via Surface Fitting

2013 ◽  
Vol 333-335 ◽  
pp. 839-844
Author(s):  
Kai Hong Shi ◽  
Zong Qing Lu ◽  
Qing Min Liao
Keyword(s):  
Image Segmentation ◽  
Pharmaceutical Industry ◽  
Active Contour Model ◽  
Three Dimensional ◽  
Surface Fitting ◽  
Segmentation Method ◽  
Image Subtraction ◽  
Low Contrast ◽  
X Ray ◽  
Fixed Condition

Image segmentation techniques currently used for X-ray inspection in pharmaceutical industry suffer from some limitations. The object in an image is close to the background and its contours are weak or blurred because of the X-ray imaging characteristic. Based on our research of X-ray inspection, a simple and efficient image segmentation method is proposed in this paper. It is implemented by treating the image and desired contours as three dimensional surface and holes respectively in order to simplify the model of segmentation, and making use of surface fitting and image subtraction to extract the target region efficiently. The novelty of this approach is that we need less selection of parameters to extract contours with low contrast by surface fitting. Experiments on real X-ray images demonstrate the advantages of the proposed method over active contour model (ACM) and Chan_Vese model (CV model) in terms of both accuracy and efficiency on fixed condition.

scholarly journals Concrete Image Segmentation Based on Multiscale Mathematic Morphology Operators and Otsu Method

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Sheng-Bo Zhou ◽  
Ai-Qin Shen ◽  
Geng-Fei Li
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Noise Suppression ◽  
Segmentation Method ◽  
Low Contrast ◽  
Otsu Method ◽  
Edge Detectors ◽  
Mathematic Morphology ◽  
Threshold Algorithms ◽  
Concrete Image

The aim of the current study lies in the development of a reformative technique of image segmentation for Computed Tomography (CT) concrete images with the strength grades of C30 and C40. The results, through the comparison of the traditional threshold algorithms, indicate that three threshold algorithms and five edge detectors fail to meet the demand of segmentation for Computed Tomography concrete images. The paper proposes a new segmentation method, by combining multiscale noise suppression morphology edge detector with Otsu method, which is more appropriate for the segmentation of Computed Tomography concrete images with low contrast. This method cannot only locate the boundaries between objects and background with high accuracy, but also obtain a complete edge and eliminate noise.

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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