Cell Image Segmentation Method Based on Convolution Neural Network

2019 ◽  
Author(s):  
Chen Tianjun
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolution Neural Network ◽  
Segmentation Method ◽  
Cell Image

scholarly journals An Advanced Road Structure Image Segmentation Method using Swt in Cnn

2019 ◽  
Vol 9 (2S3) ◽  
pp. 114-118
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
High Speed ◽  
Learning Phase ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Aerial Image ◽  
Segmentation Method ◽  
Stroke Width ◽  
Segmentation Approach

In this paper, the design of advanced road structure image segmentation approach using stroke width transformation (SWT) in convolution neural network (CNN) is proposed. The main intent of the proposed system is to acquire the aerial images for the vehicle. Basically, this image segmentation performs its operation in two forms they are operating phase and learning phase. Here the aerial image has enhanced by using the SWT transformation. Hence the main advantage of this proposes system is that it processes the entire operation in simple way with high speed. The SWT will capture the images of road areas in effective way. Hence the propose system has various features which will determine the color, width and many other.

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.

T1w MRI Brain Tumor Image Segmentation using Active Contour Models and Convolution Neural Network Fusion

2021 ◽  
Author(s):  
Vamisdhar Entireddy ◽  
Babu K Rajesh ◽  
R Sampathkumar ◽  
Jyothirmai Gandeti ◽  
Syed Shameem ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Active Contour ◽  
Convolution Neural Network ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models
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