A novel SAR fusion image segmentation method based on triplet Markov field

2015 ◽  
Author(s):  
Jiajing Wang ◽  
Shuhong Jiao ◽  
Zhenyu Sun
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
Fusion Image ◽  
Markov Field

scholarly journals Image Segmentation Using the EM/MPM Algorithm

2021 ◽  
pp. 618-622
Author(s):  
Hanane DALIMI ◽  
Mohamed AFIFI ◽  
Said AMAR
Keyword(s):  
Image Segmentation ◽  
Estimation Method ◽  
Segmentation Method ◽  
Histogram Method ◽  
Markov Field ◽  
Unsupervised Image Segmentation

In this article we propose to place our work in a Markovian framework for unsupervised image segmentation. We give one of the procedures for estimating the parameters of a Markov field, we limit the work to the EM estimation method and the Posterior Marginal Maximization (MPM) segmentation method. Estimating the number of regions who compones the image is relatively difficult, we try to solve this problem by the K-means Histogram method.

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.

scholarly journals Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization

Plant Methods ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiong Xiong ◽  
Lingfeng Duan ◽  
Lingbo Liu ◽  
Haifu Tu ◽  
Peng Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Segmentation Method ◽  
Robust Image ◽  
Rice Panicles
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