Bayesian entropy estimation: Applications in robust image filtering

2012 ◽  
Author(s):  
Jose Ismael de la Rosa Vargas ◽  
Jesus Villa ◽  
Efren Gonzalez ◽  
Ovaldo Gutierrez ◽  
Enrique de la Rosa
Keyword(s):  
Image Filtering ◽  
Entropy Estimation ◽  
Robust Image

scholarly journals MAP entropy estimation: applications in robust image filtering

2013 ◽  
Vol 8 ◽  
Author(s):  
J. I. de la Rosa ◽  
J. Villa-Hernandez ◽  
E. de la Rosa M. ◽  
E. Gonzalez-Ramirez ◽  
O. Gutierrez ◽  
...  
Keyword(s):  
Image Filtering ◽  
Entropy Estimation ◽  
Robust Image

Entropy estimation for robust image segmentation in presence of non Gaussian noise

2020 ◽  
Author(s):  
José I. de la Rosa ◽  
Osvaldo Gutiérrez ◽  
Jesús Villa-Hernández ◽  
Gamaliel Moreno ◽  
Efrén González ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Gaussian Noise ◽  
Entropy Estimation ◽  
Robust Image ◽  
Non Gaussian

Algorithms of robust image filtering with the preservation of fine details in the presence of noise

2001 ◽  
Vol 46 (9) ◽  
pp. 642-646
Author(s):  
V. F. Kravchenko ◽  
V. I. Ponomarev ◽  
V. I. Pustovoit
Keyword(s):  
Image Filtering ◽  
Robust Image

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.

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