A novel region-merging approach guided by priority for high resolution image segmentation

2017 ◽  
Vol 8 (8) ◽  
pp. 771-780 ◽  
Author(s):  
Tengfei Su
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Region Merging ◽  
Resolution Image ◽  
High Resolution Image

An Improved Normalized Cut Image Segmentation Algorithm with k-Means Cluster

2014 ◽  
Vol 548-549 ◽  
pp. 1179-1184 ◽  
Author(s):  
Wen Ting Yu ◽  
Jing Ling Wang ◽  
Long Ye
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Normalized Cut ◽  
Resolution Image ◽  
High Resolution Image ◽  
Novel Approach ◽  
Normalized Cuts ◽  
Segmentation Methods ◽  
Improved Performance ◽  
Popular Image

Image segmentation with low computational burden has been highly regarded as important goal for researchers. One of the popular image segmentation methods is normalized cut algorithm. But it is unfavorable for high resolution image segmentation because the amount of segmentation computation is very huge [1]. To solve this problem, we propose a novel approach for high resolution image segmentation based on the Normalized Cuts. The proposed method preprocesses an image by using the normalized cut algorithm to form segmented regions, and then use k-Means clustering on the regions. The experimental results verify that the proposed algorithm behaves an improved performance comparing to the normalized cut algorithm.

scholarly journals Attention Toward Neighbors: A Context Aware Framework For High Resolution Image Segmentation

2021 ◽  
Author(s):  
Fahim Faisal Niloy ◽  
M. Ashraful Amin ◽  
Amin Ahsan Ali ◽  
AKM Mahbubur Rahman
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Context Aware ◽  
Resolution Image ◽  
High Resolution Image

Radiation Damage, Contrast and Statistics in High Resolution Biological Electron Microscopy

1970 ◽  
Vol 28 ◽  
pp. 260-261
Author(s):  
Robert M. Glaeser
Keyword(s):  
High Resolution ◽  
Particle Flux ◽  
Unit Area ◽  
Signal To Noise ◽  
Resolution Image ◽  
High Resolution Image ◽  
Pass Through ◽  
Counting Statistics ◽  
The Relationship ◽  
Picture Element

It is well known that a large flux of electrons must pass through a specimen in order to obtain a high resolution image while a smaller particle flux is satisfactory for a low resolution image. The minimum particle flux that is required depends upon the contrast in the image and the signal-to-noise (S/N) ratio at which the data are considered acceptable. For a given S/N associated with statistical fluxtuations, the relationship between contrast and “counting statistics” is s131_eqn1, where C = contrast; r2 is the area of a picture element corresponding to the resolution, r; N is the number of electrons incident per unit area of the specimen; f is the fraction of electrons that contribute to formation of the image, relative to the total number of electrons incident upon the object.

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