Multistage Hierarcircal Image Segmentation Algorithm Using Markov Random Field And Maximum Entropy Principle For Spatially Continuous Images

2005 ◽  
Author(s):  
S. Lee ◽  
M.M. Crawford
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Maximum Entropy ◽  
Markov Random Field ◽  
Maximum Entropy Principle ◽  
Segmentation Algorithm ◽  
Markov Random ◽  
Entropy Principle ◽  
Image Segmentation Algorithm ◽  
Continuous Images

Tool Wear Image Segmentation Based on Markov Random Field Model

2010 ◽  
Vol 102-104 ◽  
pp. 600-604
Author(s):  
Si Chang Xiong ◽  
Ling Ping Dong ◽  
Dong Hui Wen
Keyword(s):  
Image Segmentation ◽  
Tool Wear ◽  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Segmentation Algorithm ◽  
Wear Area ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Markov Random

In this article, we propose a tool wear image segmentation algorithm based on Markov Random Field model. In this algorithm, the wear area was divided into wear area, background area and body area, in terms of the MAP (maximum a posteriori) criterion, and we got the pre-segmentation image. Afterwards, the aiming area (region B) was segmented out from the wear area by using peak-band algorithm and the edges are integrated in mathematical morphology theorem. As a result, we obtained an integrated tool wear region. Experiments indicate that the segmentation algorithm can be used to evaluate the degree of tool wear and applied to synchronously monitor tool wear condition effectively.

Sign in / Sign up

Export Citation Format

Share Document