The Markov Random Field in Materials Applications: A synoptic view for signal processing and materials readers

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

The Markov Random Field in Materials Applications: A synoptic view for signal processing and materials readers

By: 
Mary Comer; Jeff Simmons

The Markov random field (MRF) is one of the most widely used models in image processing, constituting a prior model for addressing problems such as image segmentation, object detection, and reconstruction. What is not often appreciated is that the MRF owes its origin to the physics of solids, making it an ideal prior model for processing microscopic observations of materials. While both fields know of their respective interpretations of the MRF, each knows very little about the other’s version of it. Hence, both fields have “blind spots,” where some concepts readily appreciated by one field are completely obscured from the other. With this in mind, the objectives of this article are to 1) develop a synoptic view of the MRF, the related Gibbs distribution, and the Hammersley–Clifford theorem that links them, in such a way that signal processing and materials readers will see them from the same perspective; and 2) explain physics-based regularization using the MRF and describe how it can provide insight into the performance of MRF-based segmentation methods. While the MRF has already been used in many machine learning contexts, we will use a simpler, more transparent method to illustrate the fundamental behavior of the MRF, with the understanding that this behavior should be inherent in more complex learning approaches.

The Markov random field (MRF) is one of the most widely used models in image processing, constituting a prior model for addressing problems such as image segmentation, object detection, and reconstruction. What is not often appreciated is that the MRF owes its origin to the physics of solids, making it an ideal prior model for processing microscopic observations of materials. While both fields know of their respective interpretations of the MRF, each knows very little about the other’s version of it. Hence, both fields have “blind spots,” where some concepts readily appreciated by one field are completely obscured from the other.

SPS on Twitter

  • The SPS Biomedical Imaging and Signal Processing Technical Committee Webinar Series continues on Tuesday, 6 Decembe… https://t.co/SYEEzoxIAK
  • SPS is close to reaching 20,000 members for the first time since 1995! Wouldn't that be a great way to kick off our… https://t.co/F2QMYmkW1W
  • The DEGAS Webinar Series continues on Wednesday, 30 November when Selin Aviyente presents "Single View and Multivie… https://t.co/07tgZXwcpK
  • CALL FOR PAPERS: The IEEE Transactions on Multimedia is accepting submissions for a Special Issue on When Multimedi… https://t.co/2sorOUwhLZ
  • SPS needs your support! is approaching. If our program receives 30 unique donations of US$10 or… https://t.co/hdxELXvKcK

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar