This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Thresholding Using Relaxation
May 1981 (vol. 3 no. 5)
pp. 598-606
Azriel Rosenfeld, Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
Russell C. Smith, Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
If a picture contains dark objects on a light background (or vice versa), the objects can be extracted by thresholding, i.e., by classifying the pixels into ``light'' and ``dark'' classes. If the picture is noisy, so that the object and background gray level populations overlap, there will be errors in the thresholded output. A relaxation process can be used to reduce these errors; we classify the pixels probabilistically, and then adjust the probabilities for each pixel, based on its neighbors' probabilities, with light reinforcing light and dark dark. When this adjustment process is iterated, the dark probabilities become very high for pixels that belong to dark regions, and vice versa, so that thresholding becomes trivial.
Citation:
Azriel Rosenfeld, Russell C. Smith, "Thresholding Using Relaxation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, no. 5, pp. 598-606, May 1981, doi:10.1109/TPAMI.1981.4767152
Usage of this product signifies your acceptance of the Terms of Use.