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A Trained Spin-Glass Model for Grouping of Image Primitives
July 2005 (vol. 27 no. 7)
pp. 1172-1182
ASCII Text
x 
 
Joes Staal, Stiliyan N. Kalitzin, Max A. Viergever, "A Trained Spin-Glass Model for Grouping of Image Primitives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 7, pp. 1172-1182, July, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.131,
author = {Joes Staal and Stiliyan N. Kalitzin and Max A. Viergever},
title = {A Trained Spin-Glass Model for Grouping of Image Primitives},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {7},
issn = {0162-8828},
year = {2005},
pages = {1172-1182},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.131},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A Trained Spin-Glass Model for Grouping of Image Primitives
IS - 7
SN - 0162-8828
SP1172
EP1182
EPD - 1172-1182
A1 - Joes Staal,
A1 - Stiliyan N. Kalitzin,
A1 - Max A. Viergever,
PY - 2005
KW - Index Terms- Statistical pattern recognition
KW - spin-glass model
KW - statistical learning
KW - Bayesian grouping.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an energy functional consisting of a local and a bilocal part, allowing interaction between the image primitives. Instead of defining the state of lowest energy as the grouping result, the mean state of the system is taken. In this way, instabilities caused by multiple minima in the energy are being avoided. The means of the spins are taken as the a posteriori probabilities for the grouping result. In the paper, it is shown how the energy functional can be learned from example data. The energy functional is defined in such a way that, in case of no interactions between the elements, the means of the spins equal the a priori local probabilities. The grouping process enables the fusion of the a priori local and bilocal probabilities into the a posteriori probabilities. The method is illustrated both on grouping of line elements in synthetic images and on vessel detection in retinal fundus images.

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Index Terms:
Index Terms- Statistical pattern recognition, spin-glass model, statistical learning, Bayesian grouping.
Citation:
Joes Staal, Stiliyan N. Kalitzin, Max A. Viergever, "A Trained Spin-Glass Model for Grouping of Image Primitives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 7, pp. 1172-1182, July 2005, doi:10.1109/TPAMI.2005.131
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