This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach
April 1981 (vol. 3 no. 4)
pp. 412-424
Olivier D. Faugeras, MEMBER, IEEE, Image Processing Institute, University of Southern California, Los Angeles, CA 90007; INRIA, Rocquencourt, France; University of Paris XI, Paris, France.
Marc Berthod, INRIA, Rocquencourt, France.
We approach the problem of labeling a set of objects from a quantitative standpoint. We define a world model in terms of transition probabilities and propose a definition of a class of global criteria that combine both ambiguity and consistency. A projected gradient algorithm is developed to minimize the criterion. We show that the minimization procedure can be implemented in a highly parallel manner. Results are shown on several examples and comparisons are made with relaxation labeling techniques.
Citation:
Olivier D. Faugeras, Marc Berthod, "Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, no. 4, pp. 412-424, April 1981, doi:10.1109/TPAMI.1981.4767127
Usage of this product signifies your acceptance of the Terms of Use.