Title :
Deformable template recognition of multiple occluded objects
Author :
Mardia, Kanti V. ; Qian, Wei ; Shah, Druti ; De Souza, Kevin M A
Author_Institution :
Dept. of Stat., Leeds Univ., UK
fDate :
9/1/1997 12:00:00 AM
Abstract :
Based on deformable templates, the paper formulates an integrated and flexible Bayesian recognition system of multiple occluded objects. Various local dependence properties of the model are obtained to reduce the computational cost with the increase in the number of objects. Numerical results for a synthetic image and for a real image of mushrooms are discussed
Keywords :
Bayes methods; computational complexity; image recognition; object recognition; computational cost; deformable template recognition; integrated flexible Bayesian recognition system; local dependence properties; multiple occluded objects; mushrooms; numerical results; Bayesian methods; Computational efficiency; Computational geometry; Deformable models; Iterative methods; Knowledge based systems; Markov random fields; Object recognition; Robots; Stochastic processes;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
