Article ID: | iaor1999336 |
Country: | United Kingdom |
Volume: | 30 |
Issue: | 1 |
Start Page Number: | 64 |
End Page Number: | 84 |
Publication Date: | Mar 1998 |
Journal: | Advances in Applied Probability |
Authors: | Rue Hvard, Syversveen Anne Randi |
Keywords: | pattern recognition, image processing |
A common problem in Bayesian object recognition using marked point process models is to produce a point estimate of the true underlying object configuration: the number of objects and the size, location and shape of each object. We use decision theory and the concept of loss functions to design a more reasonable estimator for this purpose, rather than using the common zero–one loss corresponding to the maximum a posteriori estimator. We propose to use the squared Δ-metric of Baddeley as our loss function and demonstrate that the corresponding optimal Bayesian estimator can be well approximated by combining Markov chain Monte Carlo methods with simulated annealing into a two-step algorithm. The proposed loss function is tested using a marked point process model developed for locating cells in confocal microscopy images.