BAYESIAN OBJECT RECOGNITION WITH BADDELEY DELTA LOSS

A common problem in Bayesian object recognition using marked point pro cess models is to produce a point estimate of the true underlying obje ct configuration: the number of objects and the size, location and sha pe of each object. We use decision theory and the concept of loss func tions to design a more reasonable estimator for this purpose, rather t han using the common zero-one loss corresponding to the maximum a post eriori estimator. We propose to use the squared Delta-metric of Baddel ey (1992) as our loss function and demonstrate that the corresponding optimal Bayesian estimator can be well approximated by combining Marko v chain Monte Carlo methods with simulated annealing into a two-step a lgorithm. The proposed loss function is tested using a marked point pr ocess model developed for locating cells in confocal microscopy images .