Bayesian object recognition with Baddeley's delta loss

Bayesian object recognition with Baddeley's delta loss

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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: ,
Keywords: pattern recognition, image processing
Abstract:

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.

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