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Segmenting multiple objects with overlapping appearance and uncertainty

Segmenting multiple objects with overlapping appearance and uncertainty

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Info

Original languageEnglish
Title of host publicationBritish Machine Vision Conference
PublisherBritish Machine Vision Association and Society for Pattern Recognition
Pages839-848
Number of pages10
DOIs
StatePublished - 2006

Abstract

A probabilistic method is proposed for segmentation of multiple objects that overlap or are in close proximity to one another. A likelihood function is formulated that explicitly models overlapping object appearance. Priors on global appearance and geometry (including shape) are learned from example images. Markov chain Monte Carlo methods are used to obtain samples from a posterior distribution over model parameters from which expectations can be estimated. The method is described in detail for the problem of segmenting femur and tibia in x-ray images. The result is a probabilistic segmentation that quantifies uncertainty so that measurements such as joint space can be made with associated uncertainty.

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