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2 February 2009Multi-object segmentation using coupled nonparametric shape and relative pose priors
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our
method is motivated by the observation that neighboring or coupling objects in images generate configurations
and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs
coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate
kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape
distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on
such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm
based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted
objects in a number of applications. In particular for medical image analysis, we use our method to
extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging
segmentation problem. We also apply our technique to the problem of handwritten character segmentation.
Finally, we use our method to segment cars in urban scenes.