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2 February 2009 Multi-object segmentation using coupled nonparametric shape and relative pose priors
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Proceedings Volume 7246, Computational Imaging VII; 72460H (2009) https://doi.org/10.1117/12.815215
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States
Abstract
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mustafa Gökhan Uzunbas, Octavian Soldea, Müjdat Çetin, Gözde Ünal, Aytül Erçil, Devrim Unay, Ahmet Ekin, and Zeynep Firat "Multi-object segmentation using coupled nonparametric shape and relative pose priors", Proc. SPIE 7246, Computational Imaging VII, 72460H (2 February 2009); https://doi.org/10.1117/12.815215
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