Detection of object motion regions in aerial image pairs with a multilayer Markovian model
IEEE Transactions on Image Processing
A label field fusion Bayesian model and its penalized maximum rand estimator for image segmentation
IEEE Transactions on Image Processing
A detection-based framework for the analysis of recycling in TIRF microscopy
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
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In this paper, we put forward a novel fusion framework that mixes together label fields instead of observation data as is usually the case. Our framework takes as input two label fields: a quickly estimated and to-be-refined segmentation map and a spatial region map that exhibits the shape of the main objects of the scene. These two label fields are fused together with a global energy function that is minimized with a deterministic iterative conditional mode algorithm. As explained in the paper, the energy function may implement a pure fusion strategy or a fusion-reaction function. In the latter case, a data-related term is used to make the optimization problem well posed. We believe that the conceptual simplicity, the small number of parameters, the use of a simple and fast deterministic optimizer that admits a natural implementation on a parallel architecture are among the main advantages of our approach. Our fusion framework is adapted to various computer vision applications among which are motion segmentation, motion estimation and occlusion detection.