Segmentation Framework Based on Label Field Fusion

• Published in: IEEE transactions on image processing 2007-10, Vol.16 (10), p.2535-2550
• Main Authors: Jodoin, P.-M., Mignotte, M., Rosenberger, C.
• Format: Article
• Language: English
• Subjects: Algorithms; Application software; Applied sciences; Artificial Intelligence; Color; Color segmentation; Colorimetry - methods; Computer Graphics; Computer Science; Computer science; control theory; systems; Computer vision; Computer Vision and Pattern Recognition; Design optimization; Exact sciences and technology; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Information, signal and communications theory; Iterative algorithms; label fusion; Labels; Layout; Mathematical analysis; Mathematical models; Miscellaneous; Motion detection; Motion estimation; Motion segmentation; Numerical Analysis, Computer-Assisted; occlusion; Operations research; Optimization; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Reproducibility of Results; Segmentation; Sensitivity and Specificity; Shape; Signal processing; Signal Processing, Computer-Assisted; Strategy; Studies; Subtraction Technique; Telecommunications and information theory
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2007.903841

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.