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2017 | OriginalPaper | Chapter

Generalized Fusion Moves for Continuous Label Optimization

Author : Christopher Zach

Published in: Computer Vision – ACCV 2016

Publisher: Springer International Publishing

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Abstract

Energy-minimization methods are ubiquitous in computer vision and related fields. Low-level computer vision problems are typically defined on regular pixel lattices and seek to assign discrete or continuous values (or both) to each pixel such that a combined data term and a spatial smoothness prior are minimized. In this work we propose to minimize difficult energies using repeated generalized fusion moves. In contrast to standard fusion moves, the fusion step optimizes over binary and continuous sets of variables representing label ranges. Further, each fusion step can optimize over additional continuous unknowns. We demonstrate the general method on a variational-inspired stereo approach, and optionally optimize over radiometric changes between the images as well.

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previous chapter Unifying Algebraic Solvers for Scaled Euclidean Registration from Point, Line and Plane Constraints
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Metadata
Title
Generalized Fusion Moves for Continuous Label Optimization
Author
Christopher Zach
Copyright Year
2017
Book
Computer Vision – ACCV 2016

Print ISBN: 978-3-319-54192-1

Electronic ISBN: 978-3-319-54193-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-54193-8_5

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