Document Type

Contribution to Reference Work

Publication Date

2-17-2017

Journal

International Journal of Computer Vision

Volume

3

Issue

123

First Page

415

Last Page

434

URL with Digital Object Identifier

https://doi.org/10.1007/s11263-017-0994-x

Abstract

Label ordering, the specification of subset–superset relationships for segmentation labels, has been of increasing interest in image segmentation as they allow for complex regions to be represented as a collection of simple parts. Recent advances in continuous max-flow segmentation have widely expanded the possible label orderings from binary background/foreground problems to extendable frameworks in which the label ordering can be specified. This article presents Directed Acyclic Graph Max-Flow image segmentation which is flexible enough to incorporate any label ordering without constraints. This framework uses augmented Lagrangian multipliers and primal–dual optimization to develop a highly parallelized solver implemented using GPGPU. This framework is validated on synthetic, natural, and medical images illustrating its general applicability.

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