Bone and Joint Institute

Title

Thin structure estimation with curvature regularization

Document Type

Conference Proceeding

Publication Date

2-17-2015

Journal

Proceedings of the IEEE International Conference on Computer Vision

Volume

2015 International Conference on Computer Vision, ICCV 2015

First Page

397

Last Page

405

URL with Digital Object Identifier

10.1109/ICCV.2015.53

Abstract

© 2015 IEEE. Many applications in vision require estimation of thin structures such as boundary edges, surfaces, roads, blood vessels, neurons, etc. Unlike most previous approaches, we simultaneously detect and delineate thin structures with sub-pixel localization and real-valued orientation estimation. This is an ill-posed problem that requires regularization. We propose an objective function combining detection likelihoods with a prior minimizing curvature of the center-lines or surfaces. Unlike simple block-coordinate descent, we develop a novel algorithm that is able to perform joint optimization of location and detection variables more effectively. Our lower bound optimization algorithm applies to quadratic or absolute curvature. The proposed early vision framework is sufficiently general and it can be used in many higher-level applications. We illustrate the advantage of our approach on a range of 2D and 3D examples.

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