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Author: Sheshadri Thiruvenkadam

Affiliation: MIAL and GE Global Research, India

ISBN: 978-989-8565-47-1

Keyword(s): Multi-modal, Non-rigid Registration, Non-local Gradients.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Image Registration ; Medical Image Applications ; Motion, Tracking and Stereo Vision ; Optical Flow and Motion Analyses

Abstract: In this work, the challenging problem of dense non-rigid registration [NRR] for multi-modal data is addressed. We look at a class of differentiable metrics based on weighted L2 distance of non-local image gradients. For intensity dependent choice of weights, the metric is seen to give enhanced multi-modal capability than using just gradients. In a variational dense deformation setting, the metric is coupled with non-local regularization to make the framework feature based. The above combination maintains the visual quality of the registered image, and gives a good correspondence for features of similar geometry under the challenges of noise, large motion, and presence of small structures. We also address computational speed ups of the energy minimization using an approximation scheme. The proposed approach is demonstrated on synthetic and medical data, and results are quantitatively compared with MI based, diffeomorphic NRR.

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Paper citation in several formats:
Thiruvenkadam, S. (2013). Dense Multi-modal Registration with Structural Integrity using Non-local Gradients.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 258-263. DOI: 10.5220/0004211702580263

@conference{visapp13,
author={Sheshadri Thiruvenkadam.},
title={Dense Multi-modal Registration with Structural Integrity using Non-local Gradients},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={258-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004211702580263},
isbn={978-989-8565-47-1},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Dense Multi-modal Registration with Structural Integrity using Non-local Gradients
SN - 978-989-8565-47-1
AU - Thiruvenkadam, S.
PY - 2013
SP - 258
EP - 263
DO - 10.5220/0004211702580263

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