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Author: Bálint Antal

Affiliation: University of Debrecen, Hungary

ISBN: 978-989-758-195-3

ISSN: 2184-2817

Keyword(s): Endoscope, Laparoscope, Heart, 3D Reconstruction, Depth Map, Deep Neural Networks, Machine Learning.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Biomedical Signal Processing ; Image Processing ; Informatics in Control, Automation and Robotics ; Robotics and Automation

Abstract: In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.

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Paper citation in several formats:
Antal, B. (2016). Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks.In Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: SPCS, (PECCS 2016) ISBN 978-989-758-195-3, ISSN 2184-2817, pages 116-121. DOI: 10.5220/0006008001160121

@conference{spcs16,
author={Bálint Antal.},
title={Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks},
booktitle={Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: SPCS, (PECCS 2016)},
year={2016},
pages={116-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006008001160121},
isbn={978-989-758-195-3},
}

TY - CONF

JO - Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: SPCS, (PECCS 2016)
TI - Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks
SN - 978-989-758-195-3
AU - Antal, B.
PY - 2016
SP - 116
EP - 121
DO - 10.5220/0006008001160121

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