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Author: Bálint Antal

Affiliation: University of Debrecen, Hungary

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.

CC BY-NC-ND 4.0

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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 (PECCS 2016) - SPCS; ISBN 978-989-758-195-3; ISSN 2184-2817, SciTePress, 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 (PECCS 2016) - SPCS},
year={2016},
pages={116-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006008001160121},
isbn={978-989-758-195-3},
issn={2184-2817},
}

TY - CONF

JO - Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems (PECCS 2016) - SPCS
TI - Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks
SN - 978-989-758-195-3
IS - 2184-2817
AU - Antal, B.
PY - 2016
SP - 116
EP - 121
DO - 10.5220/0006008001160121
PB - SciTePress

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