Publications

@inproceedings{placitelli3DPointCloud2011,

title = {3D Point Cloud Sensors for Low-cost Medical In-situ Visualization},

author = { Alessio Pierluigi Placitelli and Luigi Gallo},

doi = {10.1109/BIBMW.2011.6112435},

isbn = {978-1-4577-1613-3},

year  = {2011},

date = {2011-11-01},

booktitle = {2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)},

pages = {596--597},

publisher = {IEEE},

address = {Atlanta, GA, USA},

abstract = {Medical in-situ visualization deals with the display of the patient's specific imaging data at the location where they actually are. To be effective, it requires high end I/O devices, and computationally expensive and time-consuming calibration and registration steps. In this paper, we explore the use of widely available and low-priced 3D point cloud sensors in medical augmented reality (AR) applications. Specifically, we examine the typical pipeline of AR applications and explore the potential simplifications derived from the use of RGB-D cameras during the calibration and registration steps. Moreover, we describe a low-cost system built from open-source components that takes advantage of 3D point cloud data to apply medical imagery to live-video streams of patients.},

keywords = {3D registration, Augmented Reality, Healthcare, Kinect},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{placitelli_3d_2011,

title = {3D Point Cloud Sensors for Low-cost Medical In-situ Visualization},

author = {Alessio Pierluigi Placitelli and Luigi Gallo},

doi = {10.1109/BIBMW.2011.6112435},

isbn = {978-1-4577-1613-3},

year  = {2011},

date = {2011-11-01},

booktitle = {2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)},

pages = {596–597},

publisher = {IEEE},

address = {Atlanta, GA, USA},

abstract = {Medical in-situ visualization deals with the display of the patient's specific imaging data at the location where they actually are. To be effective, it requires high end I/O devices, and computationally expensive and time-consuming calibration and registration steps. In this paper, we explore the use of widely available and low-priced 3D point cloud sensors in medical augmented reality (AR) applications. Specifically, we examine the typical pipeline of AR applications and explore the potential simplifications derived from the use of RGB-D cameras during the calibration and registration steps. Moreover, we describe a low-cost system built from open-source components that takes advantage of 3D point cloud data to apply medical imagery to live-video streams of patients.},

keywords = {3D registration, Augmented Reality, Healthcare, Kinect},

pubstate = {published},

tppubtype = {inproceedings}

}