CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2001 vol.23 Issue No.11 - November
Issue No.11 - November (2001 vol.23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.969117
<p><b>Abstract</b>—In this paper, we propose a novel method to register two or more optical images to a 3D surface model. The potential applications of such a registration method could be in medicine; for example, in image guided interventions, surveillance and identification, industrial inspection, computer assisted manufacture, computer assisted maintenance, or telemanipulation in remote or hostile environments. Registration is performed by optimizing a similarity measure with respect to the transformation parameters. We propose a novel similarity measure based on “photo-consistency.” For each surface point, the similarity measure computes how consistent the corresponding optical image information in each view is with a lighting model. The relative pose of the optical images must be known. We validate the system using data from an optical-based surface reconstruction system and surfaces derived from magnetic resonance (MR) images of the human face. We test the accuracy and robustness of the system with respect to the number of video images, video image noise, errors in surface location and area, and complexity of the matched surfaces. We demonstrate the algorithm working on 10 further optical-based reconstructions of the human head and skin surfaces derived from MR images of the heads of five volunteers. Matching four optical images to a surface model produced a 3D error of between 1.45 and 1.59 mm, at a success rate of 100 percent, where the initial misregistration was up to 16 mm or degrees from the registration position.</p>
2D-3D registration, similarity measures, photo-consistency, pose estimation, extrinsic parameter calibration.
Daniel Rueckert, Derek L.G. Hill, Matthew J. Clarkson, "Using Photo-Consistency to Register 2D Optical Images of the Human Face to a 3D Surface Model", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.23, no. 11, pp. 1266-1280, November 2001, doi:10.1109/34.969117