NDLI: Photometric Stereo via Expectation Maximization

Content Provider	IEEE Xplore Digital Library
Author	Tai-Pang Wu Chi-Keung Tang
Copyright Year	1979
Abstract	This paper presents a robust and automatic approach to photometric stereo, where the two main components, namely surface normals and visible surfaces, are respectively optimized by expectation maximization (EM). A dense set of input images is conveniently captured using a digital video camera while a handheld spotlight is being moved around the target object and a small mirror sphere. In our approach, the inherently complex optimization problem is simplified into a two-step optimization, where EM is employed in each step: 1) Using the dense input, the weight or importance of each observation is alternately optimized with the normal and albedo at each pixel and 2) using the optimized normals and employing the Markov random fields (MRFs), surface integrabilities and discontinuities are alternately optimized in visible surface reconstruction. Our mathematical derivation gives simple updating rules for the EM algorithms, leading to a stable, practical, and parameter-free implementation that is very robust even in the presence of complex geometry, shadows, highlight, and transparency. We present high-quality results on normal and visible surface reconstruction, where fine geometric details are automatically recovered by our method.
Sponsorship	IEEE Computer Society
Page Count	15
File Size	4087325
Starting Page	546
Ending Page	560
File Format	PDF
ISSN	01628828
Volume Number	32
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Photometry Surface reconstruction Image reconstruction Robustness Reflectivity Geometry Stereo image processing Shape measurement Digital cameras Mirrors albedo and visible surface reconstruction. Photometric stereo expectation maximization normal
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multiview Photometric Stereo

Example-based photometric stereo: shape reconstruction with general, varying BRDFs

Extracting the shape and roughness of specular lobe objects using four light photometric stereo

Shape Estimation Using Polarization and Shading from Two Views

Shape and Spatially-Varying BRDFs from Photometric Stereo

Optimal Reconstruction of Approximate Planar Surfaces Using Photometric Stereo

Subpixel Photometric Stereo

Variational Surface Interpolation from Sparse Point and Normal Data

The Geometry of Reflectance Symmetries

Photometric Stereo via Expectation Maximization

Similar Documents

Multiview Photometric Stereo

Example-based photometric stereo: shape reconstruction with general, varying BRDFs

Extracting the shape and roughness of specular lobe objects using four light photometric stereo

Shape Estimation Using Polarization and Shading from Two Views

Shape and Spatially-Varying BRDFs from Photometric Stereo

Optimal Reconstruction of Approximate Planar Surfaces Using Photometric Stereo

Subpixel Photometric Stereo

Variational Surface Interpolation from Sparse Point and Normal Data

The Geometry of Reflectance Symmetries

Photometric Stereo via Expectation Maximization