NDLI: Maximum Likelihood Estimation of Depth Maps Using Photometric Stereo

Content Provider	IEEE Xplore Digital Library
Author	Harrison, A.P. Joseph, D.
Copyright Year	1979
Abstract	Photometric stereo and depth-map estimation provide a way to construct a depth map from images of an object under one viewpoint but with varying illumination directions. While estimating surface normals using the Lambertian model of reflectance is well established, depth-map estimation is an ongoing field of research and dealing with image noise is an active topic. Using the zero-mean Gaussian model of image noise, this paper introduces a method for maximum likelihood depth-map estimation that accounts for the propagation of noise through all steps of the estimation process. Solving for maximum likelihood depth-map estimates involves an independent sequence of nonlinear regression estimates, one for each pixel, followed by a single large and sparse linear regression estimate. The linear system employs anisotropic weights, which arise naturally and differ in value to related work. The new depth-map estimation method remains efficient and fast, making it practical for realistic image sizes. Experiments using synthetic images demonstrate the method's ability to robustly estimate depth maps under the noise model. Practical benefits of the method on challenging imaging scenarios are illustrated by experiments using the Extended Yale Face Database B and an extensive data set of 500 reflected light microscopy image sequences.
Sponsorship	IEEE Computer Society
Page Count	13
File Size	1032392
Starting Page	1368
Ending Page	1380
File Format	PDF
ISSN	01628828
Volume Number	34
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Mathematical model Equations Gaussian noise Covariance matrix finite difference methods. Photometric stereo depth map maximum likelihood estimation nonlinear regression
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

Bayesian segmentation via asymptotic partition functions

The Empirical Likelihood method applied to covariance matrix estimation

Covariance matrix estimation and classification with limited training data

On the fitting of surfaces to data with covariances

Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images

Speaker adaptation in a large-vocabulary Gaussian HMM recognizer

Smart nonlinear diffusion: a probabilistic approach

Consistent Depth Maps Recovery from a Video Sequence

From FNS to HEIV: a link between two vision parameter estimation methods

Maximum Likelihood Estimation of Depth Maps Using Photometric Stereo

Similar Documents

Bayesian segmentation via asymptotic partition functions

The Empirical Likelihood method applied to covariance matrix estimation

Covariance matrix estimation and classification with limited training data

On the fitting of surfaces to data with covariances

Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images

Speaker adaptation in a large-vocabulary Gaussian HMM recognizer

Smart nonlinear diffusion: a probabilistic approach

Consistent Depth Maps Recovery from a Video Sequence

From FNS to HEIV: a link between two vision parameter estimation methods

Maximum Likelihood Estimation of Depth Maps Using Photometric Stereo