Maximum a posteriori approach to 2.5D image reconstruction using Laplacian-Gaussian mixture model

Peng Liu*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper explored the issue of separating illumination from 2D human face images. A novel statistical approach is introduced which is based on seeking maximum possibility of independency between illumination and object shape at the extreme case where the number of observation is less than the number of input images. It allows only two images of an individual under different illumination conditions via the same view point to be applied, which breaks the lower boundary condition of the least input number of images in classical photometric stereo. The proposed mathematical framework is formulated using the Bayesian statistics and the parameters are estimated using the maximum a posteriori (MAP) approach. A novel Laplacian-Gaussian mixture model (LGMM) is developed to model the noisy captured images. This model enhances the parameter estimation accuracy while reduces the overall computational complexity. In addition, the ambiguity of generalized Bas-relief transformation is resolved due to the uniqueness of 'statistical independent' solution rendered by the proposed approach.

Original languageEnglish
Title of host publication5th International Conference on Visual Information Engineering, VIE 2008
PublisherIEEE
Pages594-599
Number of pages6
Edition543 CP
ISBN (Electronic)9780863419140
DOIs
Publication statusPublished - 9 Jan 2009
Event5th International Conference on Visual Information Engineering, VIE 2008 - Xi'an, China
Duration: 29 Jul 20081 Aug 2008

Conference

Conference5th International Conference on Visual Information Engineering, VIE 2008
CountryChina
CityXi'an
Period29/07/081/08/08

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