Translational photometric alignment of single-view image sequences
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| Title: | Translational photometric alignment of single-view image sequences |
|---|---|
| Authors: | Harrison, Adam P. adam.p.harrison@gmail.com, Joseph, Dileepan1 dil.joseph@ualberta.ca |
| Source: | Computer Vision & Image Understanding. Jun2012, Vol. 116 Issue 6, p765-776. 12p. |
| Subjects: | Digital image processing, Photometric stereo, Errors, Mathematical models, Nonlinear theories, Estimation theory, Image reconstruction |
| Abstract: | Abstract: Photometric stereo is a well-established method to estimate surface normals of an object. When coupled with depth-map estimation, it can be used to reconstruct an object’s height field. Typically, photometric stereo requires an image sequence of an object under the same viewpoint but with differing illumination directions. One crucial assumption of this configuration is perfect pixel correspondence across images in the sequence. While this assumption is often satisfied, certain setups are susceptible to translational errors or misalignments across images. Current methods to align image sequences were not designed specifically for single-view photometric stereo. Thus, they either struggle to account for changing illumination across images, require training sets, or are overly complex for these conditions. However, the unique nature of single-view photometric stereo allows one to model misaligned image sequences using the underlying image formation model and a set of translational shifts. This paper introduces such a technique, entitled translational photometric alignment, that employs the Lambertian model of image formation. This reduces the alignment problem to minimizing a nonlinear sum-squared error function in order to best reconcile the observed images with the generative model. Thus, the end goal of translational photometric alignment is not only to align image sequences, but also to produce the best surface-normal estimates given the observed images. Controlled experiments on the Yale Face Database B demonstrate the high accuracy of translational photometric alignment. The utility and benefits of the technique are further illustrated by additional experiments on image sequences suffering from uncontrolled real-world misalignments. [Copyright &y& Elsevier] |
| Copyright of Computer Vision & Image Understanding is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 74283254 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Translational photometric alignment of single-view image sequences – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Harrison%2C+Adam+P%2E%22">Harrison, Adam P.</searchLink><i> adam.p.harrison@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Joseph%2C+Dileepan%22">Joseph, Dileepan</searchLink><relatesTo>1</relatesTo><i> dil.joseph@ualberta.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Vision+%26+Image+Understanding%22">Computer Vision & Image Understanding</searchLink>. Jun2012, Vol. 116 Issue 6, p765-776. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Photometric+stereo%22">Photometric stereo</searchLink><br /><searchLink fieldCode="DE" term="%22Errors%22">Errors</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+theories%22">Nonlinear theories</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: Photometric stereo is a well-established method to estimate surface normals of an object. When coupled with depth-map estimation, it can be used to reconstruct an object’s height field. Typically, photometric stereo requires an image sequence of an object under the same viewpoint but with differing illumination directions. One crucial assumption of this configuration is perfect pixel correspondence across images in the sequence. While this assumption is often satisfied, certain setups are susceptible to translational errors or misalignments across images. Current methods to align image sequences were not designed specifically for single-view photometric stereo. Thus, they either struggle to account for changing illumination across images, require training sets, or are overly complex for these conditions. However, the unique nature of single-view photometric stereo allows one to model misaligned image sequences using the underlying image formation model and a set of translational shifts. This paper introduces such a technique, entitled translational photometric alignment, that employs the Lambertian model of image formation. This reduces the alignment problem to minimizing a nonlinear sum-squared error function in order to best reconcile the observed images with the generative model. Thus, the end goal of translational photometric alignment is not only to align image sequences, but also to produce the best surface-normal estimates given the observed images. Controlled experiments on the Yale Face Database B demonstrate the high accuracy of translational photometric alignment. The utility and benefits of the technique are further illustrated by additional experiments on image sequences suffering from uncontrolled real-world misalignments. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Vision & Image Understanding is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.cviu.2012.01.005 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 765 Subjects: – SubjectFull: Digital image processing Type: general – SubjectFull: Photometric stereo Type: general – SubjectFull: Errors Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Nonlinear theories Type: general – SubjectFull: Estimation theory Type: general – SubjectFull: Image reconstruction Type: general Titles: – TitleFull: Translational photometric alignment of single-view image sequences Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Harrison, Adam P. – PersonEntity: Name: NameFull: Joseph, Dileepan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 10773142 Numbering: – Type: volume Value: 116 – Type: issue Value: 6 Titles: – TitleFull: Computer Vision & Image Understanding Type: main |
| ResultId | 1 |
