Abstract
The human visual system is proficient in perceiving three-dimensional shape from the shading patterns in a two-dimensional image. How it does this is not well understood and continues to be a question of fundamental and practical interest. In this paper we present a new quantitative approach to shape-from-shading that may provide some answers. We suggest that the brain, through evolution or prior experience, has discovered that objects can be classified into lower-dimensional object-classes as to their shape. Extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low-dimensional space. We carry out this proposal for an important class of three-dimensional (3D) objects: human heads. From an ensemble of several hundred laser-scanned 3D heads, we use principal component analysis to derive a low-dimensional parameterization of head shape space. An algorithm for solving shape-from-shading using this representation is presented. It works well even on real images where it is able to recover the 3D surface for a given person, maintaining facial detail and identity, from a single 2D image of his face. This algorithm has applications in face recognition and animation.
Keywords
Photometric stereoArtificial intelligenceComputer visionComputer scienceFace (sociological concept)Surface (topology)ShadingRepresentation (politics)Active shape modelAnimation3D reconstructionObject (grammar)Shape analysis (program analysis)Pattern recognition (psychology)Image (mathematics)MathematicsComputer graphics (images)GeometrySegmentation
MeSH Terms
BrainFaceHumansImage ProcessingComputer-Assisted
Affiliated Institutions
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Publication Info
- Year
- 1996
- Type
- article
- Volume
- 8
- Issue
- 6
- Pages
- 1321-1340
- Citations
- 269
- Access
- Closed
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Cite This
Joseph J. Atick,
Paul A. Griffin,
Amanda Redlich
(1996).
Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images.
Neural Computation
, 8
(6)
, 1321-1340.
https://doi.org/10.1162/neco.1996.8.6.1321
Identifiers
- DOI
- 10.1162/neco.1996.8.6.1321
- PMID
- 8768397