Paper
1 June 1991 Photometric models in multispectral machine vision
Author Affiliations +
Abstract
The performance of several tasks in multispectral computer vision involves assumptions about the reflection of light from surfaces. These tasks include color constancy (visual representation of spectral reflectances independent of the illuminant spectrum), object-based image segmentation, and deduction of the shape of a surface from its shading. Most color-constancy theories implicitly assume Lambertian, coplanar reflecting surfaces, a distant viewer, and a distant light source that may have many components that are spatially and spectrally distinct. Object-based-segmentation theories allow curved surfaces, each of whose scattering kernels is the sum of a few separable terms (each of which is the product of a wavelength-dependent part and a geometry-dependent part). There is no restriction on the distances of light sources or observer. However, for these theories the illuminant angular/spectral distribution must consist of only one or two separable terms. Finally, A. Petrov's shape-from-shading theory allows the light source to have nearly arbitrary spectral and spatial composition, but requires the surface scattering kernels to have Lambertian dependence on the surface normal. The present paper compares these photometric models.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael H. Brill "Photometric models in multispectral machine vision", Proc. SPIE 1453, Human Vision, Visual Processing, and Digital Display II, (1 June 1991); https://doi.org/10.1117/12.44370
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Light sources

Scattering

Visual process modeling

Image segmentation

Human vision and color perception

Visualization

Radon

RELATED CONTENT


Back to Top