Directional Reflectance and Emissivity of an Opaque Surface

Nicodemus, Fred E.

doi:10.21236/ad0463473

Cited by 22 publications

(28 citation statements)

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“…the material and the shape dependent anisotropy, and the scanning geometry. The reflectivity property describes the reflection of the light at a surface, depending on the radiance and irradiance properties of the surface (Rees, 2001;Nicodemus, 1965). The emitted laser beam expands with a beam divergence characterized and provided by the manufacturer.…”

Section: Incidence Angle Definition and Footprint Elongationmentioning

confidence: 99%

Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points

Soudarissanane¹,

Lindenbergh²,

Menenti³

et al. 2011

ISPRS Journal of Photogrammetry and Remote Sensing

347

283

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Section: Incidence Angle Definition and Footprint Elongationmentioning

confidence: 99%

Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points

Soudarissanane¹,

Lindenbergh²,

Menenti³

et al. 2011

ISPRS Journal of Photogrammetry and Remote Sensing

347

283

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“…The visual appearance of a surface depends on several factors -the illumination conditions, the geometric structure of the surface sample at several spatial scales, and the surface reflectance properties, often characterized by the bidirectional reflectance distribution function (BRDF) [24] and its variants [9,16,26]. A number of techniques have been developed that can estimate the parameters of a BRDF model from a set of photographs, under restrictive assumptions of illumination, geometry and material properties [10,11].…”

Section: Introductionmentioning

confidence: 99%

Exploring features in a Bayesian framework for material recognition

Liu

Sharan

Adelson

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

207

143

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We are interested in identifying the material category, e.g. glass, metal, fabric, plastic or wood, from a single image of a surface. Unlike other visual recognition tasks in computer vision, it is difficult to find good, reliable features that can tell material categories apart. Our strategy is to use a rich set of low and mid-level features that capture various aspects of material appearance. We propose an augmented Latent Dirichlet Allocation (aLDA) model to combine these features under a Bayesian generative framework and learn an optimal combination of features. Experimental results show that our system performs material recognition reasonably well on a challenging material database, outperforming state-of-the-art material/texture recognition systems.

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“…The surface scattering (or specular) component represents the surface-scattered Fresnel radiation, which results in emission minima at the very strong molecular vibration bands Conel, 1969;Hunt and Logan, 1972;Salisbury et al, 1987;Salisbury and Wald, 1992). A strong band minimum in emission (and therefore a strong peak in reflectance from Kirchhoff's law, E = 1 À R, where E is emissivity and R is reflectance (Nicodemus, 1965)) is produced by Fresnel reflection from surface scattering because high opacity and strong absorption coefficients within the wavelength band produce a mirror-like characteristic and essentially little incident radiance is transmitted or absorbed and the majority is reflected (e.g., Hunt and Vincent, 1968;Conel, 1969;Hunt and Logan, 1972;Salisbury et al, 1987;Salisbury and Wald, 1992). These emission minima are referred to as 'reststrahlen bands' and emission at these bands is due to 'first surface reflection' or 'surface scattering' because all returned radiation has been reflected from the surface of the material and none has passed through the material (Gaffey et al, 1993).…”

Section: Surface Scatteringmentioning

confidence: 99%