&lt;title&gt;Optimal estimation of finish parameters&lt;/title&gt;

Church, E. L.; Takacs, Peter Z.

doi:10.1117/12.50498

Cited by 67 publications

(38 citation statements)

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“…Relations exist for such a conversion. 10 However, the equations are sensitive to the noise in the measurements and thus cannot be used for our data. To solve this problem, we developed a simpler conversion based on the relation between the rms roughness and the PSD function for the 1-D and the 2-D isotropic cases:…”

Section: Calculation Of the Power Spectral Density Function And The Rmentioning

confidence: 99%

Surface characterization techniques for determining the root-mean-square roughness and power spectral densities of optical components

Duparré¹,

Ferré‐Borrull²,

Gliech³

et al. 2002

Appl. Opt.

329

195

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Surface topography and light scattering were measured on 15 samples ranging from those having smooth surfaces to others with ground surfaces. The measurement techniques included an atomic force microscope, mechanical and optical profilers, confocal laser scanning microscope, angle-resolved scattering, and total scattering. The samples included polished and ground fused silica, silicon carbide, sapphire, electroplated gold, and diamond-turned brass. The measurement instruments and techniques had different surface spatial wavelength band limits, so the measured roughnesses were not directly comparable. Two-dimensional power spectral density ͑PSD͒ functions were calculated from the digitized measurement data, and we obtained rms roughnesses by integrating areas under the PSD curves between fixed upper and lower band limits. In this way, roughnesses measured with different instruments and techniques could be directly compared. Although smaller differences between measurement techniques remained in the calculated roughnesses, these could be explained mostly by surface topographical features such as isolated particles that affected the instruments in different ways.

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Section: Calculation Of the Power Spectral Density Function And The Rmentioning

confidence: 99%

Surface characterization techniques for determining the root-mean-square roughness and power spectral densities of optical components

Duparré¹,

Ferré‐Borrull²,

Gliech³

et al. 2002

Appl. Opt.

329

195

View full text Add to dashboard Cite

show abstract

“…More useful statistics can be extracted by computing the periodogram estimator of the surface power spectral density (PSD) function and summing over any desired range of spatial frequencies. [3,4] This method allows one to separate high frequency roughness from low and midspatial frequency roughness and enables one to have a much better understanding of the nature of the surface. Essential to the calculation of the PSD is detrending to remove the global surface figure terms and the use of a window function in the spatial domain to minimize spectral leakage of edge discontinuities that produce artifacts in the spectrum.…”

Section: The Methodsmentioning

confidence: 99%

“…The early optical profiling instruments utilized linear array sensors that also produced a measurement over a linear trace [2]. Processing techniques were developed for the linear profile data to avoid introducing artifacts that would mask the true statistical nature of the surface [3,4]. Most surface profiling instruments today utilize 2D array cameras that produce areal surface height maps.…”

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confidence: 99%

Minimizing Artifacts in Analysis of Surface Statistics

Takacs

2012

Imaging and Applied Optics Technical Papers

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“…Both models make use of a modified version of the ABC model [CTL90,CT91], which was originally formulated to fit the Power Spectral Density of some measured smooth surfaces. The PSD describes the surface statistics in terms of the spacial frequencies fx and fy, which depend on the wavelength λ of the incident light:…”

Section: Physically Based Modelsmentioning

confidence: 99%

“…(31) The ABC model [CTL90,CT91] is able to model the inverse power law shape PSD of polished data, and it is given by: …”

Section: Physically Based Modelsmentioning

confidence: 99%

BRDF Representation and Acquisition

Guarnera

Ghosh

et al. 2016

Computer Graphics Forum

155

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Photorealistic rendering of real world environments is important in a range of different areas; including Visual Special effects, Interior/Exterior Modelling, Architectural Modelling, Cultural Heritage, Computer Games and Automotive Design. Currently, rendering systems are able to produce photorealistic simulations of the appearance of many real‐world materials. In the real world, viewer perception of objects depends on the lighting and object/material/surface characteristics, the way a surface interacts with the light and on how the light is reflected, scattered, absorbed by the surface and the impact these characteristics have on material appearance. In order to re‐produce this, it is necessary to understand how materials interact with light. Thus the representation and acquisition of material models has become such an active research area. This survey of the state‐of‐the‐art of BRDF Representation and Acquisition presents an overview of BRDF (Bidirectional Reflectance Distribution Function) models used to represent surface/material reflection characteristics, and describes current acquisition methods for the capture and rendering of photorealistic materials.

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<title>Optimal estimation of finish parameters</title>

Cited by 67 publications

References 0 publications

Surface characterization techniques for determining the root-mean-square roughness and power spectral densities of optical components

Surface characterization techniques for determining the root-mean-square roughness and power spectral densities of optical components

Minimizing Artifacts in Analysis of Surface Statistics

BRDF Representation and Acquisition

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