Surface Reflectance: An Optical Method for Multiscale Curvature Characterization of Wear on Ceramic–Metal Composites

Abstract

Surface gradient characterization by light reflectance (SGCLR) is used for the first time for multiscale curvature calculations and discrimination of worn surfaces on six damaged ceramic–metal composites. Measurements are made using reflectance transformation imaging (RTI). Slope and curvature maps, generated from RTI, are analyzed instead of heights. From multiscale decompositions, bootstrapping, and analysis of variance (ANOVA), a strong correlation (R² = 0.90) is found between the density of furrows of Mehlum curvatures, with a band pass filter at 5.4 µm, present in ceramic grains and their mechanical properties. A strong correlation is found between the mean curvatures of the metal and the ceramics, with a high pass filter at 1286 µm.

Next Best Light Position: A self configuring approach for the Reflectance Transformation Imaging acquisition process

“…Multi-modal acquisition like multi-spectral [10], high dynamic range RTI [11], UV are the growing trends in RTI where enormous data is generated during acquisition and hence continuous acquisition poses numerical practical problems. Besides, cultural heritage conservation, RTI is oftentimes applied in industries too as a tool for engineering failure analysis [12], [13]. Automation of RTI acquisition using robot arm is another domain that draws much interest from the RTI users community as that will be an efficient way of performing the acquisitions in terms of both quality and time.…”

Improved visual saliency estimation on manufactured surfaces using high-dynamic reflectance transformation imaging

“…The RTI provides access to multidimensional information (dynamic image relighting, luminance, slopes, curvatures, 3D mappings, visual saliency cartographies, etc.). These data are very valuable for evaluating and describing the visual quality of a surface, [4][5][6][7][8][9] especially in the case of manufactured products. [10][11][12][13][14][15] The principle of RTI acquisition is to vary the direction of the light source (azimuth and elevation angles) while keeping the sensor fixed orthogonally to the inspected surface for each light position.…”

A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition

“…This clearly means that the compression ratio well discriminates the different surface topographies without computing any roughness parameters. It can be noticed that lossy compression algorithms can have an interest with topographical data recorded by photo goniometers due to the amount of data [26]. For the band pass, high pass, and low pass filters, one obtains respectively maximal F values of 558, 414, and 548 corresponding to the scales of respectively 78.2, 78.2, and 29.6 µ m ( Table 2).…”

“…This clearly means that the compression ratio well discriminates the different surface topographies without computing any roughness parameters. It can be noticed that lossy compression algorithms can have an interest with topographical data recorded by photo goniometers due to the amount of data [26].…”