Figure - available from: Journal of the Optical Society of America A
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Main results for five example scenes. Each column corresponds to the scene shown in the first row. The vertical axis shows the proportion of correct responses (accuracy), and the color of each bar represents the color of the illumination under which the corresponding image was viewed. Error bars show standard errors, and the chance level is shown by the dashed line at 0.25 in each plot. Significant differences from chance are indicated as $^*\!p \lt {0.05}$ , $^{**}\!p \lt {0.01}$ , and $^{***}\!p \lt {0.001}$ . The number of participants in the respective online measurement is given in the third row.
Source publication
Specifying surface reflectances in a simple and perceptually informative way would be beneficial for many areas of research and application. We assessed whether a ${3} \times {3}$ matrix may be used to approximate how a surface reflectance modulates the sensory color signal across illuminants. We tested whether observers could discriminate between...
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Citations
... Hyperspectral imaging, enabled by the development of hyperspectral cameras [1], stands as a pivotal technology with diverse applications reaching beyond the traditional domain of computer graphics. The advances in research associated with this domain have also led to strong development in the field of hyperspectral rendering, that is used today to study aircraft signatures [2], perceptual research [3], target detection training [4], define instrument requirements [5] or as a tool for designers [6]. This variety in areas of use has been concurrent with the development of many different hyperspectral physically-based rendering engines [6][7][8][9], some of which can adapt to all types of scene, while others specialise in simulating target observation or predicting vehicle signatures [10][11][12][13][14], extending the spectral range beyond the visible by including part of the infrared spectrum. ...
The hyperspectral component of bidirectional reflectance measurements, namely from several hundred wavelengths upwards, is attracting growing interest for numerous applications in both optics and computer graphics. In this paper, we present a motorized hyperspectral bidirectional reflectance measurement bench that performs in-plane and out-of-plane measurements for isotropic materials using a supercontinuum laser covering the visible and near infrared range, with a sub-nanometer spectral accuracy. We describe the complete data processing chain, including a method for assessing the alignment error of the measurement bench. From these measurements, we verify the principles of non-negativity, energy conservation and Helmholtz reciprocity. We introduce criteria also to evaluate the validity of the Lambertian hypothesis for the bidirectional reflectance and its deviation from reciprocity, obtained from the measurements directly. We show the need for spectral bidirectional reflectance measurements for certain materials, rejecting the separable function approximation.
