The spectral power distributions (SPD) of outdoor light sources are not constant over time and atmospheric conditionas, which causes the appearance variation of a scene and common natural illumination phenomena, such as twilight, shadow, and haze/fog. Calculating the SPD of outdoor light sources at different time (or zenith angles) and under different atmo spheric conditions is of interest to physically based vision. In this chapter, for robot vision and its applications, we propose a feasible, simple, and effective SPD calculating method based on analyzing the transmittance functions of absorption and scattering along the path of solar radiation through the atmosphere in the visible spectrum. Our model has fewer parameters and is accurate enough to be directly applied in robot vision. It can be applied in robot vision tasks including spectral inverse calculation, lighting conversion, and shadowed image processing. The experimental results of the applications demonstrate that our calculation methods have practical values in robot vision. It establishes a bridge between image and physical environmental information, e.g., time, location, and weather conditions. Spectral reflectance is defined as the “fingerprint” of an object and is illumination invariant. It has many applications in color reproduction, imaging, robot vision, and computer graphics. In previous reflectance reconstruction methods, spectral reflectance is treated equally over the whole wavelength. However, human eyes or sensors in an imaging device usually have different weights over different wavelengths. In this chapter, we propose a method to reconstruct reflectance considering wavelength-sensitive function (WSF) that is constructed from sensor sensitive functions (or color matching functions). Our main idea is to achieve more accurate reconstruction at wavelengths where sensors have high sensitivities. This more accurate reconstruction can achieve better imaging or color reproduction performance. In our method, we generate a matrix through the Hadamard product of the reflectance matrix and the WSF matrix. We then obtain reconstructed reflectance by applying the singular value decomposition on the generated matrix. The experimental results show that our method can reduce 47% mean-square error and 55% Lab error compared with the classical PCA method.
Spectral Power Distributions and Reflectance Calculations for Robot Vision
Research on Intelligent Manufacturing
02.12.2021
25 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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