Adaptive lighting optimization method based on sinusoidal grating projection

Abstract

The invention relates to an adaptive lighting optimization method based on sinusoidal grating projection. The method comprises the steps that a projector is used to project seven computer-generated sinusoidal grating stripes with different frequency numbers on the surface of a three-dimensional target object; reference strips are modulated by the three-dimensional object depth and distort, and corresponding seven distortion strips are acquired respectively; the fundamental frequency components of 7 pairs of distortion strips and reference strips are extracted in the frequency domain through Fourier transform, so as to acquire the decompression phase continuously distributed in the space; pixel matching between the projector and a camera is established; seven distortion images collected by the camera are used as images before processing, and the processed images are projected onto the surface of the object through inverse proportion supplementary lighting of the projector; and for each kind of different grating projection, the best iterative image result is selected, and the optimal adaptive lighting mode is selected by comparing the number of dark / saturation pixels of the seven adaptive lighting image results.

Description

technical field [0001] The invention relates to an adaptive lighting optimization method, which belongs to the field of adaptive optics. Background technique [0002] For an 8-bit digital camera, when the measured scene has different parts with high reflectivity and low reflectivity, the captured image only contains 8-bit limited pixel information. If the exposure is increased to make the part with low reflectivity clear, and The part with high reflectivity becomes saturated; conversely, if the exposure is reduced, the part with high reflectivity becomes clear, while the part with low reflectivity becomes dark, so the part with high reflectivity or low reflectivity will appear extremely bright under certain lighting conditions. Bright or extremely dark areas cause the loss of pixel information, which has an extremely adverse impact on subsequent decision-making and execution. [0003] Traditional methods mostly use manual adjustment of lighting equipment brightness to avoid...

AI Technical Summary

Problems solved by technology

[0002] For an 8-bit digital camera, when the measured scene has different parts with high reflectivity and low reflectivity, the captured image only contains 8-bit limited pixel information. If the exposure is increased to make the part with low reflectivity clear, and The part with high reflectivity becomes saturated; conversely, if the exposure is reduced, the part with high reflectivity becomes clear, while the part with low reflectivity becomes dark, so the part with high reflectivity or low reflectivity will appear extremely bright under certain lighting conditions. Bright or extremely dark areas cause loss of pixel information, which has an extremely adverse impact on subsequent decision-making and execution

[0003] Traditional methods mostly use manual adjustment of lighting equipment brightness to avoid overexposure. However, this manual adjustment method is not suitable for industrial automation and is not universal

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