Field Guide to Illumination

Arecchi, Angelo V.; Messadi, Tahar; Koshel, R. John

doi:10.1117/3.764682

Cited by 65 publications

(42 citation statements)

References 14 publications

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“…Therefore, both components are widely used in projection optical systems [44] . Here, both components are in general well suited for homogenization; however, they will differ in their performance and will also infl uence the optical system design, as illustrated in Figure 10 .…”

Section: Light Homogenizationmentioning

confidence: 99%

Phase space approach to the use of integrator rods and optical arrays in illumination systems

Rausch

Herkommer

2012

Advanced Optical Technologies

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Integrator rods and optical arrays are the most frequently used components in illumination design for homogenizing radiation fi elds. However, these two standard components are very different in their performance and characteristics. This tutorial is aimed to illustrate the operation principle, basic design rules and the performance of those components. It should guide the optical designer towards the optimum choice for the individual illumination application. To illustrate the functionality of integrator rods and optical arrays simultaneously in angle and position, the concept of phase space is introduced. Here the effect of the homogenizing components can nicely be illustrated in the form of phase space transformations. This offers new insight and a different perspective onto the employment and characteristics of those elements.

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Section: Light Homogenizationmentioning

confidence: 99%

Phase space approach to the use of integrator rods and optical arrays in illumination systems

Rausch

Herkommer

2012

Advanced Optical Technologies

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“…This relation can be written as in Eq. (1), where xy are the planar coordinate of the camera pixel, k is the wavelength, q is the intensity measured in a given pixel, C is a constant, R is the spectral response of the camera, S is the spectral reflectance of the scene, E is the spectral distribution of the irradiance, and k is the camera (Jain, Kasturi, and Schunck 1995;Arecchi, Messadi, and Koshel 2007).…”

Section: Color Mixermentioning

confidence: 99%

An Optical Mixer and RGB Control for Fine Images using Grey Scale Distribution

Kim

Cho

2012

International Journal of Optomechatronics

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We considered the issue of color mixing from the viewpoint of gray scale images and investigated the effect of the mixed light. The goal of this study was to improve the quality of the grey scale images by using mixed RGB illumination. The mixing mechanism and image quality were formulized and applied to a machine vision system with an RGB mixer. The RGB mixer was made of RGB LEDs, a power AMP, a mixing chamber, and a bundle of optical fibers. The intensity of the RGB light was adjusted by varying the 12 bit DAC voltage. The optical characteristics of the RGB LEDs were measured by a spectrometer. The color variation of the mixed light was monitored by a chromatic meter. Grey scale images were captured and analyzed under various lighting conditions provided by the mixer. The optimal illumination was determined by the distribution of the grey scale in the captured image. The chromatic diagram showed that various colors can be generated, and the image histogram showed that the mixed light can increase the quality of the grey scale image.

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“…The active control factors in machine vision are intensity and peak wavelength, though other factors are usually invariable. Although image quality is sensitive to intensity and peak wavelength, the optimal combination of these factors may be varied by the material of the target object [3]. Because the active control factors are currently manually changed, it is considerably labor intensive to adjust…”

Section: Introductionmentioning

confidence: 99%

Robust Parameter Design of Derivative Optimization Methods for Image Acquisition Using a Color Mixer

et al. 2017

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A tuning method was proposed for automatic lighting (auto-lighting) algorithms derived from the steepest descent and conjugate gradient methods. The auto-lighting algorithms maximize the image quality of industrial machine vision by adjusting multiple-color light emitting diodes (LEDs)-usually called color mixers. Searching for the driving condition for achieving maximum sharpness influences image quality. In most inspection systems, a single-color light source is used, and an equal step search (ESS) is employed to determine the maximum image quality. However, in the case of multiple color LEDs, the number of iterations becomes large, which is time-consuming. Hence, the steepest descent (STD) and conjugate gradient methods (CJG) were applied to reduce the searching time for achieving maximum image quality. The relationship between lighting and image quality is multi-dimensional, non-linear, and difficult to describe using mathematical equations. Hence, the Taguchi method is actually the only method that can determine the parameters of auto-lighting algorithms. The algorithm parameters were determined using orthogonal arrays, and the candidate parameters were selected by increasing the sharpness and decreasing the iterations of the algorithm, which were dependent on the searching time. The contribution of parameters was investigated using ANOVA. After conducting retests using the selected parameters, the image quality was almost the same as that in the best-case parameters with a smaller number of iterations.

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Field Guide to Illumination

Cited by 65 publications

References 14 publications

Phase space approach to the use of integrator rods and optical arrays in illumination systems

Phase space approach to the use of integrator rods and optical arrays in illumination systems

An Optical Mixer and RGB Control for Fine Images using Grey Scale Distribution

Robust Parameter Design of Derivative Optimization Methods for Image Acquisition Using a Color Mixer

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