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A mixed light source generates various colors with the potential to adjust the intensities of multiple LEDs, which makes it possible to generate arbitrary colors. Currently, PCs and OSs provide color selection windows that can obtain the red, green, and blue (RGB) or hue, saturation, and lightness (HSL) color coordinates of a user’s selection. Mixed light sources are usually composed of primary-colored LEDs with LEDs in auxiliary colors such as white and yellow used in a few cases. The number of LED inputs and the dimming levels are greater than the number of elements in the color coordinate when using auxiliary-colored LEDs, which causes an under-determined problem. This study proposes a method to determine the dimming levels of LEDs based on a simplex search method (SSM). Commercial LEDs have different optical power values and impure color elements, even if they are RGB. Hence, the characteristics of the LEDs were described using a linear model derived from the tri-stimulus values (an XYZ color coordinate model) and dimming levels. Color mixing models were derived for an arbitrary number of auxiliary-colored LEDs. The under-determined problem was solved using an SSM without an inverse matrix operation. The proposed method can be applied to a machine vision system and an RGBW light mixer for semiconductor inspection. The dimming levels, obtained using the proposed method showed better optical responses than those derived using other methods. [ABSTRACT FROM AUTHOR]

Mixed-color illumination affects the quality of images in industrial vision system and it is important to optimize color and intensity for image acquisition. This study used simplex search to find the optimal illumination in a short amount of time. A typical color mixer synthesized various color of lights by changing the inputs of RGB power LEDs and passing the lights through an optical system. The image quality under mixed-color illumination was calculated according to the sharpness. For the purpose of optimal illumination using simplex search, a probe network was organized with N + 1probing points for N inputs. The shape of the probe network, simplex, was varied through procedures of extension, contraction, and shrinkage. The inputs of the color mixer were changed until the size of the simplex became smaller than a threshold. The simplex search was tested for commercial semiconductor patterns, and was useful for finding the optimal illumination. [ABSTRACT FROM AUTHOR]

Quick search for optimal illumination using mixed color is proposed, and the possibility for use in industrial vision systems is considered. Multidimensional formulation of the optimum search methods is reviewed to use multiple inputs for various color sources and one output of image sharpness. The voltage inputs are used to adjust the intensity of each color source and the optimal illumination is found by the maximum of the sharpness. The optimum search methods reveal that the number of iterations for the optimal condition in mixed color illumination was under 0.1% the number required by equal section search. The searching paths and convergence were considered in both of the input coordinate and the color coordinate. The results show that the optimum search methods make it possible to find the optimal lighting conditions quickly and accurately. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]