Mixing and Simplex Search for Optimal Illumination in Machine Vision

Cho

Proceedings of the 2013 IEEE/SICE International Symposium on System Integration

et al. 2013

Self Cite

This study proposed how to find optimal illumination for industrial vision in short time using random search algorithm and multiple color light sources. The fineness of an image captured by a monochrome camera is varied by illumination and can be evaluated by image sharpness. The relation between the sharpness and the illumination is nonlinear, so direct optimum methods are applicable to mix the multiple sources. Random search is one of the direct optimum methods and were derived from the sharpness as input and N driving voltages for N light sources. The random search was tested in an RGB mixer and reduced the number of iteration for optimal illumination compared with conventional equal step search.

Section: Resultsmentioning

confidence: 99%

Color mixing and random search for optimal illumination in machine vision

Cho

Proceedings of the 2013 IEEE/SICE International Symposium on System Integration

et al. 2013

Self Cite

“…Hence the no-reference model has been presented in previous research [18]. The IQI in industrial machine vision can be evaluated using focus indices [15,16]. Several no-reference models of IQI have been applied to aspects of image processing, such as sharpness/blurriness [18][19][20][21], contrast [22], noise [23], and image spectrum [24,25].…”

Section: Image Correction and Indexmentioning

confidence: 99%

“…However, the gradient-based index is advantageous for the SPR patterns [29]. The sharpness for focusing was used for light control in previous studies [15][16][17]30], the gradient-based index was considered for lighting, and the Tenenbaum gradient was finally chosen [31].…”

Section: Image Correction and Indexmentioning

confidence: 99%

“…The raw images acquired after scanning usually exhibit vertical patterns due to non-uniform luminance; hence, brightness correction was performed [13]. The acquired image is sufficiently large and has small periodic patterns, and thus the Tenenbaum gradient was applied to calculate the index, instead of the sharpness, which was used in previous studies [15,16]. The dimming level for maximizing the image quality was determined by iterating the simplex search method (SSM).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Color Lighting for Scanning Images of Flat Panel Display using Simplex Search

Jin

et al. 2018

J. Imaging

Self Cite

A system for inspecting flat panel displays (FPDs) acquires scanning images using multiline charge-coupled device (CCD) cameras and industrial machine vision. Optical filters are currently installed in front of these inspection systems to obtain high-quality images. However, the combination of optical filters required is determined manually and by using empirical methods; this is referred to as passive color control. In this study, active color control is proposed for inspecting FPDs. This inspection scheme requires the scanning of images, which is achieved using a mixed color light source and a mixing algorithm. The light source utilizes high-power light emitting diodes (LEDs) of multiple colors and a communication port to dim their level. Mixed light illuminates an active-matrix organic light-emitting diode (AMOLED) panel after passing through a beam expander and after being shaped into a line beam. The image quality is then evaluated using the Tenenbaum gradient after intensity calibration of the scanning images. The dimming levels are determined using the simplex search method which maximizes the image quality. The color of the light was varied after every scan of an AMOLED panel, and the variation was iterated until the image quality approached a local maximization. The number of scans performed was less than 225, while the number of dimming level combinations was 2048 4 . The proposed method can reduce manual tasks in setting-up inspection machines, and hence is useful for the inspection machines in FPD processes.

“…The optimum methods have a general form of problem definition using a cost function as follows [17]: min…”

Section: Index For Image Qualitymentioning

confidence: 99%

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

et al. 2017

Self Cite

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.