A review of light intensity control and quick optimum search in machine vision

Kim, Hyung-Tae; Kim, SeungTaek; Cho, Young-June

doi:10.1109/isot.2012.6403275

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…where W j is the neural network weights matrix for function f , and f(•) is a hyperbolic tangent sigmoid transfer function. Then, (7) can be defined as (8): The neural network's output with n nodes in a hidden layer is defined as (9):…”

Section: Adaptive Methods For Correcting Measured Color Intensity Vmentioning

confidence: 99%

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

et al. 2020

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Machine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much attention is given to its illumination conditions. The paper examines the adverse factors affecting the quality of image recognition and presents an adaptive method for reducing the influence of changing illumination conditions in the color inspection process of transparent PET preforms. The method is based on predicting measured color intensity correction parameters according to illumination conditions. To test this adaptive method, a hardware and software system for image capture and processing was developed. This system is capable of inspecting large quantities of preforms in real time using a neural network with a modified gradient descent and momentum algorithm. The experiment showed that correction of the measured color intensity value reduced the standard deviation caused by variable and uneven illumination by 61.51%, demonstrating that machine vision color intensity evaluation is a robust and adaptive solution under illuminated conditions for detecting abnormalities in machine-based PET inspection procedures. INDEX TERMS Image processing, machine vision, neural nets, data mining.

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Section: Adaptive Methods For Correcting Measured Color Intensity Vmentioning

confidence: 99%

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

et al. 2020

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“…Auto light is observed less often in industrial vision systems, but the principle is similar to that of auto focusing. [4,5] The study showed that automatic illumination can be achieved by light variation instead of optics variation in auto focusing. Common light control is based on a single light, but the best light for a specific target can differ.…”

Section: Introductionmentioning

confidence: 99%

Mixing and Simplex Search for Optimal Illumination in Machine Vision

Kim

Cho

Jin

et al. 2014

International Journal of Optomechatronics

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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 þ 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.

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“…We considered how to decrease the iterative action and accelerate the maximum search using one variable optimum methods. [5] HyungTae, SeungTaek Kim Single color illumination is mostly used but multi color illumination is employed in recent. Current multi color illumination in machine vision is focused on generating uniform and exact color by mixing RGB LEDs.…”

Section: Introductionmentioning

confidence: 99%

Color mixing and random search for optimal illumination in machine vision

Kim

Cho

Kim

et al. 2013

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

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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.

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A review of light intensity control and quick optimum search in machine vision

Cited by 6 publications

References 12 publications

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

Mixing and Simplex Search for Optimal Illumination in Machine Vision

Color mixing and random search for optimal illumination in machine vision

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