• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.58-63
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• 2009

The appearance inspection of various electronic products and parts was executed by the eyesight of human. The appearance inspection is applied to the most electronic component of LCD Panel, flexible PCB and remote control. If the appearance of electronic products of small and minute size is inspected by the eyesight of human, we can't expect the stable inspection result because inspection result is changed by condition of physical and spirit of the checker. Therefore currently machine vision systems are used to many appearance inspection fields instead of inspection by human. The many problems of inspection by the checker are not occurred in machine vision circumstance. However, the inspection by automatic machine vision system is mainly influenced by illumination of workplace. In this paper, we propose a histogram transform method for improving accuracy of machine visual inspection.

• Korean Journal of Optics and Photonics
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• v.5 no.1
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• pp.1-8
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• 1994

A five mirror system with a reduction magnification(M=+1/5) is designed for DUV optical lithography. First, for spherical mirror systems, the numerical solutions of all zero 3rd order aberrations are derived and the 3-dimensional shape of the solution-domain is obtained. In these solutions, we select solutions which have as less residual aberrations and smaller central obscurration as possible and the aspherization is carried out to the last two spherical mirrors to obtain a system that has as higher NA as possible. Finally we obtain the system of which NA is 0.45, the central obscuration is about 25% and the resolution is about 650 cycles/mm at the 50% MTF value criterion and the depth of focus of 0.8${\mu}m$ for the nearly incoherent illumination (${\sigma}$=1.0) and the wavelength of 0.193${\mu}m$ (ArF excimer laser line).

• Journal of the Optical Society of Korea
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• v.20 no.4
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• pp.488-499
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• 2016

We present a design and optical simulation of a cost-effective hybrid daylighting/LED system composed of mixing sunlight and light-emitting diode (LED) illumination powered by renewable solar energy for indoor lighting. In this approach, the sunlight collected by the concentrator is split into visible and non-visible rays by a beam splitter. The proposed sunlight collector consists of a Fresnel lens array. The non-visible rays are absorbed by the solar photovoltaic devices to provide electrical power for the LEDs. The visible rays passing through the beam splitters are coupled to a stepped thickness waveguide (STW) by tilted mirrors and confined by total internal reflection (TIR). LEDs are integrated at the end of the STW to improve the lighting quality. LEDs’ light and sunlight are mixed in the waveguide and they are coupled into an optical fiber bundle for indoor illumination. An optical sensor and lighting control system are used to control the LED light flow to ensure that the total output flux for indoor lighting is a fixed value when the sunlight is inadequate. The daylighting capacity was modeled and simulated with a commercial ray tracing software (Lighttools^TM). Results show that the system can achieve 63.8% optical efficiency at geometrical concentration ratio of 630. A required accuracy of sun tracking system achieved more than ±0.5^o . Therefore, our results provide an important breakthrough for the commercialization of large scale optical fiber daylighting systems that are faced with challenges related to high costs.

• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.282-286
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• 2011

The artificial increase in the solar intensity incident on solar cells using lenses or mirrors can allow solar cells to generate equivalent power with a lower cost. In application areas of Fresnel lenses as solar concentrators, several variations of design were devised and tested. Some PV systems still use commercially available flat Fresnel lenses as concentrators. In this study, we designed and optimized flat Fresnel lens and the 'light pipe' to develop 500X concentrated solar PV system. We performed rigorous ray tracing simulation of the flat Fresnel lens and light-pipe. The light-pipe can play imporatant roles of redistributing solar energy at the solar cell and increase the mechanical tolerance so that it can increase the lifetime of the high-concentration solar PV system and decrease the cost of manufacturing. To investigate the sensitivity of the solar power generated by the concentrated solar PV according to the performance of lens and light pipe, we performed raytracing and executed a simulation of electrical performance of the solar cell when it is exposed to the non-uniform illumination. We could conclude that we can generate 95 % or more energy compared with the energy that can be generated by perfectly uniform illumination once the total energy is given the same.

• Korean Journal of Optics and Photonics
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• v.17 no.3
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• pp.240-247
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• 2006

This paper discusses the optical system design for projection TV using LCOS type micro display, which provides the high resolution, slim depth, and a large screen of more than 60 inches. We analyzed the relationship between the illumination system, projection lens, color separation & recombination system, and micro display. From this quantitative analysis, the starting data for the optimum light engine was defined, and all optical systems were designed by an optimization process. Three RGB panels were proposed for a high luminence system, and the four prisms symmetrically located make equal optical path lengths for the RGB rays. This color separation & recombination system enables the a compact illumination system. Also, in order to the slim light engine with high resolution, the folded projection lens system was designed by inserting a mirror between projection lenses.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.14-20
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• 2009

The goal of image processing is to improve the visual appearance of images for human viewers. The histogram is an important tool which can be used as basic data of digital image processing. Therefore, to effectively manage a histogram in digital image processing is very important. Currently machine vision systems are used in many appearance inspection fields instead of inspection by human. However, the appearance inspection result by machine vision system is mainly influenced by illumination of workplace. In this paper, we propose a histogram transform method for improving accuracy of machine visual inspection. The enhancement effect of area feature is obtained by performing proposed histogram transformation in area that needs improvement The proposed algorithm is verified by appearance inspection of jig plate samples.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.371-384
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• 2019

Motion perception has been tremendously improved in neuroscience and computer vision. The baseline motion perception model is mediated by the dorsal visual pathway involving the cortex areas the primary visual cortex (V1) and the middle temporal (V5 or MT) visual area. However, few works have been done on the extension of neural models to improve the efficacy and robustness of motion perception of real sequences. To overcome shortcomings in situations, such as varying illumination and large displacement, an adaptive V1-MT motion perception (Ad-V1MTMP) algorithm enriched to deal with real sequences is proposed and analyzed. First, the total variation semi-norm model based on Gabor functions (TV-Gabor) for structure-texture decomposition is performed to manage the illumination and color changes. And then, we study the impact of image local context, which is processed in extra-striate visual areas II (V2), on spatial motion integration by MT neurons, and propose a V1-V2 method to extract the image contrast information at a given location. Furthermore, we take feedback inputs from V2 into account during the polling stage. To use the algorithm on natural scenes, finally, multi-scale approach has been used to handle the frequency range, and adaptive pyramidal decomposition and decomposed spatio-temporal filters have been used to diminish computational cost. Theoretical analysis and experimental results suggest the new Ad-V1MTMP algorithm which mimics human primary motion pathway has universal, effective and robust performance.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.995-1010
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• 2020

In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.