• 한국토양비료학회지
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• 제47권5호
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• pp.319-323
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• 2014

This study investigates the prediction of soil chemical properties (organic matter (OM), pH, Ca, Mg, K, Na, total acidity, cation exchange capacity (CEC)) on 688 Korean soil samples using the visible-near infrared reflectance (VIS-NIR) spectroscopy. Reflectance from the visible to near-infrared spectrum (350 to 2500 nm) was acquired using the ASD Field Spec Pro. A total of 688 soil samples from 168 soil profiles were collected from 2009 to 2011. The spectra were resampled to 10 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil chemical properties. Principal components analysis (PCA), partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil chemical properties. The regression rules model (Cubist) showed the best results among these, with lower error on the calibration data. For quantitatively determining OM, total acidity, CEC, a VIS-NIR spectroscopy could be used as a routine method if the estimation quality is more improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5039-5055
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• 2018

The quality of natural outdoor images captured by visible camera sensors is usually degraded by the haze present in the atmosphere. In this paper, a fast image dehazing method based on visible image and near-infrared fusion is proposed. In the proposed method, a visible and a near-infrared (NIR) image of the same scene is fused based on the dual-tree complex wavelet transform (DT-CWT) to generate a dehazed color image. The color of the fusion image is regulated through haze concentration estimated by dark channel prior (DCP). The experiment results demonstrate that the proposed method outperforms the conventional dehazing methods and effectively solves the color distortion problem in the dehazing process.

• Journal of the Optical Society of Korea
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• 제12권2호
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• pp.79-87
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• 2008

We have developed a miniature fisheye lens with $190^{\circ}$ field of view operating simultaneously in the visible and the near infrared wavelengths. The modulation transfer function characteristic for the visible wavelength is sufficient for a mega-pixel-grade image sensor. The lens also has a fair resolution in the infrared wavelength region. The calibrated $f-{\theta}$ distortion is less than 5%, and the relative illumination is over 90%. In consequence, a sharp wide-angle image can be obtained which is uniform in brightness over the entire range of field angles. The real image heights for the visible and the near infrared wavelengths have been fitted to polynomial functions of incidence angle with sub-pixel accuracies. Combined with the near equidistance projection scheme of the lens, this lens can be advantageously employed in various image-processing applications requiring a wide-angle lens.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• 센서학회지
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• 제29권2호
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• pp.133-140
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• 2020

This study proposes a method of blending visible and near infrared images to enhance edge details and local contrast. The proposed method consists of radiance map generation and color compensation. The radiance map is produced by a Laplacian pyramid and a soft mixing method based on principal component analysis. The color compensation method uses the ratio between the composed radiance map and the luminance channel of a visible image to preserve the visible image chrominance. The proposed method has better edge details compared to a conventional visible and NIR image blending method.

• 한국염색가공학회지
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• 제17권4호
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• pp.7-14
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• 2005

The purpose of this study was to develop the color matching program with the excellent camouflage capacity in the near infrared range($\~$1100nm) including the visible light range for cotton fabrics. It was measured IR spectral reflectance in the range of $380\~1,100nm$ after dyed with vat dyes, and we made database for reflectance with various concentration on vat dyes which have a low reflectance value in the infrared range. The color matching algorithm that could be simulated in both the human visible light and the near infrared range was constructed by numerical analysis method using the database. In this study we also developed the dyeing conditions and dyeing process through the continuous-dyeing experiment with the vat dyes for cotton fabrics.

• 공업화학
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• 제32권2호
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• pp.125-131
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• 2021

일반적인 반도체 기반의 광촉매는 물질 고유의 밴드갭 때문에 자외선이나 가시광선에 의해서만 활성화될 수 있고, 태양광 에너지의 약 50%를 차지하는 근적외선 영역의 에너지는 활용할 수 없다. 따라서 기존의 반도체 광촉매의 성능을 향상시키기 위해서는 자외선에서 근적외선에 이르는 넓은 영역에서 더 많은 태양광 에너지를 활용할 수 있어야 한다. 태양광의 근적외선 영역을 활용하기 위해 기존 반도체 광촉매를 업컨버전 나노입자와 결합하는 연구들이 수행되고 있다. 업컨버전 나노입자는 근적외선 광자를 여러 개 흡수하여 자외선이나 가시광선으로 변환하여 광촉매를 활성화할 수 있다. 그리고 반도체 광촉매와 업컨버전 나노입자에 플라즈모닉 금속 나노입자를 함께 결합시키면 태양광에 의한 광촉매 활성을 더욱 향상시킬 수 있다. 본 총설은 업컨버전 나노입자를 이용하여 근적외선 영역의 태양광 에너지가 광촉매의 성능 향상에 기여할 수 있도록 하는 최근의 연구결과를 바탕으로 서술하였다.

• Journal of electromagnetic engineering and science
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• 제17권4호
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• pp.186-190
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• 2017

Visible-near-infrared (VIS-NIR) spectroscopy is a fast and non-destructive method for analyzing materials. However, most commercial VIS-NIR spectrometers are inappropriate for use in various locations such as in homes or offices because of their size and cost. In this paper, we classified eight food powders using a portable VIS-NIR spectrometer with a wavelength range of 450-1,000 nm. We developed three machine learning models using the spectral data for the eight food powders. The proposed three machine learning models (random forest, k-nearest neighbors, and support vector machine) achieved an accuracy of 87%, 98%, and 100%, respectively. Our experimental results showed that the support vector machine model is the most suitable for classifying non-linear spectral data. We demonstrated the potential of material analysis using a portable VIS-NIR spectrometer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.208-223
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• 2015

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.3105-3105
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• 2001

Ingests and fecal contamination on a poultry carcass is a food safety hazard due to potential microbiological contamination. A visible/near-infrared (NIR) spectrometer was used to discriminate among pure ingesta and fecal material, breast skin contaminated with ingesta or fecal material and uncontaminated breast skin. Birds were fed isocaloric diets formulated with either maize, mile, or wheat and soybean meal for protein requirements. Following completion of the feeding period (14 days), the birds were humanely processed and eviscerated to obtain ingests from the crop or proventriculus and feces from the duodenum, ceca, and colon portion of the digestive tract. Pure feces and ingesta, breast skin, and contaminated breast skin were scanned from 400 to 2500 nm and analyzed from 400 to 900 nm. Principal component analysis (PCA) of reflectance spectra was used to discriminate between contaminates and uncontaminated breast skin. Results indicate that visible (400 to 760 nm) and NIR 760-900 nm spectra can detect contaminates. From PCA analysis, key wavelengths were identified for discrimination of uncontaminated skin from contaminates based the evaluation of loadings weights.