• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.73-80
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• 2019

In this paper, we propose an effective dynamic range compression (DRC) method of infrared images. A histogram of infrared images has narrow dynamic range compared to visible images. Hence, it is important to apply the effective DRC algorithm for high performance of an infrared image analysis. The proposed algorithm for high dynamic range divides an infrared image into the overlapped blocks and calculates Shannon's entropy of overlapped blocks. After that, we classify each block according to the value of entropy and apply adaptive histogram modification method each overlapped block. We make an intensity mapping function through result of the adaptive histogram modification method which is using standard-deviation and maximum value of histogram of classified blocks. Lastly, in order to reduce block artifact, we apply hanning window to the overlapped blocks. In experimental result, the proposed method showed better performance of dynamic range compression compared to previous algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.377-378
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• 2008

• Korean Journal of Pharmacognosy
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• v.44 no.4
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• pp.344-349
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• 2013

Near infrared spectroscopy (NIRS) combined with multivariate analysis was used to discriminate the geographical origin of Alisma orientale from Korea (n=94) and China (n=72). Two-thirds of samples were selected randomly for the training set, and one-third of samples for the test set. Second derivative was used for the pretreatment of NIR spectra. Partial least square discriminant analysis (PLS-DA) models correctly discriminated 100% of the Korean and Chinese A. orientale samples. These results demonstrate the potential use of NIR spectroscopy combined with multivariate analysis as a rapid and accurate method to discriminate A. orientale according to their geographical origin.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.1
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• pp.1-13
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• 2005

The purpose of this paper analyze development feasibility and alternatives for infrared detector development in both technology and cost. Infrared Detector is core component of Thermal Imaging System and developed by ADD from 2006 10 2008 year. We got raw input data from development and technical expert, and then analyze cost and technology for development feasibility, and alternatives study. Technology level is analyzed by TRL(Technology Readiness Level) and AOA(Analysis of Alternatives) is done by development cost estimate. Estimating the development cost, we use SEER-H that is parametric cost estimating tool based on Knowledge Base. This study can help those who are related to the cost and development feasibility analysis of other weapon systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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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.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.4
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• pp.242-246
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• 2000

The Near Infrared Reflectance Spectroscopy(NIR) was used to evaluate nutritional diagnosis for rapid leaf analysis method, 177 'Fuji' apple and 130 'Campbell Early' grape leaves were measured by Near Infrared reflectance spectra in the NIR region(1,100~2.500nm). Total nitrogen content was measured by kjelldhal distillation, after salycilic acid-sulfuric acid digestion. An empirical equation to predict total nitrogen content from its spectral signature was developed by adapting the Near Infrared Reflectance Spectroscopy analysis(NIRa) technique and the results were apple-0.965(R). 0.086(SEC), grape-0.926(R), 0.152(SEC). Standard Error of Prediction(SEP) of NIRa for predicting the total nitrogen of apple and grape leaves was 0.360 and 0.210, respectively. It was concluded that Near infrared reflectance spectroscopy analysis is promising method for rapid analysis of apple and grape leaves.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.291-300
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• 2015

In the steel manufacturing process heat-endurance deterioration of a ladle used to cause a big accident. In this paper, an infrared imaging system and image analyzing procedure are proposed for inspecting refractory bricks of a ladle. The proposed algorithm contains following three parts: two-stage image selection procedure, reference point detection, and analysis of heat-endurance deterioration. Experiments were conducted with real data from a steel plant and detailed configuration of infrared imaging system was presented.

• Near Infrared Analysis
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• v.1 no.1
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• pp.9-17
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• 2000

A variety of standardization methods between two near-infrared (NIR) spectrometers were investigated for the prediction of five constituents in trans-alkylation process. Spectra were collected by two different instruments (one is regarded as mater instrument, other on as slave instrument). Three well-known standardization methods of direct standardization (DS), piecewise direct standardization (PDS) and slope/bias correction of response variable were applied to trans-alkylation samples on the slave instrument. We have examined for a set of reliable standardization samples using smaller number of transfer samples in order to increase efficiency of standardization.

• Food Science and Biotechnology
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• v.18 no.5
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• pp.1199-1203
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• 2009

Melamine is a chemical intermediate to manufacture amino resins and plastics, which cannot be used as food additive since it can cause kidney stones. A qualitative determination method of melamine residue in powdered and liquid milk was developed using Fourier transform infrared (FTIR) spectroscopic technique. The calibration sets consisted of 21 standard melamine solutions, in which 1% trichloroacetic acid aqueous solution and acetonitrile (3:1, v/v) were used as solvent. The model was validated using 10 standard melamine solutions which were unused to build up the calibration set. Infrared (IR) absorbance peaks specific to almost all chemical groups in melamine molecule were shown in the spectral range between 1,100 and 1,800/cm. Combined partial least squares (PLS)-$2^{nd}$ derivative calibration model coupled with mean centering (MC) mathematical enhancement showed the highest correlation coefficients ($R^2$>0.99). In brief, the FTIR technique can be used for quantitative analysis of melamine in milk samples.