• 대한전자공학회논문지SP
• /
• 제44권2호
• /
• pp.93-101
• /
• 2007

정합과 노출 보정을 동시에 최적화하기 위하여 반복적인 정합 알고리듬인 Lucas-Kanade 알고리듬을 히스토그램 변환에 기초한 노출 보정 알고리듬과 접목하였다. 단순 회귀 모델에 기초하여 비매개변수 추정인 실험적 조건 평균과 그의 다항식 근사를 이용하여 노출 보정을 시도하였다. 제안한 동시 최적화 알고리듬은 각 최적화 과정의 분리화가 가능하므로 기존의 Mann이나 Candocia의 동시 최적화 알고리듬에 비하여 구현의 융통성 측면에서 유리하다. 투사 공간 변환 관계를 가지는 실영상 들을 가지고 모의실험을 수행한 결과에서 보면 노출 보정을 고려하지 않은 경우에 비하여 좋은 성능을 얻음을 확인할 수 있었다.

• 한국멀티미디어학회논문지
• /
• 제24권8호
• /
• pp.1059-1067
• /
• 2021

In this paper, a HDR algorithm for a single image is proposed using the exposure fusion, that adaptively calculates gamma correction coefficients according to the image distribution. Since typical HDR methods should use at least three images with different exposure values at the same scene, the main problem was that they could not be applied at the single shot image. Thus, HDR enhancements based on a single image using tone mapping and histogram modifications were recently presented, but these created some location-specific noises due to improper corrections. Therefore, the proposed algorithm calculates proper gamma coefficients according to the distribution of the input image and generates different exposure images which are corrected by the dark and the bright region stretching. A HDR image reproduction controlling exposure fusion weights among the gamma corrected and the original pixels is presented. As the result, the proposed algorithm can reduce certain noises at both the flat and the edge areas and obtain subjectively superior image quality to that of conventional methods.

• 한국환경보건학회지
• /
• 제50권1호
• /
• pp.43-53
• /
• 2024

Background: Exposure assessment is an important part of risk assessment for consumer products. Exposure models are used when estimating consumer exposures by considering exposure routes, subjects, and circumstances. These models differ based on their tiers, types, and target populations. Consequently, exposure estimates may vary between models. Objectives: This study aimed to compare the results of different exposure models using identical exposure factors. Methods: Chemical exposure from consumer products was calculated using four consumer exposure assessment models: Targeted Risk Assessment 3.1, Consumer Exposure Model 2.1 (CEM), ConsExpo web 1.1.1, and the Korean Exposure Algorithm (primary and detailed) issued by the Ministry of Environment, No. 972 (MOE). The same exposure factors were used in each model to calculate inhalation and dermal exposures to acetaldehyde, d-limonene, and naphthalene in all-purpose cleaners, leather coating sprays, and sealants. Results: In the results, TRA provided the highest estimate. Generally, MOE (detailed), CEM and ConsExpo showed lower exposures. The inhalation exposure for leather coating spray showed the largest differences between models, with differences reaching up to 1.2×10⁷ times. Since identical inputs were used for the calculations, it is likely that the models significantly influenced the estimated results. Conclusions: Despite using the same exposure factors to calculate dermal and inhalation exposures, the results varied substantially based on the model's exposure algorithm. Therefore, selecting an exposure model for assessing consumer products should be done with careful consideration.

• 한국멀티미디어학회논문지
• /
• 제24권11호
• /
• pp.1461-1471
• /
• 2021

This paper presents a light exposure correction algorithm for less pleasant images, acquired with a light metering failure. Since conventional tone mapping and gamma correction methods adopt a function mapping with the same range of input and output, the results are pleasurable for almost symmetric distributions to their intensity average. However, their corrections gave insufficient outputs for asymmetric cases at either bright or dark regions. Also, histogram modification approaches show good results on varied pattern images, but these generate unintentional noises at flat regions because of the compulsive shift of the intensity distribution. Therefore, in order to sufficient corrections for both bright and dark areas, the proposed algorithm calculates the gamma coefficients using primary parameters extracted from the global distribution. And the fusion weights are adaptively determined with complementary parameters, considering the classification information of a binary segmentation. As the result, the proposed algorithm can obtain a good output about both the symmetric and the asymmetric distribution images even with severe exposure values.

• 한국정보통신학회논문지
• /
• 제13권1호
• /
• pp.29-36
• /
• 2009

본 논문은 모바일 폰 카메라의 적용을 위한 자동노출 (AE, Auto Exposure)기능의 알고리즘 및 하드웨어 설계에 관한 것이다. 자동노출기능은 카메라로 촬영하기 위한 피사체의 밝기를 적절하게 하기 위해 자동으로 노출을 조정해 주는 기능이며 기존의 자동노출 기능은 마이크로 컨트롤러(MCU)에 의해 수행됨으로써 고비용과 느린동작속도 등의 문제점이 있었다. 본 논문에서는 마이크로 컨트롤러를 사용하지 않고 하드웨어로 구현 가능한 자동노출 기능의 알고리즘을 개발함으로써 시스템의 제작비용 및 동작 속도 등 기존 시스템에 대한 단점을 개선할 수 있도록 하였다. 하드웨어 자원사용의 효율성을 고려하여 알고리즘을 제안하였으며 제안된 알고리즘의 하드웨어 설계와 설계된 시스템을 실제 모바일 폰 카메라 센서에 적용하여 테스트함으로써 개발된 자동노출 기능을 확인하였다.

• 한국산업보건학회지
• /
• 제29권3호
• /
• pp.358-367
• /
• 2019

Objectives: This study aimed to review model algorithms and input parameters applied to some exposure models and to compare the simulated estimates using an exposure scenario from each model. Methods: A total of five exposure models which can estimate inhalation exposure were selected; the Korea Ministry of Environment(KMOE) exposure model, European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment(ECETOC TRA), SprayExpo, and ConsExpo model. Algorithms and input parameters for exposure estimation were reviewed and the exposure scenario was used for comparing the modeled estimates. Results: Algorithms in each model commonly consist of the function combining physicochemical properties, use characteristics, user exposure factors, and environmental factors. The outputs including air concentration ($mg/m^3$) and inhaled dose(mg/kg/day) are estimated applying input parameters with the common factors to the algorithm. In particular, the input parameters needed to estimate are complicated among the models and models need more individual input parameters in addition to common factors. In case of CEM, it can be obtained more detailed exposure estimates separating user's breathing zone(near-field) and those at influencing zone(far-field) by two-box model. The modeled exposure estimates using the exposure scenario were similar between the models; they were ranged from 0.82 to $1.38mg/m^3$ for concentration and from 0.015 to 0.180 mg/kg/day for inhaled dose, respectively. Conclusions: Modeling technique can be used for a useful tool in the process of exposure assessment if the exposure data are scarce, but it is necessary to consider proper input parameters and exposure scenario which can affect the real exposure conditions.

• 한국산업보건학회지
• /
• 제24권2호
• /
• pp.219-228
• /
• 2014

Objectives: The major objective of this study was to develop a tier 2 exposure model combining tier 1 exposure model estimates and worker monitoring data and suggesting narrower exposure ranges than tier 1 results. Methods: Bayesian statistics were used to develop a tier 2 exposure model as was done for the European Union (EU) tier 2 exposure models, for example Advanced REACH Tools (ART) and Stoffenmanager. Bayesian statistics required a prior and data to calculate the posterior results. In this model, tier 1 estimated serving as a prior and worker exposure monitoring data at the worksite of interest were entered as data. The calculation of Bayesian statistics requires integration over a range, which were performed using a Riemann sum algorithm. From the calculated exposure estimates, 95% range was extracted. These algorithm have been realized on Excel spreadsheet for convenience and easy access. Some fail-proof features such as locking the spreadsheet were added in order to prevent errors or miscalculations derived from careless usage of the file. Results: The tier 2 exposure model was successfully built on a separate Excel spreadsheet in the same file containing tier 1 exposure model. To utilize the model, exposure range needs to be estimated from tier 1 model and worker monitoring data, at least one input are required. Conclusions: The developed tier 2 exposure model can help industrial hygienists obtain a narrow range of worker exposure level to a chemical by reflecting a certain set of job characteristics.

• IEIE Transactions on Smart Processing and Computing
• /
• 제1권1호
• /
• pp.8-16
• /
• 2012

An image obtained from a low light environment results in a low-exposure problem caused by non-ideal camera settings, i.e. aperture size and shutter speed. Of particular note, the multiple color-filter aperture (MCA) system inherently suffers from low-exposure problems and performance degradation in its image classification and registration processes due to its finite size of the apertures. In this context, this paper presents a novel method for the color enhancement of low-exposure images and its application to color shift model-based MCA system for image refocusing. Although various histogram equalization (HE) approaches have been proposed, they tend to distort the color information of the processed image due to the range limits of the histogram. The proposed color enhancement algorithm enhances the global brightness by analyzing the basic cause of the low-exposure phenomenon, and then compensates for the contrast degradation artifacts by using an adaptive histogram specification. We also apply the proposed algorithm to the preprocessing step of the refocusing technique in the MCA system to enhance the color image. The experimental results confirm that the proposed method can enhance the contrast of any low-exposure color image acquired by a conventional camera, and is suitable for commercial low-cost, high-quality imaging devices, such as consumer-grade camcorders, real-time 3D reconstruction systems, digital, and computational cameras.

• 방사선산업학회지
• /
• 제17권3호
• /
• pp.275-282
• /
• 2023

Workers in nuclear power plants are likely to be exposed to radiation from various geometrical sources. In order to evaluate the exposure level, the point-kernel method can be utilized. In order to perform a dose assessment based on this method, the radiation source should be divided into point sources, and the number of divisions should be set by the evaluator. However, for the general public, there may be difficulties in selecting the appropriate number of divisions and performing an evaluation. Therefore, the purpose of this study is to develop an algorithm for dose assessment for arbitrary shaped sources based on the point-kernel method. For this purpose, the point-kernel method was analyzed and the main factors for the dose assessment were selected. Subsequently, based on the analyzed methodology, a dose assessment algorithm for arbitrary shaped sources was developed. Lastly, the developed algorithm was verified using Microshield. The dose assessment procedure of the developed algorithm consisted of 1) boundary space setting step, 2) source grid division step, 3) the set of point sources generation step, and 4) dose assessment step. In the boundary space setting step, the boundaries of the space occupied by the sources are set. In the grid division step, the boundary space is divided into several grids. In the set of point sources generation step, the coordinates of the point sources are set by considering the proportion of sources occupying each grid. Finally, in the dose assessment step, the results of the dose assessments for each point source are summed up to derive the dose rate. In order to verify the developed algorithm, the exposure scenario was established based on the standard exposure scenario presented by the American National Standards Institute. The results of the evaluation with the developed algorithm and Microshield were compare. The results of the evaluation with the developed algorithm showed a range of 1.99×10^-1~9.74×10^-1 μSv hr^-1, depending on the distance and the error between the results of the developed algorithm and Microshield was about 0.48~6.93%. The error was attributed to the difference in the number of point sources and point source distribution between the developed algorithm and the Microshield. The results of this study can be utilized for external exposure radiation dose assessments based on the point-kernel method.

• 대한방사선기술학회지:방사선기술과학
• /
• 제43권6호
• /
• pp.503-509
• /
• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.