• 한국환경과학회지
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• 제10권1호
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• pp.35-39
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• 2001

The mutagenicity of the river water of Nakdong river estuary was determined by Ames test using the blue rayon suspension method. Samples were collected from 10 sites in the estuary once in each season of 1998. The samples collected from the sites where industrial waste discharge on May were mutagenic, but the other samples were not mutagenic. The sample collected from the site 1 located near the industrial area (Hadan-dong) were highly mutagenic in the TA98 with (+S9) and without (-S9) mix as well as in the TA100 with (+S9) and without (-S9) S9 mix, suggesting that the river water of this site is polluted by direct and indirect mutagens of frame-shift type as well as direct and indirect mutagens of base-replacement type. The positive mutagenicity, although relatively low, was also detected in TA98 with (+S9) and without (-S9) S9 mix in the extract of the site 4 near the industrial area(Jangrim-dong), suggesting that the primary mutation type is frame-shift. The negative mutagenicity from July to December at the sites (1-4) near the industrial area seems to be affected by the low economic growth rate in 1998 in Korea. On the other hand, the negative mutagenicity in all extracts collected from the sites 5-10 near the residential area where living sewage discharge, suggests that the river water was not polluted by mutagens.

• 한국정보통신학회논문지
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• 제10권5호
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• pp.871-876
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• 2006

본 논문은 공간 부호화 패턴들을 이용하여 3차원 얼굴 정보를 정확하게 측정하기 위하여 초기 얼굴 패턴 영상으로부터 이미지 패턴을 검출하기 위한 새로운 알고리즘을 제안한다. 획득된 영상이 불균일하거나 패턴의 경계가 명확하지 않으면 패턴을 분할하기가 어렵다. 그리고 누적된 오류로 인하여 코드화가 되지 않는 영역이 발생한다. 본 논문에서는 이러한 요인에 강하고 코드화가 잘 될 수 있도록 FCM 클러스터링 방법을 이용하였다. 패턴 분할을 위하여 클러스터는 2개, 최대 반복횟수는 100, 임계값은 0.00001로 설정하여 실험하였다. 제안된 패턴 분할 방법은 기존 방법들(Otsu, uniform error, standard deviation, Rioter and Calvard, minimum error, Lloyd)에 비해 8-20%의 분할 효율을 향상시켰다.

• 감성과학
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• 제8권1호
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• pp.9-15
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• 2005

Because hand-written characters, especially drawn by a brush can give readers various impressions, they are not only a communication method but also art works. Authors have already investigated the relationship between brush motion analytical results and sensory testing results obtained from Japanese hiragana and reported quantitative evaluation method for the beauty of hiragana, In this paper, sensory tests for South Koreans who cannot recognize the word are carried out, compared with sensory testing results of Japanese. The evaluation objects are 6 hiragana drawn by 4 beginners and 2 experts, Semantic Differential Method based on 30 paired evaluation words are used in the sensory tests. Therefore South Koreans also feel the beauty in hiragana drawn by experts, as compared with by beginners. On the other hand it was confirmed that South Koreans couldnt recognize the difference among beginners. Judging from the factor analysis results, both Japanese and South Koreans selected stability as the 1st factor, there is interesting difference in the following orders.

• 산업경영시스템학회지
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• 제43권1호
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• 한국지능시스템학회논문지
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• 제21권5호
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• pp.543-548
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• 2011

영상의 세세한 부분에 대한 표현 정확도를 나타내는 엔트로피는 일반적으로 영상이 가진 그레이 레벨의 도수, 즉, 히스토그램을 바탕으로 얻어지며, 영상의 이진화를 위한 지표로 널리 사용되어 왔다. 본 논문에서는 이러한 영상 이진화를 위한 엔트로피 계산에 있어서 히스토그램이 아닌 그레이 레벨의 분산을 이용한 엔트로피를 바탕으로 그레이 영상을 이진화하는 알고리즘을 제안하고, 9개의 시험 영상에 대한 실험과 기존의 영상 이진화 기법인 오츠 기법 및 히스토그램을 이용한 엔트로피 기반의 임계값 결정법과의 비교 및 검토를 통하여 제안된 기법의 효용성을 보인다.

• 한국컴퓨터정보학회논문지
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• 제22권5호
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• pp.57-63
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• 2017

In the paper, we propose a new reliable technique for measuring food intake based on image automatically without user intervention. First, food and bowl image before and after meal is obtained by user. The food and the bowl are divided into each region by the K-means clustering, Otsu algorithm, Morphology, etc. And the volume of food is measured by a proportional expression based on the information of the container such as it's entrance diameter, depth, and bottom diameter. Finally, our method calculates the volume of the consumed food by the difference between before and after meal. The proposed technique has higher accuracy than existing method for measuring food intake automatically. The experiment result shows that the average error rate is up to 7% for three types of containers. Computer simulation results indicate that the proposed algorithm is a convenient and accurate method of measuring the food intake.

• 전기학회논문지
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• 제63권1호
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• pp.173-178
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• 2014

In this study, the new color image processing algorithms with an android-based mobile device are developed to detect the abnormal color densities in a skin image and interpret them as the vitiligo lesions. Our proposed method is firstly based on transforming RGB data into HSI domain and segmenting the imag into the vitiligo-skin candidates by applying Otsu's threshold algorithm. The structure elements for morphological image processing are suggested to delete the spurious regions in vitiligo regions and the image blob labeling algorithm is applied to compare RGB color densities of the abnormal skin region with them of a region of interest. Our suggested color image processing algorithms are implemented with an android-platform smartphone and thus a mobile device can be utilized to diagnose or monitor the patient's skin conditions under the environments of pervasive healthcare services.

• Journal of Magnetics
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• 제18권3호
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• pp.311-316
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• 2013

Digital image processing has increasingly been implemented in nanostructural analysis and would be an ideal tool to characterize the morphology and position of self-assembled magnetic nanoparticles for high density recording. In this work, magnetic nanoparticles were synthesized by the modified polyol process using $Fe(acac)_3$ and $Pt(acac)_2$ as starting materials. Transmission electron microscope (TEM) images of as-synthesized products were inspected using an image processing procedure. Grayscale images ($800{\times}800$ pixels, 72 dot per inch) were converted to binary images by using Otsu's thresholding. Each particle was then detected by using the closing algorithm with disk structuring elements of 2 pixels, the Canny edge detection, and edge linking algorithm. Their centroid, diameter and area were subsequently evaluated. The degree of polydispersity of magnetic nanoparticles can then be compared using the size distribution from this image processing procedure.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 추계학술대회
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• pp.626-628
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• 2016

본 논문에서는 이진 영상에서 일부 정보가 손실된 경우에 히스토그램을 분석하여 구간을 분할한 후, 오츠 이진화와 퍼지 이진화 기법을 적용하여 원 영상을 이진화 한 후에 홉필드 네트워크를 적용하여 영상을 복원하는 방법을 제안한다. 제안된 방법은 그레이 영상에서 히스토그램을 분석하여 픽셀 값의 변화의 폭이 큰 부분들을 분석하여 구간들을 분할하고 변화의 폭이 큰 부분의 지점에 속하는 영역은 오츠 이진화 기법을 적용하여 이진화하고 그 외의 구간들은 퍼지 이진화 기법을 적용하여 영상을 이진화 한다. 그리고 이진화 된 영상을 홉필드 네트워크를 적용하여 학습한다. 실험 영상에 정보 손실이 발생한 영상을 대상으로 제안된 방법을 적용한 결과, 대부분의 정보 손실이 있는 영상에서 모두 복원되는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4866-4888
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• 2020

One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.