• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.467-472
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• 2005

The principal objective of enhancement methods is to process an image so that the output image is more suitable than the original image lot a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compared the performance of Image Contrast Enhancement Technique Using Clustering Algorithm(ICECA) with those of color adjustment methods such as Histogram Equalization(HE), Brightness Preserving Bi-Histogram Equalization(BBHE), and the Multi-Scale Refiner(MSR). We compared these methods by applying the image enhancement methods to a set of diverse images.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.637-644
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• 2006

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

• Journal of Crop Science and Biotechnology
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• v.11 no.3
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• pp.157-162
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• 2008

A total of 2,765 accessions were used as the initial set having both seed coat color and 100-seed weight data. As a result of molecular profiling using six SSR markers followed by stratification based on their usages, 335 accessions(12.1%) were selected by clustering based on UPGMA. Since 75 out of 335 accessions were mixed in phenotypic traits as a result of characterization, 260 accessions were finally set as a core set. This core set revealed nearly the same diversity compared with the other results on morphological traits of Korean soybean landraces. In total, 115 alleles(19.2 alleles per locus) were detected in the initial set and 79 alleles(13.2 alleles per locus) were detected in the core set. All 30 major alleles were present in the initial set and in the core set as well. In allele coverage, the core set was 71.4% of the initial set. These comparisons of number of alleles, gene diversity and coverage indicated that the core set represented the entire set well.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.481-483
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• 2018

AR은 VR과 달리 실세계 공간의 객체에 대한 서비스를 제공하므로 서비스 개발을 방해하는 많은 요인들이 발생한다. 이를 보완하기위해 비주얼 마커, SLAM, 객체인식 등 여러 AR 기술이 존재한다. 본 논문은 AR 기술 중에서 객체인식의 정확도 향상을 위해 지역가변 이진화(Local variable binarization)와 색상의 군집화를 사용해서 이미지에서 객체를 추출하는 방법을 제안한다. 지역 가변화는 픽셀을 순차적으로 읽어 들이면서 픽셀 주위의 값의 평균을 구하고, 이 값을 해당 픽셀의 임계 값으로 사용하는 알고리즘이다. 픽셀마다 주위 색상 값에 의해 임계 값이 변화되므로 윤곽선 표현이 기존의 이진화보다 뚜렷이 나타난다. 색상의 군집화는 객체의 중요색상과 배경의 중요색상을 중심으로 유사한 색상끼리 군집화 하는 것이다. 객체 내에서 가장 많이 나온 값과 객체 외에 가장 많이 나온 값을 각 각 기준으로 색조와 채도의 값을 Euclidean 거리를 사용해 객체의 색상과 배경 색상을 분리했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.35-38
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• 2009

The paper presents an effective method to detect fire in video surveillance and monitoring system. The main contribution of this work is that we successfully use the Hidden Markov Models in the process of detecting the fire with a few preprocessing steps. First, the moving pixels detected from image difference, the color values obtained from the fire flames, and their pixels clustering are applied to obtain the image regions labeled as fire candidates; secondly, utilizing massive training data, including fire videos and non-fire videos, creates the Hidden Markov Models of fire and non-fire, which are used to make the final decision that whether the frame of the real-time video has fire or not in both temporal and spatial analysis. Experimental results demonstrate that it is not only robust but also has a very low false alarm rate, furthermore, on the ground that the HMM training which takes up the most time of our whole procedure is off-line calculated, the real-time detection and alarm can be well implemented when compared with the other existing methods.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.399-408
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• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• The Journal of Advanced Prosthodontics
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• v.11 no.5
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• pp.239-246
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• 2019

PURPOSE. The purpose of this study was to propose and assess a shade guide for pink gingival aesthetics using a Spanish population sample. MATERIALS AND METHODS. The $L^*$, $C^*$, h, $a^*$ and $b^*$ coordinates of 259 participants were measured using a spectrophotometer in 3 standardized points along the attached gingiva of the maxillary central incisors. A hierarchical clustering analysis was applied to obtain separate solutions regarding the number of shade tabs. For each of the solutions obtained, color differences (${\Delta}E^*$) were calculated using the CIELab and CIEDE2000 formulas, and the proposed shade guide was selected considering (1) the color differences between tabs and (2) the coverage error of each of the solutions. RESULTS. The proposed shade guide consisted of 8 gingival shade tabs and achieved CIELab and CIEDE2000 coverage errors of less than the respective 50:50% acceptability thresholds (${\Delta}E^*=4.6$ units and ${\Delta}E_{00}=4.1$). The coordinates for the various gingival shade tabs were as follows: Tab 1: $L^*43.3$, $a^*21.9$, $b^*12.3$ (1.6); Tab 2: $L^*42.9$, $a^*34.1$, $b^*19.1$; Tab 3: $L^*46.5$, $a^*25.8$, $b^*10.9$; Tab 4: $L^*46.5$, $a^*27.3$, $b^*15.1$; Tab 5: $L^*49.6$, $a^*23.5$, $b^*16.8$; Tab 6: $L^*51.5$, $a^*19.7$, $b^*13.6$; Tab 7: $L^*55.9$, $a^*22.0$, $b^*15.0$; and Tab 8: $L^*56.0$, $a^*19.9$, $b^*18.8$. CONCLUSION. The CIELab and CIEDE2000 coverage errors for the 8 shade tabs of the proposed gingival shade guide were significantly lower than those of other guides. Therefore, despite the limitations of this study, the proposed guide is more appropriate for matching gingival shade in the Spanish general population.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.608-617
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• 2017

Methods of contrast enhancement have problem such as side effect of over-enhancement with non-gaussian histogram distribution, tradeoff enhancement efficiency against brightness preserving. In order to enhance contrast at various histogram distribution, segmentation to region with gaussian distribution and then enhance contrast each region. First, we segment an image into several regions using GMM(Gaussian Mixture Model)fitting by that k-mean clustering and EM(Expectation-Maximization) in $L^*a^*b^*$ color space. As a result region segmentation, we get the region map and probability map. Then we apply local contrast enhancement algorithm that mean shift to minimum overlapping of each region and preserve brightness histogram equalization. Experiment result show that proposed region based contrast enhancement method compare to the conventional method as AMBE(AbsoluteMean Brightness Error) and AE(Average Entropy), brightness is maintained and represented detail information.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.3
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• pp.55-64
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• 2014

While utilizing high resolution satellite image for land use classification has been popularized, object-oriented classification has been adapted as an affordable classification method rather than conventional statistical classification. The aim of this study is to extract the paddy field area using object-oriented classification with time series NDVI from high-resolution satellite images, and the RapidEye satellite images of Jaeryung-gun in North Korea were used. For the implementation of object-oriented classification, creating objects by setting of scale and color factors was conducted, then 3 different land use categories including paddy field, forest and water bodies were extracted from the objects applying the variation of time-series NDVI. The unclassified objects which were not involved into the previous extraction classified into 6 categories using unsupervised classification by clustering analysis. Finally, the unsuitable paddy field area were assorted from the topographic factors such as elevation and slope. As the results, about 33.6 % of the total area (32313.1 ha) were classified to the paddy field (10847.9 ha) and 851.0 ha was classified to the unsuitable paddy field based on the topographic factors. The user accuracy of paddy field classification was calculated to 83.3 %, and among those, about 60.0 % of total paddy fields were classified from the time-series NDVI before the unsupervised classification. Other land covers were classified as to upland(5255.2 ha), forest (10961.0 ha), residential area and bare land (3309.6 ha), and lake and river (1784.4 ha) from this object-oriented classification.