• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.67-72
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• 2005

Most of image binarization algorithms analyzes the intensity distribution using the histogram for the determination of threshold value. When the intensity difference between the foreground object and the background is great, the histogram shows the tendency to be bimodal and the selection of the histogram valley as the threshold value shows the good result. On the other side. when the intensity difference is not great and the histogram doesn't show the bimodal property, the histogram analysis doesn't support the selection of the proper threshold value. This Paper Proposed the novel binarization method that applies the fuzzy membership function to each color value on the RGB color model and, by using the operation results, separates the features having the great readability from the background. The proposed method prevents the loss of information incurred by the gray scale conversion by using the RGB color model and extracts effectively the readable features by using the fuzzy inference Compared with the traditional binarization methods, the proposed method is able to remove the majority of noise areas and show the improved results on the image of transport containers , etc.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.38-47
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• 2008

A vehicle counting system at intersection is designed and implemented using analyzing a video stream from a camera. To separate foreground image from background, we compare three different methods, among which Li's method is chosen. Blobs are extracted from the foreground image using connected component analysis and the blobs are tracked by a blob tracker, frame by frame. The primary tracker use only the size and location of blob in foreground image. If there is a collision between blobs, the mean-shift tracking algorithm based on color distribution of blob is used. The proposed system is tested using real video data at intersection. If some huristics is applied, the system shows a good detection rate and a low error rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.666-671
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• 2008

In this paper, we propose a new method for tracking objects continously and successively based on fusion of region information, color information and motion template when multiple objects are occluded and splitted. For each frame, color template is updated and compared with the present object. The predicted region, dynamic template and color histogram are used to classify the objects. The vertical histogram of the silhouettes is analyzed to determine whether or not the foreground region contains multiple objects. The proposed method can recognize more correctly the objects to be tracked.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.241-247
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• 2007

자궁 경부 세포진 영상의 핵 추출을 위해서는 영상의 배경과 핵 그리고 세포질 영역의 구분이 중요하다. 또한 정상 세포핵과 암종 세포핵의 구분 및 인식을 위해서는 세포핵들의 형태학적 특징을 이용한 분류 기준을 세워야한다. 본 논문에서는 자궁 경부 세포진 영상에서 세포핵의 후보 영역과 핵을 추출하기 위해 현미경 400배율 확대 사진을 획득하는 과정에서 훼손된 컬러 영상을 복원하기 위한 방법으로 Lighting Compensation을 적용하여 영상을 보정한다. 그리고 배경 영역과 세포핵 영역을 구분하기 위해 영상의 R,G,B 영역의 히스토그램의 분포를 이용하여 배경을 제거한다. 배경이 제거된 영상을 그레이 영상으로 변환 한 후, 히스토그램 명암도의 값을 이용하여 세포핵 영역과 세포질을 분류하여 세포핵 영역을 추출한다. 그리고 Kapur 방법을 적용하여 세포핵 영역의 엔트로피 누적확률을 구한 후, 영상을 이진화 한다. Kapur 방법이 적용된 이진화 영상에서 세포핵 영역의 중심과 주위 화소를 비교하는 $3\times3$ 마스크를 적용하여 영상의 미세한 잡음을 제거 한 후, 8방향 윤곽선 추적 알고리즘을 적용하여 최종적으로 세포핵 영역을 추출한다. 추출된 세포핵의 영역을 분류 및 인식하는 과정으로 세포의 외각의 방향성 정보, 핵의 크기, 그리고 면적 비율의 특징을 이용하여 퍼지 소속 함수를 설계한 후, 소속 함수의 소속도를 구하고 퍼지 추론 규칙을 적용하여 자궁 경부 세포진 영상에서 정상 세포핵 및 암종 세포핵을 인식한다.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.86.1-86.1
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• 2012

Sloan Digital Sky Survey (SDSS) DR7 분광자료를 이용하여 z < 0.01에 존재하는 필드(field) 은하 505개를 선별하였다. SDSS의 컬러 영상을 이용하여 필드은하들의 형태(morphology)를 타원은하/왜소타원은하, 렌즈은하/왜소렌즈은하, 나선은하, 그리고 불규칙은하로 분류하였으며, 이들 은하들에 대한 측광을 수행하였다. 한편, Galex Evolution Explorer (GALEX) 자외광 영상으로부터 선별된 은하들에 대한 자외광 측광도 수행하였다. 서로 다른 환경에 있는 은하들의 최근 별탄생 활동이 어떻게 변화하는지 파악하기 위하여 처녀자리은하단 (Virgo cluster)에 있는 은하들과 비교하여 필드은하들의 자외광-가시광 색-등급 분포를 조사하였다. 처녀자리은하단 은하들에 비하여 필드은하들은 작은 자외광-가시광색지수를 가지는 만기형 은하들의 비율이 높았다. 특히, 조기형 필드은하들의 색지수 값이 처녀자리은하단 조기형 은하들에 비해 전반적으로 더 작은 값을 나타내고, 이 특징은 질량이 작은 왜소은하에서 더욱 크게 나타남을 알 수 있었다. 한편, 환경적 효과를 거의 받지 않는 필드은하들은 환경적 효과가 큰 은하단 은하들의 진화 연구에서 환경적 효과의 크기 및 기작을 규명하는 기본 자료로 사용될 수 있음을 토의하고자 한다.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.373-386
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• 2013

In this research, we specify the image consumers' preferred image quality ranges based on objective image quality evaluation factors and follow a method which measures preference of the natural image scenes. In other words, according to No-Reference, we select dynamic range, color, and contrast as factors of image quality measurements. For collecting sample images, we choose the preferred 200 landscapes which have over 30 recommendations by image consumers on the internet photo gallery. According to the scores of three objective factors of image quality measurements, the final expected score which means the image quality preference is measured and its total score is 100 points. In the main test, the actual image sample shows dynamic range 10 stop, LAB mean value L:54.7, A:2.96, B:-15.84, and RSC contrast 376.9. Total 200 image samples' normal distribution z value represents in dynamic range 0.21, LAB mean value L:0.15, A:0.38, B:0.13, and RSC contrast 0.08. In the standard normal distribution table, we can convert the z value as a percentage; dynamic range is 8.32%, LAB mean value is L:5.96%, A:14.8%, B:5.17%, and RSC contrast is 3.19%. And then, we convert the percentage values into the scores of 100; dynamic range is 91.68, LAB mean value is 91.36, and RSC contrast is 96.81. Therefore, we can conclude that the sample image's total mean score is 94.99 based on three objective image quality factors. Throughout our proposed image quality assessment model, we can measure the preference value of natural scenes. Also, we can specify the preferred image quality representation ranges and measure the expected image quality preference.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.575-592
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• 2018

This study investigates the formation mechanism of columnar joints at the Sanin Kaigan Geopark in Japan based on its morphology, rock type and igneous structure. Columnar joints distribute to five areas of three prefectures. That is, Kyogasaki, Byobuiwa, Tateiwa and Kyugenkado in Kyotango area of Kyoto prefecture; Genbudo and Kinumaki Shrine in Toyooka City of Hyogo prefecture; Yoroinosode, Takanosushima, Mini-Yoroinosode in Kamicho Town of Hyogo prefecture; Miooshima and Nagasakihana, Shitaara Domon, Kuzakuishi, Moroyose dyke in Shinonsencho Town of Hyogo prefecture; Shirawara dyke in Iwamicho Town in Tottori prefecture. Igneous structures are divided into three types: lava flow, sill and dyke. Lithologies are divided into five types including basalt, andesite, dacite, rhyolite, and quartz porphyry. Lava flow shows colonnade and entablature. However, entablature is not seen in the sills and dykes in the area. Although the polygons of columnar joint vary from tetragon to octagon, hexagon is most frequently found. The width of colonnades ranges from 10cm to 1m, but their size does not correlate with its $SiO_2$ contents. Meanwhile, their size and morphology at single site are comparatively uniform.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.71-91
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• 2018

In recent years, application of UAV(Unmanned Aerial Vehicle) to seed sowing and pest control has been actively carried out in the field of agriculture. In this study, UAS(Unmanned Aerial System) is constructed by combining image sensor of various wavelength band and SfM((Structure from Motion) based image analysis technique in UAV. Utilization of UAS based vegetation survey was investigated and the applicability of precision farming was examined. For this purposes, a UAS consisting of a combination of a VIS_RGB(Visible Red, Green, and Blue) image sensor, a modified BG_NIR(Blue Green_Near Infrared Red) image sensor, and a TIR(Thermal Infrared Red) sensor with a wide bandwidth of $7.5{\mu}m$ to $13.5{\mu}m$ was constructed for a low cost UAV. In addition, a total of ten vegetation indices were selected to investigate the chlorophyll, nitrogen and water contents of plants with visible, near infrared, and infrared wavelength's image sensors. The images of each wavelength band for the test area were analyzed and the correlation between the distribution of vegetation index and the vegetation index were compared with status of the previously surveyed vegetation and ground cover. The ability to perform vegetation state detection using images obtained by mounting multiple image sensors on low cost UAV was investigated. As the utility of UAS equipped with VIS_RGB, BG_NIR and TIR image sensors on the low cost UAV has proven to be more economical and efficient than previous vegetation survey methods that depend on satellites and aerial images, is expected to be used in areas such as precision agriculture, water and forest research.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.243-248
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• 2011

In this paper, we propose a new method to extract palm lines and read it with simple techniques from one photo. We use morphological information and 8-directional contour tracking algorithm. From the digitalized image, we transform original RGB information to YCbCr color model which is less sensitive to the brightness information. The palm region is extracted by simple threshold as Y:65~255, Cb:25~255, Cr:130~255 of skin color. Noise removal process is then followed with morphological information of the palm such that the palm area has more than quarter of the pixels and the rate of width vs height is more than 2:1 and 8-directional contour tracking algorithm. Then, the stretching algorithm and Sobel mask are applied to extract edges. Another morphological information that the meaningful edges(palm lines) have between 10 and 20 pixels is used to exclude noise edges and boundary lines of the hand from block binarized image. Main palm lines are extracted then by labeling method. This algorithm is quite effective even reading the palm from a photographed by a mobile phone, which suggests that this method could be used in various applications.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.117-130
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• 2008

This paper proposes a stable and robust method to distinct the color similarity for clothes using the LBG algorithm under various light sources, Since the conventional methods, such as the histogram intersection and the accumulated histogram, are profoundly sensitive to the changing of light environments, the distinction of color similarity for the same cloth can be different due to the complicated light sources. To reduce the effects of the light sources, the properties of hue and saturation which consistently sustain the characteristic of the color under the various changes of light sources are analyzed to define the characteristic of the color distribution. In a two-dimensional space determined by the properties of hue and saturation, the LBG algorithm, a non-parametric clustering approach, is applied to examine the color distribution of images for each clothes. The color similarity of images is defined by the average of Euclidean distance between the mapping clusters which are calculated from the result of clustering of both images. To prove the stability of the proposed method, the results of the color similarity between our method and the traditional histogram analysis based methods are compared using a dozen of cloth examples that obtained under different light environments. Our method successively provides the classification between the same cloth image pair and the different cloth image pair and this classification of color similarity for clothe images obtains the 91.6% of success rate.