• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.294-297
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• 2012

본 논문에서는 컬러 영상의 검색을 위하여 영상을 색상 정보에 기반한 pixel layer (cluster)의 집합체로 모델링하고, 두 layer 간의 유사도를 각 layer 를 이루는 pixel 들의 색상 분포에 따른 공간적 분포를 이용하여 측정하는 기법을 제안한다. 먼저 pixel layering 단계에서는 HSV 색 공간에서 mean-shift clustering 알고리즘을 통해 초기 layer 들을 얻고, 비슷한 색상의 layer 들은 합쳐 영상의 soft segmentation 과 유사한 결과를 얻는다. 비교할 두 영상에서 pixel layering 을 한 후, 각 layer 를 이진화된 공간분포 지도로 형성하고 그 차이를 비교함으로써 유사도를 측정한다. 이 때, 사용하는 가중치로서 HSV 색 공간 분포의 비슷한 정도를 정의하는데, 이는 HSV 색 공간을 XYZ 의 3 차원 좌표로 설정하고, overlap 되는 pixel 수로 정의하였다. 본 논문에서 제안한 pixel layer 들 간의 색상 공간 분포에 따른 공간적 분포를 이용한 영상 검색 기법은 MPEG-7 에서 정의한 대표색상 기반의 영상 검색보다 우수한 성능을 보여주었다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.25-27
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• 2002

고정 격자 방식의 공간 색인 방법은 간단한 구조와 단순한 색인 과정, 구현의 용이성이라는 장점이 있으나 데이터의 분포에 영향을 많이 받아 밀집된 데이터를 처리하기에는 적합하지 못한 특성이 있다. 이에 본 논문에서는 고정 격자 색인 방법에 합병 정책을 적용하여 고정 격자 색인 방법의 성능을 향상시키는 방법을 제안한다. 본 논문의 방법에 따르면 공간 효율성이 매우 증가하고 다른 공간 색인 방법에 비해 색인 과정이 단순해지며, 공간 색인의 성능이 다른 색인 방법에 비해 증가되는 장점을 가지고 있다.

• Trans-
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• v.12
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• pp.51-79
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• 2022

This study investigates the methods for deriving colors which can serve as a reference to users such as designers and or contents creators who search for online images from the web portal sites using specific words for color planning and more. Two experiments were conducted in order to accomplish this. Digital scenery photos within the geographic scope of Korea were downloaded from web portal sites, and those photos were studied to find out what colors were used to describe daytime and nighttime. Machine learning was used as the study methodology to classify colors in daytime and nighttime, and KSCA was used to derive the color frequency of daytime and nighttime photos and to compare and analyze the two results. The results of classifying the colors of daytime and nighttime photos using machine learning show that, when classifying the colors by 51~100%, the area of daytime colors was approximately 2.45 times greater than that of nighttime colors. The colors of the daytime class were distributed by brightness with white as its center, while that of the nighttime class was distributed with black as its center. Colors that accounted for over 70% of the daytime class were 647, those over 70% of the nighttime class were 252, and the rest (31-69%) were 101. The number of colors in the middle area was low, while other colors were classified relatively clearly into day and night. The resulting color distributions in the daytime and nighttime classes were able to provide the borderline color values of the two classes that are classified by brightness. As a result of analyzing the frequency of digital photos using KSCA, colors around yellow were expressed in generally bright daytime photos, while colors around blue value were expressed in dark night photos. For frequency of daytime photos, colors on the upper 40% had low chroma, almost being achromatic. Also, colors that are close to white and black showed the highest frequency, indicating a large difference in brightness. Meanwhile, for colors with frequency from top 5 to 10, yellow green was expressed darkly, and navy blue was expressed brightly, partially composing a complex harmony. When examining the color band, various colors, brightness, and chroma including light blue, achromatic colors, and warm colors were shown, failing to compose a generally harmonious arrangement of colors. For the frequency of nighttime photos, colors in approximately the upper 50% are dark colors with a brightness value of 2 (Munsell signal). In comparison, the brightness of middle frequency (50-80%) is relatively higher (brightness values of 3-4), and the brightness difference of various colors was large in the lower 20%. Colors that are not cool colors could be found intermittently in the lower 8% of frequency. When examining the color band, there was a general harmonious arrangement of colors centered on navy blue. As the results of conducting the experiment using two methods in this study, machine learning could classify colors into two or more classes, and could evaluate how close an image was with certain colors to a certain class. This method cannot be used if an image cannot be classified into a certain class. The result of such color distribution would serve as a reference when determining how close a certain color is to one of the two classes when the color is used as a dominant color in the base or background color of a certain design. Also, when dividing the analyzed images into several classes, even colors that have not been used in the analyzed image can be determined to find out how close they are to a certain class according to the color distribution properties of each class. Nevertheless, the results cannot be used to find out whether a specific color was used in the class and by how much it was used. To investigate such an issue, frequency analysis was conducted using KSCA. The color frequency could be measured within the range of images used in the experiment. The resulting values of color distribution and frequency from this study would serve as references for color planning of digital design regarding natural scenery in the geographic scope of Korea. Also, the two experiments are meaningful attempts for searching the methods for deriving colors that can be a useful reference among numerous images for content creator users of the relevant field.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.419-421
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• 2000

정확한 색 재현(Color Reproduction)을 위해서 영상 입력 장치(Image Input Device)의 조명색(Illuminant Color)에 따른 영상 변화를 분석하는 것은 중요하다. 영상 입력 장치는 피사체(Object)를 비추는 조명의 색 특성에 따라 영상을 생성한다. 이는 인간 시각 시스템(Human Visual System)이 가지는 색 불변성(Color Constancy)과는 다른 특성이며, 정확한 색 재현을 위해 필요한 색 실현 모델(Color Appearance Model)이 영상을 변환하는데 문제점으로 작용한다. 따라서, 영상 입력 장치가 생성하는 영상으로부터 조명 정보를 분석하여 인간 시각 시스템의 색 불변성을 재현할 필요가 있다. 본 논문에서는 영상의 조명 정보를 평가하기 위해 채도(Chroma)가 높은 기준 색 샘플들의 rgb 색도를 이용하여 색도 평면에 색도 다각형(Chromaticity Polygon)을 구성하고 영상의 모든 픽셀들의 rgb 색도 분포와 기준 색 샘플들의 색도 다각형간의 포함 관계에 따라 조명 정보를 평가한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.5
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• pp.571-577
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• 1989

In this paper, the double-refractive index profile, which is generalized a trapezoidal index profile, is proposed to study the characteristics of a optical fiber in the wave length 1.55um. Being compared with a triangle profile, it has many advantages over an optimum core radius, the minimum refractive index difference and the change of chromati dispersion characteristics with wavelength. Though the value of a varies from 2 to infinite, the same results are approximately obtained. Therefore, in fabrication of a fiber, the step part of trapezoidal index profile can replace the profile of a-power refractive index. Because the refractive index presented in this paper varies with a, it can be applied to multiple cladding fiber or segmented core fiber. And it is expected that the characteristics of the fiber are obtained.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.97-99
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• 2004

본 논문에서는 다차원 데이터의 유사도 검색을 효과적으로 수행하기 위한 색인 구조를 제안한다. 제안하는 색인 구조는 차원의 저주 현상을 극복하기 위한 벡터 근사 기반의 색인 구조이다. 제안하는 색인 구조는 부모 노드를 기준으로 KDB-트리와 유사한 영역 분할 방식으로 분할하고 분할된 각 영역은 데이터의 분포 특성에 따라 동적 비트를 할당하여 벡터 근사화된 영역을 표현한다. 따라서, 하나의 노드 안에 않은 영역 정보를 저장하여 트리의 깊이를 줄일 수 있다. 또한 다차원의 특징 벡터 공간에 상대적인 비트를 할당하기 때문에 군집화되어 있는 데이터에 대해서 효과적이다 제안하는 색인 구조의 우수성을 보이기 위해 다양한 실험을 통하여 성능의 우수성을 입증한다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.127-129
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• 2000

일반적인 색 정보추출방법으로 색 히스토그램(Color Histogram)은 색의 분포나 응집성, 질감에 대한 구분능력이 없다는 단점을 가지고 있어 정환한 이미지 유사성 비교를 위해 추가적인 정보를 요구한다. Androutsos등은 Haar Wavelet 변환을 통해 이미지의 방향성 질감정보를 구하였다[1]. 하지만 이 방법은 Haar Wavelet 변환의 특성으로 인해 정확한 방향성 정보를 얻을 수 없었다. 본 논문에서는 인접 픽셀(pixel)값의 편차(deviaiton)를 이용하여 방향성 정보를 추출 성능을 향상시키는 방법을 제안하였고, Brodatz 112 질감 이미지와 실재 자연사진을 통해 방향성 질감의 성능을 평가하였다.

• Journal of Naturopathy
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• v.9 no.1
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• pp.1-12
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• 2020

Purposes: The purpose of this study was to investigate the relationship between five viscera, environmental and seasonal changes, and their preference for five colors. Methods: The survey performed using the Chinese medicine constitution and the five cardinal colors (Red, Blue, Yellow, White, Black) paper. Results: The blood types distributions were A, O, B, AB, and others in order. The proportion of smokers(75.6%) and non-drinkers(25.6%). Their color preferences were highest in blue and the lowest in black, but not significant. The proportion of the viscera was stomach(45.3%), liver-lung, intestine, and heart, and the tastes were spicy(48.8%), sweet, sour, salty, and bitter in order. Blue was the highest in all. kidneys(8.3%) preferred red and bitter yellow. The change in mind(35.7%) was highest, followed by scary thoughts and their color preferences varied. The most preferred place was 'cold place,' and 'dry place' was low. The most preferred seasons were 'autumn (38.6%), and the lowest was rainy. And their colors preference were blue. The respondents(89.9%) were normal, 5.7% hypertension and 4.4% hypotension. Healthy people preferred blue (91.8%) and hypertensives yellow with not significant. The diabetics preferred blue and red(p<.04). Conclusions: In the questionnaire survey on the health and physical abnormalities of Seoul citizens, various distributions found in each category, and most of them preferred blue. The results indicated that the questionnaire and the five- colored preference were a little interrelated. However, the results are to be primary data for research in this field.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.30-30
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• 2020

본 연구는 현재 공주대학교 포장에 유전자원으로 보존하는 재래종의 매실나무 열매(청매실)에 대한 활용 가능성의 유익한 정보 및 식품학적 특성과 효능의 기초 정보를 제공하기 위하여 실시하였다. 대한민국 매실나무 집단의 열매(청매실) 무게는 평균 10.5±5.2g이었고, 범위 3.0~35.4g, 변이계수 49.5%를 나타내어 매우 다양한 무게 분포를 보였다. 청매실의 무게별 분포비율은 무게 5.1~10.0g이 50.5%로서 가장 높았고 다음으로 무게 10.1~15.0g이 27.1%, 15.1~20.0g이 10.4%, 5.0g 이하가 8.3% 순이었다. 청매실의 과육색은 백록색이 50.5%로 가장 많았으며, 다음으로 연등색이 36.5%, 등색이 12.5%, 유백색과 암등색이 각각 0.5% 순이었다. 그러나 청매실의 과육색은 황매실 성숙기 및 성숙도에 따라 과피색과 과육색이 다를 것으로 판단되었다. 청매실의 당도는 평균 6.84±0.93이었으며, 범위 4.45~8.76 °Brix로 다양하였다. 당도는 동일한 무게 집단 내에서 개체 자원 간에 다양한 변이를 나타내었으나 무게 집단별 차이를 나타내지 않았으며 변이계수도 유사한 수치이었다.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.329-341
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• 2014

Kernel-based mean-shift object tracking has gained more interests nowadays, with the aid of its feasibility of reliable real-time implementation of object tracking. This algorithm calculates the best mean-shift vector based on the color histogram similarity between target model and target candidate models, where the color histograms are usually produced after uniform color-space quantization for the implementation of real-time tracker. However, when the image of target model has a reduced contrast, such uniform quantization produces the histogram model having large values only for a few histogram bins, resulting in a reduced accuracy of similarity comparison. To solve this problem, a non-uniform quantization algorithm has been proposed, but it is hard to apply to real-time tracking applications due to its high complexity. Therefore, this paper proposes a fast non-uniform color-space quantization method using the histogram equalization, providing an adjusted histogram distribution such that the bins of target model histogram have as many meaningful values as possible. Using the proposed method, the number of bins involved in similarity comparison has been increased, resulting in an enhanced accuracy of the proposed mean-shift tracker. Simulations with various test videos demonstrate the proposed algorithm provides similar or better tracking results to the previous non-uniform quantization scheme with significantly reduced computation complexity.