• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.173-174
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• 2009

Color harmony will look good formative elements of the basic lines, shapes and colors. In this paper, we present the implementation of Johannes Itten's color balance. Yellow, red, blue, on the basis of the first color mixing between the index and second color is placed. 12 color balance made by placing a third color between the first color and second color. we recognize this 12 color balance. Edge is detected using the Canny edge operator and labeling and clustering was expressed through the colors.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.152-154
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• 2010

본 연구에서는 내용 기반의 텍스타일 이미지 검색 시스템을 구축하였다. 텍스타일 이미지에 대한 색상, 질감, 모양 특성 추출 조합을 각각 혹은 가중치를 이용한 방법으로 검색한다. 검색은 사용자 인터페이스에서 오라클 데이터베이스 시스템에서 제공되는 이미지의 색상, 질감 특성 값에 대한 검색과 결과 피드백을 보면서 진행된다. 또 검색 대상 이미지들을 유사도에 따라 다차원 비례법에 의하여 2차원 화면에 클러스터링하여 전체 이미지의 군집화 특성을 쉽게 파악할 수 있는 기능을 구현하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.415-420
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• 2013

In recent, many researches have been studying sensitivity and psychology of human on color. Among them, a picture of children can be a tool to represent their emotion. Information of colors and direction on a child's picture often represent his internal psychological states unconsciously. In this paper, we propose the method that extract the color and direction information in order to analyze the psychology in the picture of children. Histogram clustering is used for color information detection. Direction information extract from inner edge value. In the result of experiments, we shows that our method is similar to the pattern classification of the general method.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.15-18
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• 2001

동영상에서 움직이는 객체 검출은 동영상의 내용을 표현하고 유사한 동영상을 검색하는 데 있어 중요한 특징간을 추출하는 방법으로 사용된다. 그러나 복잡하게 카메라가 움직이는 동영상에서 움직이는 객체 검출은 아직까지 어려운 과제이다. 본 논문에서는 복잡한 카메라의 움직임이 있는 환경에서 움직이는 객체를 강인하게 검출하는 방법을 제안한다. 움직이는 객체 검출 방법은 입력 영상을 색상간의 클러스터링을 이용하여 각 영역으로 구분하는 Mean Shift 알고리즘과 인접한 프레임에서 구분된 영역을 대응시켜 영역의 모션 벡터를 구하는 영역 매칭, 유사한 궤적을 가지는 영역들의 클러스터링을 이용하여 객체를 검출하는 궤적 클러스터링 알고리즘을 사용한다. 제안한 영역 기반 알고리즘은 기존의 픽셀이나 블록 기반의 방법보다 움직이는 객체를 정확하게 검출하였다. 실험 결과 복잡하게 움직이는 카메라의 환경 속에서 움직이는 객체를 강인하게 검출하였다.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.12
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• pp.560-567
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• 2008

In this paper, we discuss a simple and straightforward binarization procedure which can generate black/white comics from the video frame image. Generally, the region of human's skin is colored white or light gray, while the dark region is filled with the irregular but regular patterns like hatching in most of the black/white comics. Note that it is not enough for simple threshold method to perform this work. Our procedure is decoupled into four processes. First, we use bilateral filter to suppress noise color variation and reserve boundaries. Then, we perform mean-shift segmentation for each similar colored pixels to be clustered. Third, the clustered regions are merged and extended by our region extension algorithm considering each color of their regions. Finally, we decide which pixels are on or off using by our dynamic binarization method based on the HSV color model. Our novel black/white cartooning procedure was so successful to render comic cuts from a well-known cinema in a resonable time and manual intervention.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.6
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• pp.649-656
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• 2014

In this paper, we address the problem of center lane detection algorithm for autonomous driving. Color information for center lane is gathered by analyzing a row line color distribution of road in front of a vehicle. The candidate pixels for center lane are extracted from the histogram of road colors. Morphological filtering and clustering process are applied to the candidate pixels to extract the exact center lane. We predict a expected area of center lane and search only the regions in subsequent frames, that reduces the time required for center lane detection.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.403-407
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• 2005

자궁 경부 세포진 영상의 핵 영역 분할은 자궁 경부암 자동화 검색 시스템의 가장 어렵고도 중요한 분야로 알려져 있다. 본 논문에서는 자궁 경부 세포진 영상에서 HSI 모델을 이용하여 세포진 핵 영역을 추출한다. 추출된 세포진 핵 영역은 형태학적 정보(morphometric feature)와 명암 정보(densitometric feature), 색상 정보(colorimetric feature), 질감 정보(textural features)를 분석하여 핵의 특징을 추출한다. 또한 Bethesda System에서의 분류 기준에 따라 핵의 분류 기준을 정하고 추출된 핵의 특징들을 퍼지 c-Means 클러스터링 알고리즘에 적용하여 실험한 결과, 제안된 방법이 자궁 세포진 핵 추출과 인식에 있어서 효율적임을 확인하였다.