• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1010-1012
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• 2017

본 논문에서는 HSV, YCbCr 컬러 모델의 색상정보를 통한 화재 검출 알고리즘을 제안한다. 첫 번째 단계에서는 영상의 변화를 감지하기 위해서 입력된 영상으로부터 평균배경영상을 계산하여 전경영상을 분리한다. 그리고 차영상을 이용해 움직임을 인식하여 컬러 모델 색상정보를 비교할 영역을 구한다. 전경영상의 구해진 영역에서 컬러모델의 복합 색상정보를 이용하여 화재 영역을 검출한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4472-4477
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• 2013

This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.128-136
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• 2006

The stippling techniques, which represent objects with numerous points using pen and ink. The previous stippling techniques for Non-Photorsealistc Rendering(NPR) use single-colored points to represent the tone of gray image ur the material of surface. This paper presents a new stippling technique with various colored points based on the analysis of color information. By using the color information of the input image on HSV model, we define the color weight function that allows to determine automatically the number and size of points. The color jittering based on Munsell's color model can generate stippling drawings using various colored points to represent the image. Our color stippling method is expected to be used in many areas such as animation, digital art, video processing and CG tool.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.176-190
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• 2002

According as vehicles increases, system such as Advanced Drivers Assistance System(ADAS ) to inform forward situation to driver is required. In this paper, we proposes method to detect forward vehicles and lane from sequential color images by basis process to inform forward situation to driver. We detect a front vehicle using that shadow area exists on part under vehicles and that road area occupies many parts even if road traffic is confused. We detect lane information using that lane part is white order by reverse characteristic of shadow area. This method shows good result in case road is confused or there is direction indication to road. HSV color space is selected for color modeling. This method uses saturation component and value component in HSV color model to detect vehicles and lane. It uses statistics features of HSV component and position to know whether detected vehicles area is vehicles such as vehicles previous frame. To verify the effects of the proposed method, we capture the road images with notebook and CCD camera for PC and Present the results such as processing time, accuracy and vehicles detection against the images.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.39-48
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• 2006

This paper is proposed the wavelet morphology algorithm with the spatial auto-object segmentation concept and the clustering concept. When it is segmented the color face by using the proposed algorithm, it is made to the simple image. Also, it is used the spatial quality in order to segment and detect the image as a real time without the user's manufacturing. This removed a small part that is regarded as a noise in image by HSV color model and applied the wavelet morphology to remove a part excepting for the face image. In this paper, it is made a comparison between the wavelet morphology algorithm and the morphology algorithm. And It is showed to accurately detect the face object parts in the image appled to HSV color space model.

• Journal of Satellite, Information and Communications
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• v.8 no.3
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• pp.52-57
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• 2013

This paper describes the color comparison analysis of flame in each standard color model in order to propose the optimal color model for image processing based flame detection algorithm. Histogram intersection values were used to analyze the separation characteristics between color of flame and color of non-flame in each standard color model which are RGB, YCbCr, CIE Lab, HSV. Histogram intersection value in each color model and components is evaluated for objective comparison. The analyzed result shows that YCbCr color model is the most suitable for flame detection by average HI value of 0.0575. Among the 12 components of standard color models, each Cb, R, Cr component has respectively HI value of 0.0433, 0.0526, 0.0567 and they have shown the best flame separation characteristics.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.105-110
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• 2001

차량이 증가함에 따라서 첨단 교통 시스템(intelligent Transportation System: ITS)은 교통의 효율성, 신뢰성, 안정성 향상에 중점을 두게되었다. 첨단 교통 시스템의 일부분인 운전자 도움 시스템(Advanced Drivers Assistance System)은 운전을 하고 있는 상황에서 도움을 주기 위한 체계이고, 전방의 장애물 검지는 운전자 도움 시스템에서 전방의 상황을 운전자에게 알려주기 위한 중요한 요소이다. 본 논문에서는 HSV 컬러모델을 이용하여 연속된 컬러 영상으로부터 도로상의 차선과 방향 표시자에 구애받지 않고 전방의 차량을 검출하는 방법을 제안한다. HSV 컬러 모텔에서 차량을 검출하기 위해서는 태도(Saturation)와 명도(Value)성분의 관계를 이용하여 차량 영역을 구하고, HSV성분과 위치 특징을 이용하여 이전 프레임의 차량인지 검증한다. 도로 영상에서 차량이 있는 경우 차량의 아래 부분에 그림자 영역이 존재한다는 점을 이용한다. 제안된 방식의 효과를 검증하기 위해 노트북 PC와 PC용 CCD 카메라로 도로에서의 영상을 촬영하고 차량검출알고리즘을 적용한 처리 시간, 정확도, 차량검지 등의 결과를 보인다.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1641-1648
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• 2017

The main purpose of this paper is to compare the performances of various filters with color images to remove the noise. Furthermore, we suggest a modified de-noising process by the transformation of color model from RGB to another color models, such as HSV and $YC_BC_R$, to improve the quality of de-noising methods encompassing Median, Wiener, and Mean filters. Neither the performance comparison of the de-noising filters with color images nor the converting the color model for better de-noise on the degraded images haven't been performed before. Inspired to make improvements, we conduct experiments with new de-noising process on color images. The result of the experiments is shown that it could assist on certain filters being more reliable techniques.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.3037-3047
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• 2000

This paper describes a method of tracking a close leading vehicle by color image processing using the pairs of tail and brake lights. which emit red light and are housed on the rear of the vehicle in stop-and-go traffic condition. In the color image converted as an HSV color model. candidate regions of rear lights are identified using the color features of a pair of lights. Then. the pair of tailor brake lights are detected by means of the geometrical features and location features for the pattern of the tail and brake lights. The location of the leading vehicle can be estimated by the location of the detected lights and the vehicle can be tracked continuously. It is also possible to detect the braking status of the leading vehicle by measuring the change in HSV color components of the pair of lights detected. In the experiment. this method tracked a leading vehicle successfully from urban road images and was more useful at night than in the daylight. The KAV-Ill (Korea Autonomous Vehicle- Ill) equipped with a color vision system implementing this algorithm was able to follow a leading vehicle autonomously at speeds of up to 15km!h on a paved road at night. This method might be useful for developing an LSA (Low Speed Automation) system that can relieve driver's stress in the stop-and-go traffic conditions encountered on urban roads.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.533-534
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• 2011

본 논문에서 적외선(IR; Infrared), 자외선(UV; Ultraviolet), 가시광선(VR; Visible ray)의 영역에서 촬영한 설진 영상으로부터 가장 효과적인 설태 인식을 위한 최적 혀 영상 분석 방법을 제안한다. 제안한 방법은 설진에서 혀 영상 촬영을 위한 최적 파장 범위와 해당 파장에서 설태 분석에 최적의 컬러 영상을 선정한다. 최적 영상 선정을 위해서는 각 파장별로 촬영한 혀 영상을 LAB, HSV, YcBcR, RGB 컬러모델로 변환하고, 변환된 영상들로부터 설태와 비설태 영역의 히스토그램(Histogram)을 분석에 의해서 영역-분별력을 측정한다. 실험 결과 설진에서 설태 인식을 위한 최적 혀 영상은 자외선 영역에서의 RGB 컬러모델로 나타났다.