• 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 Korea Multimedia Society
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• v.12 no.7
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• pp.947-952
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• 2009

In this paper, we propose a method of generating color sketch images using the DIP operator. In the proposed method, an input RGB color image is first transformed into an HSV color image. A sketch image of the V component image is then extracted by applying the DIP operator to the V component image, which is the brightness component of the input image. For the visual convenience, the extracted sketch image of the V component image is next inverted and contrast-stretched. The S component image is also enhanced to deepen the color of output sketch image while maintaining its color. Finally, the V and S component images along with the original H component image are transformed into an output RGB color sketch image. Experimental results show that the proposed method yields output color sketch images similar to hand-drawn sketch pictures whose colors are the same as those of input color images.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.372-379
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• 2013

In this paper, we proposed a content-based image retrieval algorithm using local binary patterns and HSV color histogram. Images are retrieved using image input in image retrieval system. Many researches are based on global feature distribution such as color, texture and shape. These techniques decrease the retrieval performance in images which contained background the large amount of image. To overcome this drawback, the proposed method extract background fast and emphasize the feature of object by shrinking the background. The proposed method uses HSV color histogram and Local Binary Patterns. We also extract the Local Binary Patterns in quantized Hue domain. Experimental results show that the proposed method 82% precision using Corel 1000 database.

• 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.

• 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 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.

• 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.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.480-483
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• 2005

This study has proposed a method of content-based image retrieval in order to overcome disadvantages of color histogram. The existing histogram method has a weak point that reduces accuracy because of quantization error, and more. In order to solve this, we convert color information to HSV and quantize Hue factor being net color information and calculate histogram and then use this for retrieval feature that is robust in brightness, movement, and rotation. As a result of experimenting, the method proposed has showed better precision than the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1358-1364
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• 2002

Presently, Multimedia data are increasing suddenly in broadcasting and internet fields. For retrieval of still images in multimedia database, content-based image retrieval system is implemented in this paper that user can retrieve similar objects from image database after choosing a wanted query region of object. As to extract color features from query image, we transform color to HSV with proposed method that similarity is obtained it through histogram intersection with database images after making histogram. Also, query image is transformed to gray image and induced to wavelet transformation by which spatial gray distribution and texture features are extracted using banded autocorrelogram and GLCM before having similarity values. And final similarity values is determined by adding two similarity values. In that, weight value is applied to each similarity value. We make up for defects by taking color image features but also gray image features from query image. Elevations of recall and precision are verified in experiment results.