• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.537-544
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• 2006

Image Search is one of the most exciting and fast growing research areas in the filed of multimedia technology. This paper conducts an empirical evaluation of color descriptor that uses the information of color distribution in color images, which is the most basic element for image search. With the experimental results, we observe that in the top 10% of precision, HSV, Daubechies 9/7 and 2 level decomposition have little better than others. Also histogram quadratic metrics outperform the Minkowski form distance metrics in similarity measurements, but spend more than 20 in computational times.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.55-62
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• 2013

This paper aims to detect Speed Bump by using Omni-directional Camera and to suggest Real-time update scheme of Speed Bump through Vision Based Approach. In order to detect Speed Bump from sequence of camera images, noise should be removed as well as spot estimated as shape and pattern for speed bump should be detected first. Now that speed bump has a regular form of white and yellow area, we extracted speed bump on the road by applying erosion and dilation morphological operations and by using the HSV color model. By collecting huge panoramic images from the camera, we are able to detect the target object and to calculate the distance through GPS log data. Last but not least, we evaluated accuracy of obtained result and detection algorithm by implementing SLAMS (Simultaneous Localization and Mapping system).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.65-84
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• 2006

In this paper, we present an improved image formation model and propose a color image enhancement based on the model. In the presented image formation model, an input image is represented as a product of global illumination, local illumination, and reflectance. In the proposed color image enhancement, an input RGB color image is converted into an HSV color image. Under the assumption of white-light illumination, the H and S component images are remained as they are and the V component image only is enhanced based on the image formation model. The global illumination is estimated by applying a linear LPF with wide support region to the input V component image and the local illumination by applying a JND (just noticeable difference)-based nonlinear LPF with narrow support region to the processed image, where the estimated global illumination is eliminated from the input V component image. The reflectance is estimated by dividing the input V component image by the estimated global and local illuminations. After performing the gamma correction on the three estimated components, the output V component image is obtained from their product. Histogram modeling is next executed such that the final output V component image is obtained. Finally an output RGB color image is obtained from the H and S component images of the input color image and the final output V component image. Experimental results for the test image DB built with color images downloaded from NASA homepage and MPEG-7 CCD color images show that the proposed method gives output color images of very well-increased global and local contrast without halo effect and color shift.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.10-18
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• 2009

Color rendition method is necessary for improving the low contrast images which are achieved by PDA, mobile phone camera or PC camera. There are some methods for color rendition. However, after correcting the color, image quality degradations, such as graying-out, halo-artifact and color noise, may occur. In order to overcome these problems, this paper proposes a retinex-based color rendition method. The proposed method uses the HSV color coordinate system to avoid the graying-out, and the advanced image formation model to reduce the halo-artifact in which the image is divided into three components as the global illumination, the local illumination, and reflectance. The experiment results show that the proposed method yields better performance of color correction over the conveniently method.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.133-137
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• 2001

본 논문에서는 해프톤 영상의 칼라 분포 특성을 이용하여 해프톤 영상과 비해프톤 영상을 분류하는 방법에 대해 연구하였다. 이를 위해 스캐닝한 RGB 해프톤 영상을 HSV 칼라 모델로 변환한 후, Hue에 대한 히스토그램에서 해프톤 영상이 갖는 고유한 칼라분포 특성을 분석하였다. 일반적으로 해프톤 영상에서는 인쇄시 사용된 칼라들로 인해 특정 Hue 값을 갖는 픽셀의 빈도가 비해프톤 영상과 달리 피크(peak) 형태로 높게 나타나는 특성이 있다. 본 논문에서는 이러한 특성을 고려하여 해프톤 영상과 비해프톤 영상을 구분할 수 있는 분류 방법을 개발하였으며, 실험을 통해 그 타당성을 검토하였다. 제안한 방법은 내용기반 검색시스템에서의 영상분류과정과 문서영상분할 분야 등에서 활용할 수 있다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.397-398
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• 2006

본 논문에서는 이미지 검색을 위해 가장 기본적인 요소인 이미지 색상에 따른 칼라 분포정보를 이용하고 다양한 요소에 따라 가중치를 부여한 칼라기반의 검색 기술자를 제안하였고 실험적 평가를 통하여 제안 기술자의 성능을 평가하였다. 칼라 히스토그램을 통한 이미지 검색 기술자를 설계하는데 있어 칼라모델은 HSV, 웨이블릿 변환 필터는 D9/7, 웨이블릿 분해는 2 레벨을 적용하였을 때 가장 좋은 검색효율성을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.6
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• pp.15-24
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• 2011

As increase awareness of intelligent vehicle systems, many kinds of lane detection algorithm have been proposed. General boundary extraction method can bring good result in detection of lane on the road. But a shadow on the road, or other boundaries, such as horizontal lines can be detected. The method using morphological operations was used to extract information about Lane. By applying HSV color model for color information of lane, the candidate of the lane can be extracted. In this paper, the lane detection region was set by Hough transformation using the candidate of the lane. By extracting lane markings on the lane detection region, lane detection method can bring good result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.473-480
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• 2009

This paper is to enhance a color image running in the PXA255 ARM processor based on embedded linux environments. Retinex is one of the representative algorithm for image enhancement in the previous research. However, retinex is not suitable the run on the embedded system because of its long processing time. So, we proposed the image enhancement algorithm for embedded system, with less quantity of operation and the effect equivalent to retinex. To achieve this goal, we propose and implement the image enhancement algorithm, which utilizes the image formation model and gamma correction to be effective in a back-light and dark image. The proposed algorithm converts the color space from RGB to HSV, and then V and S channels are processed. In order to optimize the proposed method in the PXA255 ARM processor, quantity of calculation is reduced. The performance of the proposed algorithm was evaluated through qualitative method and quantitative method. The results show that brightness and contrast are improved with less quantity of operation.