• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.67-72
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• 2012

In this paper, we present an image enhancement method for atmospherically degraded images using atmospheric light and transmission based on color analysis. We first generate a normalized image using maximum value of each RGB color channel. Then, each atmospheric light is estimated from RGB color channel respectively by calculating reflectance of an image. We also, generate a transmission using gamma coefficients from the Y channel of the image. We can significantly enhance the visibility of an image by using the estimated atmospheric light and the transmission. The proposed algorithm can remove atmospheric degradation components better than existing techniques because the color prevents color distortion which is common problem of existing techniques. Experimental results demonstrate that the proposed algorithm can improve visibility be removing fog, smoke, and dust.

• 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.

• Journal of Power System Engineering
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• v.20 no.4
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• pp.32-37
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• 2016

This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.83-86
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• 1995

Many image display devices allow only a limited number of colors to be simultaneously displayed. In displaying of natural color image using color palette, it is necessary to construct an optimal color palette and map each pixel of the original image to a color palette with fast. In this paper, we proposed the clustering algorithm using local region block centered one color cluster in the prequantized 3-D histogram. Cluster pairs which have the least distortion error are merged by considering distortion measure. The clustering process is continued until to obtain the desired number of colors. Same as the clustering process, original color image is mapped to palette color via a local region block centering around prequantized original color value. The proposed algorithm incorporated with a spatial activity weighting value which is smoothing region. The method produces high quality display images and considerably reduces computation time.

• Journal of the Korean Graphic Arts Communication Society
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• v.25 no.2
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• pp.15-28
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• 2007

For the color offset printing, it starts with input of data. The past days, drum scanner or flat scanner used to input of data, but now it changes gradually into using of digital camera because digital camera become popular. The high quality digital camera saves as a data(RAW format), sRGB which have built in digital camera, or Adobe RGB format. sRGB of ICC(International Color Consortium) profile is a standard color gamut of digital camera. Distribution of color gamut in sRGB is less than Adobe RGB have a distribution in ICC profile. sRGB also can not be expressed in some specific part, because distribution of color gamut in sRGB is not able to cover all parts in ICC Profile of international standards CMYK. It is more popular to use Adobe RGB than sRGB to do high quality offset printing and software basis color setting in Europe and Japan. In spite of this data basis, there is a difficulty of doing color correction between the color proofing prints and the final prints. To see how the software color setting effects to RGB data, this thesis will use Gretag Macbeth ColorChecker 24 patch which has the most general natural color chart to compare sRGB and Adobe RGB to check the differences of color changes. It will use the several factors that came out from the process of making ICC Profile to figure out the optimum In-house profile. It also compares the differences of using matt paper and glossy paper to do best quality color proof offset printing, and how the Rendering Intent effects the proof print.

• Journal of the Korean Graphic Arts Communication Society
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• v.28 no.2
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• pp.117-128
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• 2010

Advancements in digital image have put high quality digital camera into the hands of many image professionals and consumers alike. High quality digital camera images consist originally of raw which have a set of color rendering operation applied to them to produce good images. With color rendering, the raw file was converted to Adobe RGB and sRGB color space. Also color rendering can incorporate factor such as white balance, contrast, saturation. Therefore, in this paper we conduct a study on production of optimum profile considered color rendering in digital camera. To do the experiment, the images were Digital ColorChecker SG target and ColorChecker DC target. A profiling tool was ProfileMaker 5.03. The results were analyzed by comparing in color gamut of $CIEL^*a^*b^*$ color space and calculating ${\Delta}E^*_{ab}$. Also results were analyzed in terms of different $CIEL^*a^*b^*$ color space quadrants based on lightness, chroma.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.42-42
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• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.541-550
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• 1994

A true color image is needed many data on the occasion of the transmission and storage. Therefore, we want to describe color image by a minority data without unreasonableness at eyesight. In this paper, it is presented 256 colormap construction method in RGB, YIQ/YUV space and common colormap expression method at merge between colormaps by reason of dissimilar original color image to display at a monitor for each other colormap at the same time. In comparison with processed result in RGB, YIQ/YUV space, it was measured by PSNR, standard variation, and edge preservation rate using sobel operator. Process time is 3second in colormap construction and 2second in merge between colormaps. In the PSNR value, RGB space has higher 0.15, 0.34 on an average than YIQ and YUV spae. Standard variation has lower in 0.15, 0.41 on an average than Yiq and YUV space. But in the data compression, YIQ/YUV space have about 1/3 compression efficiency than RGB space by reason of use to only 4bit of 8bit in color component.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.628-638
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• 1999

In this paper, we presents a method of retrieving 24 bpp RGB images based on color-spatial features. For each image, it is subdivided into regions by using similarity of color after converting RGB color space to CIE L*u*v* color space that is perceptually uniform. Our segmentation algorithm constrains the size of region because a small region is discardable and a large region is difficult to extract spatial feature. For each region, averaging color and center of region are extracted to construct color-spatial features. During the image retrieval process, the color and spatial features of query are compared with those of the database images using our similarity measure to determine the set of candidate images to be retrieved. We implement a content-based color image retrieval system using the proposed method. The system is able to retrieve images by user graphic or example image query. Experimental results show that Recall/Precision is 0.80/0.84.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.65-70
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• 2013

In this paper, we proposed color image watermarking using human visual system. A watermark is embedded by transforming a color image of RGB coordinate into a color image of HSI coordinate with considering that chromatic components are less sensitive than achromatic components. Watermark is embedded in the frequency domain of the chromatic channels by using discrete cosine transform. Watermark is extracted from watermarked image by using inverse discrete cosine transform. To verify the proposed method, a standard image and a fingerprint image are used for the original image and the watermark image, respectively. Simulation results are satisfied with invisibility and robustness from attacks as image compression.