• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.4
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• pp.840-866
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• 2014

Visibility in underwater images is usually poor because of the attenuation of light in the water that causes low contrast and color variation. In this paper, a new approach for underwater image quality improvement is presented. The proposed method aims to improve underwater image contrast, increase image details, and reduce noise by applying a new method of using contrast stretching to produce two different images with different contrasts. The proposed method integrates the modification of the image histogram in two main color models, RGB and HSV. The histograms of the color channel in the RGB color model are modified and remapped to follow the Rayleigh distribution within certain ranges. The image is then converted to the HSV color model, and the S and V components are modified within a certain limit. Qualitative and quantitative analyses indicate that the proposed method outperforms other state-of-the-art methods in terms of contrast, details, and noise reduction. The image color also shows much improvement.

• Current Optics and Photonics
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• v.6 no.3
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• pp.304-312
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• 2022

Digital images of cultural relics captured using line scan cameras present limitations due to uneven intensity and low contrast. To address this issue, this report proposes a colour linear array image enhancement method that can maintain a constant colour. First, the colour linear array image is converted from the red-green-blue (RGB) colour space into the hue-saturation-intensity colour space, and the three components of hue, saturation, and intensity are separated. Subsequently, the hue and saturation components are held constant while the intensity component is processed using the established intensity compensation model to eliminate the uneven intensity of the image. On this basis, the contrast of the intensity component is enhanced using an improved local contrast enhancement method. Finally, the processed image is converted into the RGB colour space. The experimental results indicate that the proposed method can significantly improve the visual effect of colour linear array images. Moreover, the objective quality evaluation parameters are improved compared to those determined using existing methods.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.9-18
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• 2013

In order to measure the parameters of consumers' preferred image quality, this research suggests image quality assessment factors; dynamic range, color, and contrast. They have both physical image quality factors and psychological characteristics from the previous researches. We found out the specific ranges of preferred image quality metrics. As a result, Digital Zone System meant for dynamic range generally shows 6~10 stop ranges in portrait, nightscape, and landscape. Total RGB mean values represent in portrait (67.2~215.2), nightscape (46~142), and landscape (52~185). Portrait total RGB averages have the widest range, landscape, and nightscape, respectively. Total scene contrast ranges show in portrait (196~589), nightscape (131~575), and landscape (104~767). Especially in portrait, skin tone RGB mean values are in ZONE V as the exposure standard, but practically image consumers' preferred skin tone level is in ZONE IV. Also, total scene versus main subject contrast ratio represents 1:1.2; therefore, we conclude that image consumers prefer the out-of-focus effect in portrait. Throughout this research, we can measure the preferred image quality metrics ranges. Also, we expect the practical and specific dynamic range, color, and contrast information of preferred image quality to positively influence product development.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.314-321
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• 2022

Depth information used in SLAM and visual odometry is essential in robotics. Depth information often obtained from sensors or learned by networks. While learning-based methods have gained popularity, they are mostly limited to RGB images. However, the limitation of RGB images occurs in visually derailed environments. Thermal cameras are in the spotlight as a way to solve these problems. Unlike RGB images, thermal images reliably perceive the environment regardless of the illumination variance but show lacking contrast and texture. This low contrast in the thermal image prohibits an algorithm from effectively learning the underlying scene details. To tackle these challenges, we propose multi-channel remapping for contrast. Our method allows a learning-based depth prediction model to have an accurate depth prediction even in low light conditions. We validate the feasibility and show that our multi-channel remapping method outperforms the existing methods both visually and quantitatively over our dataset.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.623-626
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• 2003

Image analysis was performed with two color systems, Red-Green-Blue (RGB) values and normalized Hue-Saturation-Intensity (HSI). We conducted field studies in Cheongju to determine canopy spectral reflectance and digital image analysis of rice. Spectral reflectance measurements made with a portable spectrometer(LI-1800) correlated with growing stage and digital images for rice. Images in which the color was specified by the common RGB coordinates could be used when there was a sharp contrast between the color of the rice and that of the field soil. In the absence of sharp contrast, identification of the rice covered area was much easer after the color had been transformed into HSI coordinates. This study introduced fundamental theories in digital image analysis and applied that for field situations rice.

• Korean Journal of Bronchoesophagology
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• v.18 no.2
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• pp.45-48
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• 2012

Background Neoplastic vessels tend to proliferate on the surface of malignant lesions in the aerodigestive tract. So, superficial malignant lesions can be detected earlier by enhancing mucosal vascular clarity. To enhance mucosal vascular clarity on endoscopic image, we developed an image processing algorithm of RGB (red-green-blue) channel substitution image (CSI). Methods Each pixel in original white light image (WLI) has its own value of red, green and blue channel. Various combinations of RGB channel substitution was tried on original WLI. Results To make superficial blood vessels darker than brighter background mucosa, in the CSI algorithm, RGB value in each pixel of WLI is substituted; red value to green one, green value to blue one. There was a good contrast between superficial mucosal vessels and background brighter mucosa in the CSI image. Conclusion By RGB CSI algorithm, WLI could be successfully converted to new images with enhanced mucosal vascular clarity. Using RGB CSI algorithm could provide added vascular visibility on original WLI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.616-621
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• 2005

In this paper. digital watermarking algorithm based on human visual system and transform domain is presented. Firstly, original image is separated into RGB thannels, watermark is embedded into the coefficients of DCT so as to consider a contrast sensitivity and texture degrees. In preprocessing, DCT domain based transform is involved and binary image of visually recognizable patterns is utilized as a watermark. Consequently, experimental results showed that proposed algorithm is robust and imperceptibility such destruction attack as JPEG compression.

• 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 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.