• Journal of the Korean earth science society
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• v.28 no.1
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• pp.87-104
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• 2007

As part of the SPVS (Short-Period Variability Survey) which is a wide-field $(90'{\times}60')$ photometric monitering program at Bohyunsan Optical Astronomy (BOAO), we performed V band time-series CCD photometric observations ofthe young open cluster NGC 129 for 11 nights between October 12, 2004 and November 3, 2005 using the 155mm refractor equipped with $3K{\times}2K$ CCD camera. From the observation we obtained 2400 V band CCD frames and color-magnitude diagram of the cluster. To transform instrumental magnitude to standard magnitude, we applied ensemble normalization technique to all observed time-series data. After the photometric reduction process, we examined variations of 9537 stars. As a result, sixty six of the new variable stars were discovered. To determine the periods of the sevariables, we used DFT(Discrete Fourier Transform) and phase-matching technique. According to light curve shape, period, amplitude and the position on a C-M diagram, we classified these variables as 9 SPB type, 9 ${\delta}$ Scuti type, 29 eclipsing, 17 long term variables. However, two of them were not classified. From this study, we learned that small telescopes could be a very useful tool to observe variable stars in the open cluster in survey program.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.40-48
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• 2012

This paper models the hue shift phenomenon and proposes a hue correction method to give perceptual matching between projector with and without additional white channel. To quantify the hue shift phenomenon for whole hue angle, 24 color patches with the same lightness are frist created along equally-spaced hue angle, and these are displayed one by one both displays with different luminance levels. Next, each hue value of the patches appeared on the projector with additional white channel is adjusted by observers until the hue values of patches on both displays appear the same visually. After obtaining the hue shift values from the color matching experiment, these values are piecewise fit into six polynomial functions, which approximately determine shifted hue amounts for an arbitrary hue values of each pixel in projector with additional white channel and are utilized to correct them. Actually, an input RGB image is converted to CIELAB LCH color space to get hue values of each pixel and this hue value is shifted as much as the amount calculated by the functions of hue shift model for correction. Finally, corrected image is inversely converted to an output RGB image. For an evaluation, the matching experiment with several test images and the z-score comparisons were performed.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.267-274
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• 2004

In this paper, the interesting moving objects tracking algorithm using color information on Multi-Video camera is proposed Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area, after converting RGB color coordination of image which is input from multi-video camera into HSI color coordination. Hue information of the detected moving area are normalized by 24 steps from 0$^{\circ}$ to 360$^{\circ}$ It is used for the feature parameters of the moving objects that three normalization levels with the highest distribution and distance among three normalization levels after obtaining a hue distribution chart of the normalized moving objects. Moving objects identity among four cameras is distinguished with distribution of three normalization levels and distance among three normalization levels, and then the moving objects are tracked and surveilled. To examine propriety of the proposed method, four cameras are set up indoor difference places, humans are targeted for moving objects. As surveillance results of the interesting human, hue distribution chart variation of the detected Interesting human at each camera in under 10%, and it is confirmed that the interesting human is tracked and surveilled by using feature parameters at four cameras, automatically.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.1-8
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• 2019

In this paper, we propose an improved facial skin color extraction method to solve the problem that facial surface is lost due to shadow or illumination in skin color extraction process and skin color extraction is not possible. In the conventional HSV method, when facial surface is brightly illuminated by light, the skin color component is lost in the skin color extraction process, so that a loss area appears on the face surface. In order to solve these problems, we extract the skin color, determine the elements in the H channel value range of the skin color in the HSV color space among the lost skin elements, and combine the coordinates of the lost part with the coordinates of the original image, To minimize the number of In the face detection process, the face was detected using the LBP Cascade Classifier, which represents texture feature information in the extracted skin color image. Experimental results show that the proposed method improves the detection rate and accuracy by 5.8% and 9.6%, respectively, compared with conventional RGB and HSV skin color extraction and face detection using the LBP cascade classifier method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.553-560
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• 2020

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

• Science of Emotion and Sensibility
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• v.9 no.spc3
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• pp.187-196
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• 2006

This study has showed verified results on 'Survey into Observer Preference for Table Decoration for Eating and Drinking'. It is a preliminary study to look into interrelation of preference to color tables, image adjectives, aesthetic value, by giving color changes in a certain regularity to each influential factor in functional table decoration. The result has demonstrated that, as for color image and preference,'observers prefers mid/low dark-toned brown which gives a feeling of 'high-quality, and like to eat', and orange tone with a fooling of 'warm and colorful'. In evaluating interrelation between preference and aesthetic value, the study has revealed that ordinary people take the influential factor of 'unity and Gestalt' seriously, while non-professionals preferred 'uniqueness'. Stimulant No. 7 is one of constituent factors of functional table decoration, eliciting significance of images on colors ant aesthetic value. This study also has practical problems about insufficiency or inadequateness in producing mood suitable to T P O, according to depending upon color changes in picking up stimulants, or other problems in selecting stimulants, according to time, seasons and generational parameter in which the inquiry is conducted, but it will serve as a stepping stone for developing new design using both qualitative and quantitative data in 려nctional table decoration.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1054-1062
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• 2022

The population of Korea's solar salt-producing regions is rapidly aging, resulting in a decrease in the number of productive workers. In solar salt production, salt collection is the most labor-intensive operation because existing salt collection vehicles require human operators. Therefore, we intend to develop an unmanned solar salt collection vehicle to reduce manpower requirements. The unmanned solar salt collection vehicle is designed to identify the salt collection status and location in the salt plate via color detection, the color detection performance is a crucial consideration. Therefore, an image processing algorithm was developed to improve color detection performance. The algorithm generates an around-view image by using resizing, rotation, and perspective transformation of the input image, set the RoI to transform only the corresponding area to the HSV color model, and detects the color area through an AND operation. The detected color area was expanded and noise removed using morphological operations, and the area of the detection region was calculated using contour and image moment. The calculated area is compared with the set area to determine the location case of the collection vehicle within the salt plate. The performance was evaluated by comparing the calculated area of the final detected color to which the algorithm was applied and the area of the detected color in each step of the algorithm. It was confirmed that the color detection performance is improved by at least 25-99% for salt detection, at least 44-68% for red color, and an average of 7% for blue and an average of 15% for green. The proposed approach is well-suited to the operation of unmanned solar salt collection vehicles.

• Journal of the Korea Computer Graphics Society
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• v.14 no.3
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• pp.1-9
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• 2008

This paper presents a novel optimization-based saliency-preserving method for converting color images to grayscale in a manner consistent with conventional approaches of black-and-white photographers. In black-and-white photography, a colored filter called a contrast filter has been commonly employed on a camera to lighten or darken selected colors. In addition, local exposure controls such as dodging and burning techniques are typically employed in the darkroom process to change the exposure of local areas within the print without affecting the overall exposure. Our method seeks a digital version of a conventional contrast filter to preserve visually-important image features. Furthermore, conventional burning and dodging techniques are addressed, together with image similarity weights, to give edge-aware local exposure control over the image space. Our method can be efficiently optimized on GPU. According to the experiments, CUDA implementation enables 1 megapixel color images to be converted to grayscale at interactive frames rates.

• Journal of Astronomy and Space Sciences
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• v.25 no.2
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• pp.87-100
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• 2008

A total of 792 CCD images of V523 Cas were obtained on four nights of Jan. 2003 with the bvri CCD photometric system attached to a 61cm reflector of Sobaeksan Optical Astronomy Observatory (SOAO). The 17 standard stars in the images were used to establish transformation relations between our bvri system and the standard Johnson-Cousins BVRI system. We derived the tentative equations of transformation between two photometric systems as follows; V=v-0.0689(B-V)+0.0063, B-V=1.3197(b-v)-0.1733, V-R=0.9210(v-r)-0.1309, R-I=0.8892(r-i)-0.1055. Using these equations standard V magnitudes and their color indexes (B-V, V-R, R-I) for 57 stars in the field of the image were determined.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.459-465
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• 2013

This paper is a study on an object extraction method which using color features of an object and background in the image. A human recognizes an object through the color difference of object and background in the image. So we must to emphasize the color's difference that apply to extraction result in this image. Therefore, we have converted to HSV color images which similar to human visual system from original RGB images, and have created two each other images that applied Median Filter and we merged two Median filtered images. And we have applied the Mean Shift algorithm which a data clustering method for clustering color features. Finally, we have normalized 3 image channels to 1 image channel for binarization process. And we have created object map through the binarization which using average value of whole pixels as a threshold. Then, have extracted major object from original image use that object map.