• Journal of Astronomy and Space Sciences
• /
• v.18 no.1
• /
• pp.7-14
• /
• 2001

Red giant branch shape and the luminosity of horizontal branch on the (V-I)-V CMD are used to derive the metallicity the globular cluster NGC 5053. The metallicities of NGC 5053 derived by SMR method ([Fe/H]=-2.62$\pm$0.07) and the relation between[Fe/H] and $(V-I)_{0.g}$ ([Fe/H]=-2.50) are in good agreement with previously determined values. This result confirms that the morphologies of RGB and HB on the (V-I)-V CMDs can be good indirect photometric metallicity indicators of galactic globular clusters.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.6
• /
• pp.561-566
• /
• 2019

In the Internet space, integrity and security must be maintained for secure and confidential communication, which ensures reliability between sender and receiver. Cryptography is an important factor in maintaining robustness against external attacks. For this purpose, encryption and steganography methods are used. Steganography is a method of hiding confidential information without making statistically significant changes to digital media. I propose a method of transforming the Hangul-Jamo consisting of choseong, jungseong and jongseong, and inserting them into RGB pixel values of the cover image. In order to improve security, a new blending method was used to hide the altered information in the lowest region. In this case, a mixture of LSB and LDR techniques was applied. PSNR was calculated for image quality. The PSNR of the proposed method is 43.225dB, which satisfies the lowest level.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.4
• /
• pp.431-441
• /
• 2016

Since the use of UAV (Unmanned Aerial Vehicle) is convenient for the acquisition of data on broad or inaccessible regions, it is nowadays used to establish spatial information for various fields, such as the environment, ecosystem, forest, or for military purposes. In this study, the process of estimating FVC (Fractional Vegetation Cover), based on multi-spectral UAV, to overcome the limitations of conventional methods is suggested. Hence, we propose that the FVC map is generated by using multi-spectral imaging. First, two types of result classifications were obtained based on RF (Random Forest) using RGB images and NDVI (Normalized Difference Vegetation Index) with RGB images. Then, the result map was reclassified into vegetation and non-vegetation. Finally, an FVC map-based RF were generated by using pixel calculation and FVC map-based GI (Gutman and Ignatov) model were indirectly made by fixed parameters. The method of adding NDVI shows a relatively higher accuracy compared to that of adding only RGB, and in particular, the GI model shows a lower RMSE (Root Mean Square Error) with 0.182 than RF. In this regard, the availability of the GI model which uses only the values of NDVI is higher than that of RF whose accuracy varies according to the results of classification. Our results showed that the GI mode ensures the quality of the FVC if the NDVI maintained at a uniform level. This can be easily achieved by using a UAV, which can provide vegetation data to improve the estimation of FVC.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.4
• /
• pp.27-39
• /
• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• Proceedings of the IEEK Conference
• /
• 2004.08c
• /
• pp.846-849
• /
• 2004

Compared to the bar code which is being widely used for commercial products management, color code is advantageous in both the outlook and the number of combinations. And the color code has application areas complement to the RFID's. However, due to the severe distortion of the color component values, which is easily over $50{\%}$ of the scale, color codes have difficulty in finding applications in the industry. To improve the accuracy of recognition of color codes, it'd better to statistically process an entire color region and then determine its color than to process some samples selected from the region. For this purpose, we suggest a technique to detect edges between color regions in this paper, which is indispensable for an accurate segmentation of color regions. We first transformed RGB color image to HSI and YIQ color models, and then extracted I- and Y-components from them, respectively. Then we performed Canny edge detection on each component image. Each edge image usually had some edges missing. However, since the resulting edge images were complementary, we could obtain an optimal edge image by combining them.

• Proceedings of the Korea Contents Association Conference
• /
• 2004.11a
• /
• pp.321-326
• /
• 2004

Many researchers have been studied texturing the 3D face model with front and side pictures of ordinary person. It is very important to exactly detect the psition of eyes, nose, mouth of a human from the side pictures. Previous results first found the position of eye, nose, or mouth and then extract the other face components using their positional correlation. The detection results greatly depend on the correct extraction of the neck from the images. Therefore, we present a new algorithm that remove the neck completely and thus improve the detection rates of face components. To do this, we will use the RGB values and its differences.

• Journal of Advanced Navigation Technology
• /
• v.20 no.6
• /
• pp.625-630
• /
• 2016

Recently, various types of image contents are being produced, and interest in copyright protection technology is increasing. In this paper, we propose an image encryption technology for color images. This technique divides the image into RGB color components and then performs quad-tree decomposition based on the edge of image. After the quad-tree partitioning, encryption is performed on the selected blocks. Encryption is performed on color components to measure encryption efficiency, and encryption efficiency is measured even after reconstitution into a color image. The encryption efficiency uses a visual measurement method and an objective image quality evaluation method. The PSNR values were measured as 7~10 dB for color difference components and 16~19 dB for color images. The proposed image encryption technology will be used to protect copyright of various digital image contents in the future.

• Geophysics and Geophysical Exploration
• /
• v.6 no.1
• /
• pp.35-39
• /
• 2003

A software to visualize and analyse 3-D seismic data is developed using OpenGL, one of the most popular 3-D graphic library, under the PC and Windows platform. The software can visualize the data as volume and slices, whose color distribution is specified by a special dialog box that can pick a color in RGB or HSV format. The dialog box can also designate opacity values so that several 3-D objects can be displayed superimposed each other. Horizon picking is implemented very easily with this software thanks to the guided picking method. The picked points from a horizon will compose a set of points, mesh, and a surface, which can be viewed and analysed in three dimensions.

• The Journal of the Korea Contents Association
• /
• v.16 no.7
• /
• pp.431-438
• /
• 2016

In this paper, we present an upsampling technique for depth map image using color and depth weights. First, we construct a high-resolution image using the bilinear interpolation technique. Next, we detect a common edge region using RGB color space, HSV color space, and depth image. If an interpolated pixel belongs to the common edge region, we calculate weighting values of color and depth in $3{\times}3$ neighboring pixels and compute the cost value to determine the boundary pixel value. Finally, the pixel value having minimum cost is determined as the pixel value of the high-resolution depth image. Simulation results show that the proposed algorithm achieves good performance in terns of PSNR comparison and subjective visual quality.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.191-196
• /
• 2021

In this paper, we implemented an image sensor module possible of detecting color temperature in an indoor environment. The color temperature information in the video information acquired by the image sensor was matched with a color difference illuminometer to produce an LUT. An algorithm was developed so that color temperature information according to the received RGB values can be automatically calculated. As a result of measuring the color temperature with an image sensor indoors, an accurate result of less than 5.91% was obtained compared to the reference value. It was confirmed that the uniformity of 23.5% or more was excellent compared to the color temperature measurement result using a color sensor.