• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.715-721
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• 2000

In this research, rule and neuro net based boundary extraction algorithm was developed. Extracting boundary of the interest, lean tissue, is essential for the quality evaluation of the beef based on color machine vision. Major quality features of the beef are size, marveling state of the lean tissue, color of the fat, and thickness of back fat. To evaluate the beef quality, extracting of loin parts from the sectional image of beef rib is crucial and the first step. Since its boundary is not clear and very difficult to trace, neural network model was developed to isolate loin parts from the entire image input. At the stage of training network, normalized color image data was used. Model reference of boundary was determined by binary feature extraction algorithm using R(red) channel. And 100 sub-images(selected from maximum extended boundary rectangle 11${\times}$11 masks) were used as training data set. Each mask has information on the curvature of boundary. The basic rule in boundary extraction is the adaptation of the known curvature of the boundary. The structured model reference and neural net based boundary extraction algorithm was developed and implemented to the beef image and results were analyzed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.2
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• pp.16-22
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• 2010

This paper proposes the real-time moving object tracking system UAV using color information. Case of object tracking, it have studied to recognizing the moving object or moving multiple objects on the fixed camera. And it has recognized the object in the complex background environment. But, this paper implements the moving object tracking system using the pan/tilt function of the camera after the object's region extraction. To do this tracking system, firstly, it detects the moving object of RGB/HSI color model and obtains the object coordination in acquired image using the compact boundary box. Secondly, the camera origin coordination aligns to object's top&left coordination in compact boundary box. And it tracks the moving object using the pan/tilt function of camera. It is implemented by the Labview 8.6 and NI Vision Builder AI of National Instrument co. It shows the good performance of camera trace in laboratory environment.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1528-1535
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• 2011

We present a fog generation algorithm in 2D images. The proposed algorithm provides a scattering model for the approximated calculation of fog density. The scattering model needs parameters of distance and altitude information. However, 2D images do not include that information, so that we calculate them from the depth information generated in an interactive manner, and estimate the scattering factor by using the scattering model. Then we generate fog effect on an input image using the scattering factor by distance-oriented selection blur and color blending. With the algorithm, we can easily create the fog-effected images and fog generated animation from 2D images.

• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1699-1708
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• 2021

Response surface methodology was applied to determine the optimum extract conditions(extract temperature and time) for the high-quality Agakong beverage. The optimal roasting condition for Agakong was set at 250 ℃ for 30 minutes. As quality criteria of Agakong, pH, color values and isofavone contents with extract temperature and extract time, the probability value (p<0.01) demonstrated a high significance for the regression model. It was found that the higher the extraction temperature and the longer the extraction time, the higher the isoflavones content. The optimized conditions of extraction isoflavones from agakong were found to be optimized ratio of extraction temperature 99.5℃, extraction time 1.7 h and the maximum rutin yield was 10.63 ㎍/mL.

• Korean Journal of Food Science and Technology
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• v.17 no.3
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• pp.208-212
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• 1985

The residence time distributions were measured experimentally to determine the effect of reverse screw elements at various screw configurations in twin screw extruder. A simple model was used to estimate the number of filled C-chamber on the forwarding screw. The inclusion of reverse-screw elements resulted in the increase of median residence time and the broadening of color distribution. True residence time was affected by using the reverse screw elements just before the die. The number of filled C-chamber was decreased with the increase of screw revolution speed at the same screw configuration.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.373-386
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• 2013

In this research, we specify the image consumers' preferred image quality ranges based on objective image quality evaluation factors and follow a method which measures preference of the natural image scenes. In other words, according to No-Reference, we select dynamic range, color, and contrast as factors of image quality measurements. For collecting sample images, we choose the preferred 200 landscapes which have over 30 recommendations by image consumers on the internet photo gallery. According to the scores of three objective factors of image quality measurements, the final expected score which means the image quality preference is measured and its total score is 100 points. In the main test, the actual image sample shows dynamic range 10 stop, LAB mean value L:54.7, A:2.96, B:-15.84, and RSC contrast 376.9. Total 200 image samples' normal distribution z value represents in dynamic range 0.21, LAB mean value L:0.15, A:0.38, B:0.13, and RSC contrast 0.08. In the standard normal distribution table, we can convert the z value as a percentage; dynamic range is 8.32%, LAB mean value is L:5.96%, A:14.8%, B:5.17%, and RSC contrast is 3.19%. And then, we convert the percentage values into the scores of 100; dynamic range is 91.68, LAB mean value is 91.36, and RSC contrast is 96.81. Therefore, we can conclude that the sample image's total mean score is 94.99 based on three objective image quality factors. Throughout our proposed image quality assessment model, we can measure the preference value of natural scenes. Also, we can specify the preferred image quality representation ranges and measure the expected image quality preference.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.1-7
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• 2020

The secure access the lighting, Heating, ventilation, and air conditioning (HVAC), fire safety, and security control boxes of building facilities is the primary objective of future smart buildings. This paper proposes an authorized user access to the electrical, lighting, fire safety, and security control boxes in the smart building, by using color grid coded optical camera communication (OCC) with face recognition Technologies. The existing CCTV subsystem can be used as the face recognition security subsystem for the proposed approach. At the same time a smart device attached camera can used as an OCC receiver of color grid code for user access authentication data sent by the control boxes to proceed authorization. This proposed approach allows increasing an authorization control reliability and highly secured authentication on accessing building facility infrastructure. The result of color grid code sequence received by the unauthorized person and his face identification allows getting good results in security and gaining effectiveness of accessing building facility infrastructure. The proposed concept uses the encoded user access authentication information through control box monitor and the smart device application which detect and decode the color grid coded informations combinations and then send user through the smart building network to building management system for authentication verification in combination with the facial features that gives a high protection level. The proposed concept is implemented on testbed model and experiment results verified for the secured user authentication in real-time.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.318-324
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• 2000

Ultrasonic and magnetic resonance imaging systems are used to visualize the interior states of biological objects. These nondestructive methods have many advantages but too much expensive. And they do not give exact color information and may miss some details. If it is allowed to destruct some biological objects to get the interior and exterior information, constructing 3D image from the series of the sliced sectional images gives more useful information with relatively low cost. In this paper, PC based automatic 3D model generator was developed. The system was composed of three modules. One is the object handling and image acquisition module, which feeds and slices objects sequentially and maintains the paraffin cool to be in solid state and captures the sectional image consecutively. The second is the system control and interface module, which controls actuators for feeding, slicing, and image capturing. And the last is the image processing and visualization module, which processes a series of acquired sectional images and generates 3D graphic model. The handling module was composed of the gripper, which grasps and feeds the object and the cutting device, which cuts the object by moving cutting edge forward and backward. Sliced sectional images were acquired and saved in the form of bitmap file. The 3D model was generated to obtain the volumetric information using these 2D sectional image files after being segmented from the background paraffin. Once 3-D model was constructed on the computer, user could manipulate it with various transformation methods such as translation, rotation, scaling including arbitrary sectional view.

• Restorative Dentistry and Endodontics
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• v.32 no.1
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• pp.19-27
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• 2007

The purpose of this study is to develope new dental color-space system. Twelve kinds of dental composites and one kind of dental porcelain were used in this study. Disk samples (15 mm in diameter, 4 mm in thickness) of used materials were made and sample's CIE $L^*a^*b^*$ value was measured by Spectrocolorimeter (MiniScan XE plus, Model 4000S, diffuse/$8^{\circ}$ viewing mode, 14.3 mm Port diameters, Hunter Lab USA) The range of measured color distribution was analyzed. All the data were applied in the form of T### which is expression unit in CNU Cons Dental Color Chart. The value of $L^*$ lies between 80.40 and 52.70. The value of $a^*$ are between 10.60 and 3.60 and $b^*$ are between 28.40 and 2.21. The average value of $L^*$ is 67.40, and median value is 67.30. The value of $a^*$ are 2.89 and 2.91 respectively. And for the $b^*$, 14.30 and 13.90 were obtained. The data were converted to T### that is the unit count system in CNU-Cons Dental Color Chart. The value of $L^*$ is converted in the first digit of the numbering system. Each unit is 2.0 measured values. The second digit is the value of $a^*$ and is converted new number by 1.0 measured value. For the third digit $b^*$ is replaced and it is 2.0 measured unit apart. T555 was set to the value of $L^*$ ranging from 66.0 to 68.0, value of $a^*$ ranging from 3 to 4 and $b^*$ value ranging from 14 to 16.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4491-4509
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• 2017

The present paper proposes a novel dynamic system for hand gesture recognition. The approach involved is comprised of three main steps: detection, tracking and recognition. First, the gesture contour captured by a 2D-camera is detected by combining the three-frame difference method and skin-color elliptic boundary model. Then, the trajectory of the hand gesture is extracted via a gesture-tracking algorithm based on an occlusion-direction oriented linear extrapolation predictor, where the gesture coordinate in next frame is predicted by the judgment of current occlusion direction. Finally, to overcome the interference of insignificant trajectory segments, the longest common subsequence (LCS) is employed with the aid of velocity information. Besides, to tackle the subgesture problem, i.e., some gestures may also be a part of others, the most probable gesture category is identified through comparison of the relative LCS length of each gesture, i.e., the proportion between the LCS length and the total length of each template, rather than the length of LCS for each gesture. The gesture dataset for system performance test contains digits ranged from 0 to 9, and experimental results demonstrate the robustness and effectiveness of the proposed approach.