• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.119-126
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• 2013

In this paper, we propose a chromatic aberration removal algorithm in image capture devices, which considers local properties of the image. Chromatic aberration is generated by the fact that the refractive index of the lens is different for different wavelengths, which produces color artifacts on strong edge due to misalignment of RGB channels. Under the characteristics of the artifacts, the proposed algorithm first estimates the regions with the apparent color artifacts as the neighborhoods of the strong edge. In the regions, the proposed algorithm removes the color artifacts by matching the edges of RGB channels. The widely used conventional methods based on global image warping could not remove the color artifacts of longitudinal chromatic aberration and purple fringing identified by the image sensor, whereas the matching process of the proposed method could reduce them. Experimental results show that the proposed algorithm outperforms the conventional methods on objective and subjective criteria.

• Korean Journal of Acupuncture
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• v.28 no.1
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• pp.53-60
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• 2011

Objectives : Cupping therapy has been used as various treatment methods in oriental medicine clinic for a long time. Specially, Cupping stimulation caused skin hyperpigmentation which has been used as diagnosis method for extravasated blood & inner organs functional disease. But it was difficult to quantitatively measure and analyze the interrelation between extravasated blood and skin hyperpigmentation. The aim of this study is to measure the acupoint's RGB and melanin change pattern by stimulating under fixed 80kPa negative pressure and to evaluate the extravasated blood. Methods : To evaluate extravasated blood, 10 acupoints (left/right BL13, BL15, BL18, BL20, BL23) were stimulated by treatment 80kPa negative pressure for 1 minute. And then we measured the acupoints' skin color change patterns using the color meter, followed by RGB analysis and melanin change pattern. Results and Conclusions : We could observe the correlation of the acupoints' skin color condition related with RGB change pattern. All acupoints' color change degree were different. Regardless of acupoints' color degree due to extravasated blood, we observed in common the fact that RGB values decreased after stimulation and steadily increased according to time. We observed that melanin index increased after stimulation and recovered before stimulation-condition after the lapse of time. In other words, we confirmed the correlation between raised color change degree and $L^*a^*$ index change pattern. Therefore we observed the possibility of charting the $L^*a^*$ index following extravasated blood.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.9
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• pp.769-774
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• 2016

Epoxy polymer exposed to solar radiation on long-term is required to a long-term environmental test in order to ensure the reliability of operation performance. In this paper, the solar aging experiment is performed for 7, 14, 28, 56, and 84 cycles, using solar simulator designed according to MIL-STD-810. After the aging experiment, measuring RGB values and using the CIE1976 color space, each RGB decreasing rates and total color shift are calculated. In addition, using a universal testing machine, mechnical properties according to ASTM-D638 are measured. As a result, by increasing solar aging period, total color shift is increased, and the tensile strength and elongation are decreased, but the elastic modulus and the poisson's ratio are slightly changed.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.686-689
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• 2018

3D 손 포즈 추정(Hand Pose Estimation, HPE)은 스마트 인간 컴퓨터 인터페이스를 위해서 중요한 기술이다. 이 연구에서는 딥러닝 방법을 기반으로 하여 단일 RGB-Depth 카메라로 촬영한 양손의 3D 손 자세를 실시간으로 인식하는 손 포즈 추정 시스템을 제시한다. 손 포즈 추정 시스템은 4단계로 구성된다. 첫째, Skin Detection 및 Depth cutting 알고리즘을 사용하여 양손을 RGB와 깊이 영상에서 감지하고 추출한다. 둘째, Convolutional Neural Network(CNN) Classifier는 오른손과 왼손을 구별하는데 사용된다. CNN Classifier 는 3개의 convolution layer와 2개의 Fully-Connected Layer로 구성되어 있으며, 추출된 깊이 영상을 입력으로 사용한다. 셋째, 학습된 CNN regressor는 추출된 왼쪽 및 오른쪽 손의 깊이 영상에서 손 관절을 추정하기 위해 다수의 Convolutional Layers, Pooling Layers, Fully Connected Layers로 구성된다. CNN classifier와 regressor는 22,000개 깊이 영상 데이터셋으로 학습된다. 마지막으로, 각 손의 3D 손 자세는 추정된 손 관절 정보로부터 재구성된다. 테스트 결과, CNN classifier는 오른쪽 손과 왼쪽 손을 96.9%의 정확도로 구별할 수 있으며, CNN regressor는 형균 8.48mm의 오차 범위로 3D 손 관절 정보를 추정할 수 있다. 본 연구에서 제안하는 손 포즈 추정 시스템은 가상 현실(virtual reality, VR), 증강 현실(Augmented Reality, AR) 및 융합 현실 (Mixed Reality, MR) 응용 프로그램을 포함한 다양한 응용 분야에서 사용할 수 있다.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.06b
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• pp.227-230
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• 2006

The $St{\ddot{o}}ckigt$ sizing test of the most-commonly used sizing tests is easily influenced by the individual testers' bias in recognizing red coloration. Therefore the test had to be modified to improve its reliability and reproducibility by automated recognition of a coloration procedure during testing. In order to achieve this, all measured variables occurring during the $St{\ddot{o}}ckigt$ test was first be analyzed and then reflected in the new automatic system. Secondly, the most important principle applied was to transform the RGB values of the droplet image to hue (H), saturation (S) and value (V) respectively. This is because RGB cannot be used as a color standard, owing to RGB's peculiarity of being seriously affected by the observer's point of view. Therefore, the droplet color had to be separated into three distinct factors, namely the HSV values, in order to allow linear analysis of the droplet color. When the average values of the vectors calculated during color variation from yellow to brown were plotted against time, it was possible to determine the vector value of hue, the most sensitive factor among HSV, at the specific time by differentiation of a function when it exceeds the critical point. Then, the specific time consumed up to the critical point was regarded as the $St{\ddot{o}}ckigt$ sizing degree. The conventional method took more time to recognize an ending point of coloration than the automatic method, and in addition the error ranges of the conventional sizing degrees on the specific addition points of AKD were wider than those of the automatic method.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1057-1062
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• 2014

This paper proposes a tip-over detection method for a mobile robot using a support inscribed circle defined as an inscribed circle of a support polygon. A support polygon defined by the contact points between the robot and the terrain is often used to analyze the tip-over. For a robot moving on uneven terrain, if the intersection between the extended line of gravity from the robot's COG and the terrain is inside the support polygon, tip-over will not occur. On the contrary, if the intersection is outside, tip-over will occur. The terrain is detected by using an RGB-D sensor. The terrain is locally modeled as a plane, and thus the normal vector can be obtained at each point on the terrain. The support polygon and the terrain's normal vector are used to detect tip-over. However, tip-over cannot be detected in advance since the support polygon is determined depending on the orientation of the robot. Thus, the support polygon is approximated as its inscribed circle to detect the tip-over regardless of the robot's orientation. To verify the effectiveness of the proposed method, the experiments are carried out using a 4-wheeled robot, ERP-42, with the Xtion RGB-D sensor.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.65-84
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• 2006

In this paper, we present an improved image formation model and propose a color image enhancement based on the model. In the presented image formation model, an input image is represented as a product of global illumination, local illumination, and reflectance. In the proposed color image enhancement, an input RGB color image is converted into an HSV color image. Under the assumption of white-light illumination, the H and S component images are remained as they are and the V component image only is enhanced based on the image formation model. The global illumination is estimated by applying a linear LPF with wide support region to the input V component image and the local illumination by applying a JND (just noticeable difference)-based nonlinear LPF with narrow support region to the processed image, where the estimated global illumination is eliminated from the input V component image. The reflectance is estimated by dividing the input V component image by the estimated global and local illuminations. After performing the gamma correction on the three estimated components, the output V component image is obtained from their product. Histogram modeling is next executed such that the final output V component image is obtained. Finally an output RGB color image is obtained from the H and S component images of the input color image and the final output V component image. Experimental results for the test image DB built with color images downloaded from NASA homepage and MPEG-7 CCD color images show that the proposed method gives output color images of very well-increased global and local contrast without halo effect and color shift.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.328-335
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• 2022

With the advance of robot capabilities and functionalities, construction robots assisting construction workers have been increasingly deployed on construction sites to improve safety, efficiency and productivity. For close-proximity human-robot collaboration in construction sites, robots need to be aware of the context, especially construction worker's behavior, in real-time to avoid collision with workers. To recognize human behavior, most previous studies obtained 3D human poses using a single camera or an RGB-depth (RGB-D) camera. However, single-camera detection has limitations such as occlusions, detection failure, and sensor malfunction, and an RGB-D camera may suffer from interference from lighting conditions and surface material. To address these issues, this study proposes a novel method of 3D human pose estimation by extracting 2D location of each joint from multiple images captured at the same time from different viewpoints, fusing each joint's 2D locations, and estimating the 3D joint location. For higher accuracy, the probabilistic representation is used to extract the 2D location of the joints, considering each joint location extracted from images as a noisy partial observation. Then, this study estimates the 3D human pose by fusing the probabilistic 2D joint locations to maximize the likelihood. The proposed method was evaluated in both simulation and laboratory settings, and the results demonstrated the accuracy of estimation and the feasibility in practice. This study contributes to ensuring human safety in close-proximity human-robot collaboration by providing a novel method of 3D human pose estimation.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.461-472
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• 2005

In this paper, we propose face feature extraction algorithm through stereo image's matching value. The proposed algorithm detected face region by change the RGB color space of skin color information to the YCbCr color space. Applying eye-template from extracted face region geometrical feature vector of feature about distance and lean, nose and mouth between eye extracted. And, Proposed method could do feature of eyes, nose and mouth through stereo image's matching as well as 2D feature information extract. In the experiment, the proposed algorithm shows the consistency rate of 73% in distance within about 1m and the consistency rate of 52%in distance since about 1m.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.363-366
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• 2003

We propose the system that automatically adjusts the white balance on display products to a standard value according to several nations. We replace manual or semi-auto method with fully automatic method using windows application program. And we use RS-232C serial interface to communicate PC with display products which we want to adjust white balance. The PC generates patterns for measuring color information and Color Analyzer measures color and brightness. This value is transmitted through RS-232C serial interface to PC. The PC's algorithm analyzes this information and then decides which RGB Gain value is best for optimal white balance. This RGB Gain value is transmitted through RS-232C serial interface to display products. The modified color value is measured again and feed back to PC. This sequence is repeated until optimum white balance is obtained.