• International journal of advanced smart convergence
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• v.9 no.3
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• pp.118-126
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• 2020

In this paper, we present an effective single image fog removal by using the Joint Bright and Dark Channel (JBDC) and pixel-based transmission estimation to enhance the visibility of outdoor images susceptible to degradation due to weather and environmental conditions. The conventional methods include refinement process of coarse transmission with heavy computational complexity. The proposed transmission estimation reveals excellent edge-preserving performance and does not require the refinement process. We estimate the atmospheric light in pixel-based fashion, which can improve the transmission estimation performance and visual quality of the restored image. Moreover, we propose an adaptive transmission estimation to enhance the visual quality specifically in sky regions. Comprehensive experiments on various fog images show that the proposed method exhibits reduced computational complexity and excellent fog removal performance, compared with the existing methods; thus, it can be applied to various fields including real-time devices.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.180-187
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• 2021

In this paper, we present a computational volumetric reconstruction method for three-dimensional (3D) photon counting imaging with enhanced visual quality when low-resolution elemental images are used under photon-starved conditions. In conventional photon counting imaging with low-resolution elemental images, it may be difficult to estimate the 3D scene correctly because of a lack of scene information. In addition, the reconstructed 3D images may be blurred because volumetric computational reconstruction has an averaging effect. In contrast, with our method, the pixels of the elemental image rearrangement technique and a Bayesian approach are used as the reconstruction and estimation methods, respectively. Therefore, our method can enhance the visual quality and estimation accuracy of the reconstructed 3D images because it does not have an averaging effect and uses prior information about the 3D scene. To validate our technique, we performed optical experiments and demonstrated the reconstruction results.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.125-134
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• 2006

The problem of establishing the servo system to reach the desired location keeping all features in the field of view and following a straight line is considered. In addition, robustness of camera calibration parameters is considered in this paper. The proposed approach is based on switching from position-based visual servoing (PBVS) to image-based visual servoing (IBVS) and allows the camera path to follow a straight line. To achieve the objective, a pose estimation method is required; the camera's target pose is estimated from the obtained images without the knowledge of the object. A switched control law moves the camera equipped to a robot end-effector near the desired location following a straight line in Cartesian space and then positions it to the desired pose with robustness to camera calibration error. Finally simulation results show the feasibility of the proposed visual servoing technique.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.4
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• pp.187-194
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• 2015

In this paper, we present an effective visual odometry estimation system to track the real-time pose of a camera moving in 3D space. In order to meet the real-time requirement as well as to make full use of rich information from color and depth images, our system adopts a feature-based sparse odometry estimation method. After matching features extracted from across image frames, it repeats both the additional inlier set refinement and the motion refinement to get more accurate estimate of camera odometry. Moreover, even when the remaining inlier set is not sufficient, our system computes the final odometry estimate in proportion to the size of the inlier set, which improves the tracking success rate greatly. Through experiments with TUM benchmark datasets and implementation of the 3D scene reconstruction application, we confirmed the high performance of the proposed visual odometry estimation method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.1024-1026
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• 2013

DVC (Distributed Video Coding) techniques provides a basic theory for the implementation of low-power video encoder. Conventional methods decide the parity bit request at decoder side. These are effective in controlling the bit-rate, but, are not able to control the visual quality. Thus, this paper presents an effective method of estimating visual quality improved by correcting the virtual channel noise for the reconstructed frame. Through several experiments, it is shown that the proposed method are able to estimate effectively the decoded visual quality.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.545-558
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• 2006

In this paper, PD-like visual servoing is modified in two ways: a sliding-mode observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate the unknown gravity and friction. Based on Lyapunov method and input--to-state stability theory, we prove that PD-like visual servoing with the sliding mode observer and the neuro compensator is robust stable when the gain of the PD controller is bigger than the upper bounds of the uncertainties. Several simulations are presented to support the theory results.

• Physical Therapy Korea
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• v.17 no.3
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• pp.1-10
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• 2010

The purpose of this study was to measure intra-rater and inter-rater reliability and range of motion for measurement of passive shoulder internal rotation range of motion and to compare anterior glide distance of humeral head in three methods. Fifty healthy subjects and fifty patients with shoulder musculoskeletal pain were recruited for this study. The subjects' passive shoulder internal rotation range of motion was measured by visual estimation, manual stabilization, and pressure biofeedback unit methods. In two trials, measurements were performed on each subject by two examiners. Intraclass correlation coefficient (ICC(3,1)) was used to determine the reliability of each measurement. The intra-rater reliability of the three methods was excellent (ICC=.77~.93) in both groups. The inter-rater reliability of the visual estimation method was poor (ICC=.20, .29), the manual scapular stabilization method was poor and fair (ICC=.09, .50), and the pressure biofeedback unit method was excellent (ICC .86, .75) in the experimental and control groups. In the experimental group, the difference of examined range of motion by each examiner was significant in the visual estimation method and manual scapular stabilization method, but there was an insignificant difference between the groups is the pressure biofeedback unit method. This result suggests that the intra-rater and inter-rater reliability of a pressure biofeedback unit was better than the other methods. The difference in distance of the anterior glide of humeral head was insignificant among all the methods. The pressure biofeedback unit method was the most reliable method, so it is proposed to be a new and reliable method to measure internal rotation range of motion.

• Journal of Power System Engineering
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• v.6 no.1
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• pp.96-101
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• 2002

In this paper, we address the problem of controlling an end-effector to track and grab a moving target using the visual servoing technique. A visual servo mechanism based on the image-based servoing principle, is proposed by using visual feedback to control an end-effector without calibrated robot and camera models. Firstly, we consider the control problem as a nonlinear least squares optimization and update the joint angles through the Taylor Series Expansion. And to track a moving target in real time, the Jacobian estimation scheme(Dynamic Broyden's Method) is used to estimate the combined robot and image Jacobian. Using this algorithm, we can drive the objective function value to a neighborhood of zero. To show the effectiveness of the proposed algorithm, simulation results for a six degree of freedom robot are presented.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.121-124
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• 2001

For efficient transmission of 3DTV video signals, it is necessary to eliminate the inherent redundancy between the stereo image pairs. Though disparity estimation provides a powerful tool for eliminating the redundancy, it is very time consuming. This paper presents a novel disparity estimation scheme based on the human visual property. The disparity vectors of image blocks spatially adjacent to the current block are used as initial guesses fur the disparity vector of the current block. In addition, mixed-resolution coding is applied to reduce the computational complexity of disparity estimation. Through computer simulations on a stereoscopic sequence we show that the proposed method gives rise .to visually pleasing results with much reduced computational complexity.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.403-410
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• 2016

This paper proposes a robust image-based visual servoing scheme using a nonlinear observer for a monocular eye-in-hand manipulator. The proposed control method is divided into a range estimation phase and a target-tracking phase. In the range estimation phase, the range from the camera to the target is estimated under the non-moving target condition to solve the uncertainty of an interaction matrix. Then, in the target-tracking phase, the feature point uncertainty caused by the unknown motion of the target is estimated and feature point errors converge sufficiently near to zero through compensation for the feature point uncertainty.