• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.269-282
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• 2021

As the distribution of 3D content such as augmented reality and virtual reality increases, the importance of real-time computer animation technology is increasing. However, the computer animation process consists mostly of manual or marker-attaching motion capture, which requires a very long time for experienced professionals to obtain realistic images. To solve these problems, animation production systems and algorithms based on deep learning model and sensors have recently emerged. Thus, in this paper, we study four methods of implementing natural human movement in deep learning model and kinect camera-based animation production systems. Each method is chosen considering its environmental characteristics and accuracy. The first method uses a Kinect camera. The second method uses a Kinect camera and a calibration algorithm. The third method uses deep learning model. The fourth method uses deep learning model and kinect. Experiments with the proposed method showed that the fourth method of deep learning model and using the Kinect simultaneously showed the best results compared to other methods.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.91-99
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• 2012

In this paper we introduce a quiz application program that digitizes the analogue quiz game. We digitize the quiz components such as quiz proceeding, participants recognition, problem presentation, volunteer recognition who raises his hand first, answer judgement, score addition, winner decision, etc, which are manually performed in the normal quiz game. For automation, we obtained the depth images from the kinect camera which comes into the spotlight recently, so that we located the quiz participants and recognized the user-friendly defined gestures. Analyzing the depth distribution, we detected and segmented the upper body parts and located the hands' areas. Also, we extracted hand features and designed the decision function that classified the hand pose into palm, fist or else, so that a participant can select the example that he wants among presented examples. The implemented quiz application program was tested in real time and showed very satisfactory gesture recognition results.

• Journal of Broadcast Engineering
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• v.14 no.3
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• pp.299-310
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• 2009

In this paper, we proposed a new method to correct the zoom lens distortion for the video sequence captured by the zoom lens. First, we defined the nonlinear zoom lens distortion model which is represented by the focal length and the lens distortion using the characteristic that lens distortion parameters are nonlinearly and monotonically changed while the focal length is increased. Then, we chose some sample images from the video sequence and estimated a focal length and a lens distortion parameter for each sample image. Using these estimated parameters, we were able to optimize the zoom lens distortion model. Once the zoom lens distortion model was obtained, lens distortion parameters of other images were able to be computed as their focal lengths were input. The proposed method has been made experiments with many real images and videos. As a result, accurate distortion parameters were estimated from the zoom lens distortion model and distorted images were well corrected without any visual artifacts.

• 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.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.5
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• pp.307-313
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• 2015

Leak detection in a pipeline usually involves acoustic emission sensors such as contact type sensors. These contact type sensors pose difficulties for installation and cannot operate in areas having high temperature and radiation. Therefore, recently, many researchers have studied the leak detection phenomenon by using a camera. Leak detection by using a camera has the advantages of long distance monitoring and wide area surveillance. However, the conventional leak detection method by using difference images often mistakes the vibration of a structure for a leak. In this paper, we propose a method for steam leakage detection by using the moving average of difference images and histogram analysis. The proposed method can separate the leakage and the vibration of a structure. The working performance of the proposed method is verified by comparing with experimental results.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.83-95
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• 2014

Hand posture recognition is an important technique to enable a natural and familiar interface in HCI(human computer interaction) field. In this paper, we introduce a hand posture recognition method by using a depth camera. Moreover, the hand posture recognition method is incorporated with MPEG-U based advanced user interaction (AUI) interface system, which can provide a natural interface with a variety of devices. The proposed method initially detects positions and lengths of all fingers opened and then it recognizes hand posture from pose of one or two hands and the number of fingers folded when user takes a gesture representing a pattern of AUI data format specified in the MPEG-U part 2. The AUI interface system represents user's hand posture as compliant MPEG-U schema structure. Experimental results show performance of the hand posture recognition and it is verified that the AUI interface system is compatible with the MPEG-U standard.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.133-141
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• 2008

As interactive multimedia have come into wide use, user interfaces such as remote controllers or classical computer mice have several limitations that cause inconvenience. We propose a vision-based pointing device to resolve this problem. We analyzed the moving image from the camera which is embedded in the pointing device and estimate the movement of the device. The pose of the cursor can be determined from this result. To process in the real time, we used the low resolution of $288{\times}208$ pixel camera and comer points of the screen were tracked using local optical flow method. The distance from screen and device was calculated from the size of screen in the image. The proposed device has simple configurations, low cost, easy use, and intuitive handhold operation like traditional mice. Moreover it shows reliable performance even in the dark condition.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.81-89
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• 2013

Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.44-49
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• 2015

When the scene has less texture or when camera pose largely changes, the existing texture-based feature tracking methods are not reliable. Topology-based feature description methods, which use the geometric relationship between features such as LLAH, is a good alternative. However, they require feature detection methods with high performance. As a basic study on developing an effective feature detection method for topology-based feature description, this paper aims at examining their applicability to topology-based feature description by analyzing the repeatability of several feature detection methods that are included in the OpenCV library. Experimental results show that FAST outperforms the others.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1448-1451
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• 2004

Position estimation is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. To compensate the drawbacks, a new localization method that estimates the absolute position of the mobile robot by using a fixed camera on the ceiling in the corridor is proposed. And also, it can improve the success rate for position estimation using the proposed method, which calculates the real size of an object. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data, but a kind of absolute localization. The effectiveness of the proposed localization scheme is demonstrated through the experiments.