• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06b
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• pp.83-87
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• 1996

A virtual reality system using video image is designed and implemented. A participant having 2{{{{ { 1} over { 2} }}}}DOF can interact with the computer-generated virtual object using her/his full body posture and gesture in the 3D virtual environment. The system extracts the necessary participant-related information by video-based sensing, and simulates the realistic interaction such as collision detection in the virtual environment. The resulting scene obtained by compositing video image of the participant and virtual environment is updated in near real time.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.451-452
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• 2007

It is need to find a new feature for the more statable recognition system. Now, we need more features like a human eyes. Therefore, this paper proposed a new feature with the stereo camera. In this paper, the each different features from the left and right input image will be extracted by stereo vision system, and will be good for the 3-D recognition.

• Annual Conference of KIPS
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• 2024.10a
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• pp.821-822
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• 2024

This paper describes an automated fruit sorting system using Raspberry Pi and Arduino to classify apples and oranges by freshness, using EfficientNet-B0 for detection. It offers expandability and addresses labor shortages in agriculture through automation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.44-49
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• 2021

Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1453-1463
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• 1994

Recently many robot manipulators are used for various areas of industriesand factories. It has been frequently observed that the robot manipulator fails to complete the function when the object changes its original position, Due to the unexpected impacts and vibrations the center and direction of the object would be shifted in many real application. In this study, a visual sensing algorithm for the robot manipulator is proposed. The algorithm consists of two parts : Detection of the object migration and adjustments of the orobot manipulators Tool Coordinate System. The image filtering technique with visual sensor is applied for the first part of the algorithm. The change of illumination intensity indicates the object migration. Once the object migration is detected, the second part of the algorithm calculates the current position of the object. Then it adjusts the robot manipulators Tool Coordinate System. The robot manipulator and the Visual sensor communicate each other using interrupt technique via proposed algorithm. It has been observed that the proposed algorithm reduces the malfunction of a robot manipulator significantly. Thus it can provide better line balance-up of the manufacturing processes and prevent industrial accidents efficiently.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.738-744
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• 2016

In this paper, a robust visual odometry system has been proposed and implemented in an environment with dynamic illumination. Visual odometry is based on stereo images to estimate the distance to an object. It is very difficult to realize a highly accurate and stable estimation because image quality is highly dependent on the illumination, which is a major disadvantage of visual odometry. Therefore, in order to solve the problem of low performance during the feature detection phase that is caused by illumination variations, it is suggested to determine an optimal threshold value in the image binarization and to use an adaptive threshold value for feature detection. A feature point direction and a magnitude of the motion vector that is not uniform are utilized as the features. The performance of feature detection has been improved by the RANSAC algorithm. As a result, the position of a mobile robot has been estimated using the feature points. The experimental results demonstrated that the proposed approach has superior performance against illumination variations.

• Journal of the Korea Society of Computer and Information
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• v.9 no.1
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• pp.55-62
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• 2004

The computer security is considered important due to the side effect generated from the expansion of computer network and rapid increase of the use of internet. Therefore, Intrusion detection system has been an active research area to reduce the risk from intruders. A number of advantages of using mobile agent computing paradigms have been Proposed. These advantages include : overcoming network latency, reducing network load, executing asynchronously and autonomously, adapting dynamically, and operating in heterogeneous environments. Many information security models have been proposed to mitigate agent-to-agent. agent-to-platform, and platform-to-agent element risks . In these paper, We have an object which is that through intrusion detection system development, the mobile agent is managed and through the analysis of performance data. the best environment is served.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1401-1409
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• 2007

This paper presents a detecting technique for the improvement in quality by appling the various vibrational characteristics theory. The object of this study is to objectively point out faulty gear by developing the program which can be used to analyze and predict the vibrational characteristics caused by gear wear, deformation and nick of auto-transmission. The fault detection methods by vibrational signal analysis of gear have been progressed in the various fields of industry. These methods have the advantage of being easy to attach the accelerometer without discontinuance of the structure. But not all the methods are efficient for finding early faults. So in the thesis, we completed development of the inspection system of vibration by appling the most efficient detecting methods and verified the system's reliability through experiments.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.147-152
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• 2007

Face detection is a crucial part of the face recognition system. It determines the performance of the whole recognition system. Hausdorff distance metric has been used in face detection and recognition with good results. It defines the distance metric based only on the geometric similarity between two sets or points. However, not only the geometry but also the local patterns around the points are available in most cases. In this paper a new Hausdorff distance measure is proposed that makes hybrid use of the similarity of the geometry and the local patterns around the points. Several experiments shows that the new method outperforms the conventional method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1064-1073
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• 2015

In this study, we introduce the pedestrian detection system by using the feature of HOG-PCA and RBFNNs pattern classifier. HOG(Histogram of Oriented Gradient) feature is extracted from input image to identify and recognize a object. And a dimension is reduced for improving performance as well as processing speed by using PCA which is a typical dimensional reduction algorithm. So, the feature of HOG-PCA through the dimensional reduction by using PCA leads to the improvement of the detection rate. FCM clustering algorithm is used instead of gaussian function to apply the characteristic of input data as well and connection weight is used by polynomial expression such as constant, linear, quadratic and modified quadratic. Finally, INRIA person database known as one of the benchmark dataset used for pedestrian detection is applied for the performance evaluation of the proposed classifier. The experimental result of the proposed classifier are compared with those studied by Dalal.