• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.126.1-126
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• 2001

Navigation in unknown environments, where the robot has no exact geometric information in advance, requires the robot to obtain the destination positions without a map. The utilization of model-based object recognition would be a solution, where the robot can estimate the destination positions from geometric relationships between the recognized objects and the robot. This paper presents a robot System for this kind of navigation, in Which the robot navigates itself to the room designated by room number. Object recognition technique is used to find a door and character recognition is utilized to interpret the room number on the number plate near the door and to determine whether it is the destination or not. The robot has ...

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.170-173
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• 2001

To apply stereo vision system to obstacle recognition system of AGV, we constructed algorithm of stereo matching and distance measuring with stereo image for positioning of object in area. And using this system, we look into the error between real position and measured position, and studied relationship of compensation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.767-771
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• 2003

In this study, stereo vision system is applied to a mobile manipulator for effective tasks. The robot can recognize a target and compute the position of the target using a stereo vision system. While a monocular vision system needs properties such as geometric shape of a target, a stereo vision system enables the robot to find the position of a target without additional information. Many algorithms have been studied and developed for an object recognition. However, most of these approaches have a disadvantage of the complexity of computations and they are inadequate for real-time visual servoing. However, color information is useful for simple recognition in real-time visual servoing. In this paper, we refer to about object recognition using colors, stereo matching method, recovery of 3D space and the visual servoing.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.553-556
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• 2005

We present a vision-based hand gesture recognition system for object manipulation in virtual space. Most conventional hand gesture recognition systems utilize a simpler method for hand detection such as background subtractions with assumed static observation conditions and those methods are not robust against camera motions, illumination changes, and so on. Therefore, we propose a statistical method to recognize and detect hand regions in images using geometrical structures. Also, Our hand tracking system employs multiple cameras to reduce occlusion problems and non-synchronous multiple observations enhance system scalability. Experimental results show the effectiveness of our method.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.223-240
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• 1998

The purpose of this paper is to develop the pattern recognition system with a 'Robust' concept to be applicable to flexible manufacture automation in practice. The 'Robust' concept has four meanings as follows. First, pattern recognition is performed invariantly in case the object to be recognized is translated, scaled, and rotated. Second, it must have strong resistance against noise. Third, the completely learned system is adjusted flexibly regardless of new objects being added. Finally, it has to recognize objects fast. To develop the proposed system, contouring, spectral analysis and Fuzzy ART neural network are used in this study. Contouring and spectral analysis are used in preprocessing stage, and Fuzzy ART is used in object classification stage. Fuzzy ART is an unsupervised neural network for solving the stability-plasticity dilemma.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.892-895
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• 1992

This paper presents a methodology for automatic feature extraction used in a vision system of FMC (flexible Manufacturing Cell). To implement a robot vision system, it is important to make a feature database for object recognition, location, and orientation. For industrial applications, it is necessary to extract feature information from CAD database since the detail information about an object is described in CAD data. Generally, CAD description is three dimensional information but single image data from camera is two dimensional information. Because of this dimensiional difference, many problems arise. Our primary concern in this study is to convert three dimensional data into two dimensional data and to extract some features from them and store them into the feature database. Secondary concern is to construct feature selecting system that can be used for part recognition in a given set of objects.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.5
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• pp.411-417
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• 2005

In this study, stereo vision system is applied to a mobile manipulator for effective tasks. The robot can recognize a target and compute the potion of the target using a stereo vision system. While a monocular vision system needs properties such as geometric shape of a target, a stereo vision system enables the robot to find the position of a target without additional information. Many algorithms have been studied and developed for an object recognition. However, most of these approaches have a disadvantage of the complexity of computations and they are inadequate for real-time visual servoing. Color information is useful for simple recognition in real-time visual servoing. This paper addresses object recognition using colors, stereo matching method to reduce its calculation time, recovery of 3D space and the visual servoing.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.46-50
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• 2016

Recently, Depending on expectancy effect and ripple effect of augmented reality, the convergence between augmented reality and culture & arts are being actively conducted. This paper proposes a learning method for effective object tracking in 3D storytelling augmented reality in cultural properties. The proposed system is based on marker-less tracking, and there are four modules that are recognition, tracking, detecting and learning module. Recognition module is composed of SURF and LSH, and then this module generates standard object information. Tracking module tracks an object using object tracking based on reliability. This information is stored in Learning module along with learned time information. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. Also, it proposes a method for robustly implementing a 3D storytelling augmented reality in cultural properties in the future.