• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.66-72
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• 2006

In face recognition, learning speed of face is very important since the system should be trained again whenever the size of dataset increases. In existing methods, training time increases rapidly with the increase of data, which leads to the difficulty of training with a large dataset. To overcome this problem, we propose SVDD (Support Vector Domain Description)-based learning method that can learn a dataset of face rapidly and incrementally. In experimental results, we show that the training speed of the proposed method is much faster than those of other methods. Moreover, it is shown that our face recognition system can improve the accuracy gradually by learning faces incrementally at real environments with illumination changes.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.558-561
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• 2009

In this paper, we propose Simple Frame Marker(SFMarker) to support recognition of characters and images included in a marker in augmented reality. If characters are inserted inside of marker and are recognised using Optical Character Recognition(OCR), it doesn't need marker learning process before an execution. It also reduces visual disturbance compared to 2D barcode marker due to familarity of characters. Therefore, proposed SFMarker distinguishes Square SFMarker that embeds images from Rectangle SFMarker with characters according to ratio of marker and applies different recognition algorithms. Also, in order to reduce preprocessing of character recognition, SFMarker inserts direction information in border of marker and extracts it to execute character recognition fast and correctly. Finally, since the character recognition for every frame slows down tracking speed, we increase the speed of recognition process using the result of character recognition in previous frame when frame difference is low.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.90.1-90
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• 2002

Counters for the various kinds of bank notes require high-speed distinctive point extraction and recognition for notes. In this paper we propose a new point extraction and recognition algorithm for bank notes. For distinctive point extraction we use a coordinate data extraction method from specific parts of a bank note representing the same color. The recognition algorithm uses a back-propagation neural network that has coordinate data input. The proposed algorithm is designed to minimize recognition time.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.510-515
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• 2001

Generally, there are (1)Words for conversation (2)Tone (3)Pitch (4)Formant frequency (5)Speech speed, etc as the element for emotional recognition from speech signal. For human being, it is natural that the tone, vice quality, speed words are easier elements rather than frequency to perceive other s feeling. Therefore, the former things are important elements fro classifying feelings. And, previous methods have mainly used the former thins but using formant is good for implementing as machine. Thus. our final goal of this research is to implement an emotional recognition system based on pitch, formant, speech speed, etc. from speech signal. In this paper, as first stage we foun specific features of feeling angry from his words when a man got angry.

• Journal of the Korean Society for Railway
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• v.14 no.2
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• pp.116-120
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• 2011

For the train positioning system currently applied in high-speed MAGLEV train systems, it is classified into absolute positioning systems which discontinuously detect train positions, and relative positioning systems which continuously detect train positions. In this paper we analyze the configuration model and the numerical model of the absolute positioning system applied in TRANSRAPID which is a representative high-speed MAGLEV train, and design the two configuration models specific to the recognition structures of absolute positioning systems. We also verify the compatibility of the design models of absolute positioning system through simulation using MATLAB and propose the optimal configuration model of absolute positioning systems for high-speed MAGLEV train system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• Journal of the Korea Computer Industry Society
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• v.2 no.6
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• pp.867-874
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• 2001

In this paper, considering the pronunciation characteristic of Korean, recognition units which can shorten the recognition time and reflect the coarticulation effect simultaneously are suggested. These units are composed of monophone and hipbone ones. Monophone units are applied to the vowels which represent stable characteristic. Biphones are used to the consonant which vary according to adjacent vowel. In the experiment of word recognition of PBW445 database, the compound units result in comparable recognition accuracy with 57％ speed up compared with triphone units and better recognition accuracy with similar speed. In addition, we can reduce the memory size because of fewer units.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.33-39
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• 2014

Automatic driving needs many functions such as the obstacle recognition, the lane recognition, and the lane change, etc. In this paper, we realized a system which automatically drove the three-kinds of vehicle driving course, to introduce and apply the concept of 'color recognition' that expands the scope of 'lane recognition' for vehicle driving. We made the reduced each course compared with RC(Radio Control) car size, and controlled the steering considering the position and the slope of the detection line and the speed. Because the RC car does not have the brake function, we consider the speed and the position of the detection line to stop the RC car.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.19-26
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• 2022

With the recent development of intelligent transportation systems, car license plate recognition systems are being used in various fields. Such systems need to guarantee real-time performance to recognize the license plate of a driving car. Also, they should keep a high recognition rate even in problematic situations such as small license plates in low-resolution and unclear image due to distortion. In this paper, we propose a real-time car license plate recognition system that improved processing speed using object detection algorithm based on anchor-free method and text recognition algorithm based on Convolutional Neural Network(CNN). In addition, we used Spatial Transformer Network to increase the recognition rate on the low resolution or distorted images. We confirm that the proposed system is faster than previously existing car license plate recognition systems and maintains a high recognition rate in a variety of environment and quality images because the proposed system's recognition rate is 93.769% and the processing speed per image is about 0.006 seconds.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1799-1802
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• 2003

This paper has been studied a Adaptive feature-factors based fingerprints recognition in many biometrics. we study preprocessing and matching method of fingerprints image in various circumstances by using optical fingerprint input device. The Fingerprint Recognition Technology had many development until now. But, There is yet many point which the accuracy improves with operation speed in the side. First of all we study fingerprint classification to reduce existing preprocessing step and then extract a Feature-factors with direction information in fingerprint image. Also in the paper, we consider minimization of noise for effective fingerprint recognition system.