• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1431-1434
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• 2005

In the steel production line, the molten metal of a furnace is transformed into billet and then moves to the heating furnace of the hot rolling mill. This paper describes about the development of recognition system for the characters, which was marked at the billet material by use template-marking plate and hand written method, in the steel plant. For the recognition of template-marked characters, we propose PSVM algorithm. And for the recognition of hand written character, we propose combination methods of CCD algorithm and PSVM algorithm. The PSVM algorithm need some more time than the conventional KLT or SVM algorithm. The CCD algorithm makes shorter classification time than the PSVM algorithm and good for the classification of closed curve characters from Arabic numerals. For the confirmation of algorithm, we have compared our algorithm with conventional methods such as KLT classifier and one-to-one SVM. The recognition rate of experimented billet characters shows that the proposing PSVM algorithm is 97 % for the template-marked characters and combinational algorithm of CCD & PSVM is 95.5 % for the hand written characters. The experimental results show that our proposing method has higher recognition rate than that of the conventional methods for the template-marked characters and hand written characters. By using our algorithm, we have installed real time character recognition system at the billet processing line of the steel-iron plant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6063-6079
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• 2019

When the length of sampling data sequence is too large, the method of gesture recognition based on traditional Dynamic Time Warping (DTW) algorithm will lead to too long calculation time, and the accuracy of recognition result is not high.Support vector machine (SVM) has some shortcomings in precision, Edit Distance on Real Sequences(EDR) algorithm does not guarantee that noise suppression will not suppress effective data.A new method based on Improved Interpolation Dynamic Time Warping (IIDTW)algorithm is proposed to improve the efficiency of gesture recognition and the accuracy of gesture recognition. The results show that the computational efficiency of IIDTW algorithm is more than twice that of SVM-DTW algorithm, the error acceptance rate is FAR reduced by 0.01%, and the error rejection rate FRR is reduced by 0.5%.Gesture recognition based on IIDTW algorithm can achieve better recognition status. If it is applied to unlock mobile phone, it is expected to become a new generation of unlock mode.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.887-890
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• 2012

Image recognition is one of the most important research directions of pattern recognition. Image based road automatic identification technology is widely used in current society, the intelligence has become the trend of the times. This paper studied the image segmentation algorithm theory and its application in road signs recognition system. With the help of image processing technique, respectively, on road signs automatic recognition algorithm of three main parts, namely, image segmentation, character segmentation, image and character recognition, made a systematic study and algorithm. The experimental results show that: the image segmentation algorithm to establish road signs recognition model, can make effective use of smart phone system and application.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.69-73
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• 2017

In this paper, we developed a real-time image recognition algorithm based on machine learning and tested the performance of the algorithm. The real-time image recognition algorithm recognizes the input image in real-time based on the machine-learned image data. In order to test the performance of the real-time image recognition algorithm, we applied the real-time image recognition algorithm to the autonomous vehicle and showed the performance of the real-time image recognition algorithm through the application of the autonomous vehicle.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.625-631
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• 2010

This paper proposes lane recognition algorithm using lane prominence technique to extract lane candidate. The lane prominence technique is combined with embossing effect, lane thickness check, and lane extraction using mask. The proposed lane recognition algorithm consists of preprocessing, lane candidate extraction and lane recognition. First, preprocessing is executed, which includes gray image acquisition, inverse perspective transform and gaussian blur. Second, lane candidate is extracted by using lane prominence technique. Finally, lane is recognized by using hough transform and least square method. To evaluate the proposed lane recognition algorithm, this algorithm was applied to the detection of lanes in the rainy and night day. The experiment results showed that the proposed algorithm can recognize lane in various environment. It means that the algorithm can be applied to lane recognition to drive unmanned vehicles.

• Speech Sciences
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• v.8 no.4
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• pp.17-33
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• 2001

In this paper, we implement the large variable vocabulary speech recognition system, which is characterized by no additional pre-training process and no limitation of recognized word list. We have designed the system in order to achieve the high recognition rate using the decision tree based state tying algorithm and in order to reduce the processing time using the gaussian selection based variable flooring algorithm, the limitation algorithm of the number of nodes and ENNS algorithm. The gaussian selection based variable flooring algorithm shows that it can reduce the total processing time by more than half of the recognition time, but it brings about the reduction of recognition rate. In other words, there is a trade off between the recognition rate and the processing time. The limitation algorithm of the number of nodes shows the best performance when the number of gaussian mixtures is a three. Both of the off-line and on-line experiments show the same performance. In our experiments, there are some differences of the recognition rate and the average recognition time according to the distinction of genders, speakers, and the number of vocabulary.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.129-132
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• 1991

This paper describes the ECG pattern recognition using the syntatic pattern recognition algorithm. The algorithm uses the BNF rule wi th the semantic evaluation which has the structural Information of the ECG. This algorithm is constructed with (1) removing the baseline drift by the Cubic spline function and exract the significant point by the line-approximation algorithm, (2) syntatic peak recognition algorithm with the extracted significant point, (3) produce the token which is used pattern recognition, (4) pattern recognition of the ECG by the syntatic pattern recognition algorithm, (5) extract the parameter with the pattern recognized ECG signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.252-258
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• 2007

In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.35-47
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• 2018

The 4th industrial revolution, digital image technology has developed beyond the limit of multimedia industry to advanced IT fusion and composite industry. Particularly, application technology related to HCI element algorithm in 3D image object recognition field is actively developed. 3D image object recognition technology evolved into intelligent image sensing and recognition technology through 3D modeling. In particular, image recognition technology has been actively studied in image processing using object recognition recognition processing, face recognition, object recognition, and 3D object recognition. In this paper, we propose a research method of human factor 3D image recognition technology applying human factor algorithm for 3D object recognition. 1. Methods of 3D object recognition using 3D modeling, image system analysis, design and human cognitive technology analysis 2. We propose a 3D object recognition parameter estimation method using FACS algorithm and optimal object recognition measurement method. In this paper, we propose a method to effectively evaluate psychological research techniques using 3D image objects. We studied the 3D 3D recognition and applied the result to the object recognition element to extract and study the characteristic points of the recognition technology.

• 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.