• Title, Summary, Keyword: ART2 algorithm

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Recognition of a New Car License Plate Using HSI Information, Fuzzy Binarization and ART2 Algorithm (HSI 정보와 퍼지 이진화 및 ART2 알고리즘을 이용한 신차량 번호판의 인식)

  • Kim, Kwang-Baek;Woo, Young-Woon;Park, Choong-Shik
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.11 no.5
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    • pp.1004-1012
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    • 2007
  • In this paper, we proposed a new car license plate recognition method using an unsupervised ART2 algorithm with HSI color model. The proposed method consists of two main modules; extracting plate area from a vehicle image and recognizing the characters in the plate after that. To extract plate area, hue(H) component of HSI color model is used, and the sub-area containing characters is acquired using modified fuzzy binarization method. Each character is further divided by a 4-directional edge tracking algorithm. To recognize the separated characters, noise-robust ART2 algorithm is employed. When the proposed algorithm is applied to recognize license plate characters, the extraction rate is better than that of existing RGB model and the overall recognition rate is about 97.4%.

A Study on the Recognition of an English Calling Card by using Contour Tracking Algorithm and Enhanced ART1 (윤곽선 추적 알고리즘과 개선된 ART1을 이용한 영문 명함 인식에 관한 연구)

  • 김광백;김철기;김정원
    • Journal of Intelligence and Information Systems
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    • v.8 no.2
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    • pp.105-115
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    • 2002
  • This paper proposed a recognition method of english calling card using both 4-directed contour tracking algorithm and enhanced ART1 algorithm. After we extract candidate character string region using horizontal smearing and 4-directed contour tracking method, we extract character string region through comparison of character region and non-character region using horizontal and vertical ratio and area in english calling card. In extracted character string region, we extract each character using horizontal smearing and contour tracking algorithm, and recognize each character by enhanced ART1 algorithm. The proposed ART1 algorithm is enhanced by dynamic control of similarity using fuzzy sum connective operator. The result indicate that the proposed method is superior in performance.

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A Study on Enhanced Self-Generation Supervised Learning Algorithm for Image Recognition (영상 인식을 위한 개선된 자가 생성 지도 학습 알고리듬에 관한 연구)

  • Kim, Tae-Kyung;Kim, Kwang-Baek;Paik, Joon-Ki
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.2C
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    • pp.31-40
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    • 2005
  • we propose an enhanced self-generation supervised algorithm that by combining an ART algorithm and the delta-bar-delta method. Form the input layer to the hidden layer, ART-1 and ART-2 are used to produce nodes, respectively. A winner-take-all method is adopted to the connection weight adaption so that a stored pattern for some pattern is updated. we test the recognition of student identification, a certificate of residence, and an identifier from container that require nodes of hidden layers in neural network. In simulation results, the proposed self-generation supervised learning algorithm reduces the possibility of local minima and improves learning speed and paralysis than conventional neural networks.

Recognition of Car License Plates Using Difference Operator and ART2 Algorithm (차 연산과 ART2 알고리즘을 이용한 차량 번호판 통합 인식)

  • Kim, Kwang-Baek;Kim, Seong-Hoon;Woo, Young-Woon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.11
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    • pp.2277-2282
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    • 2009
  • In this paper, we proposed a new recognition method can be used in application systems using morphological features, difference operators and ART2 algorithm. At first, edges are extracted from an acquired car image by a camera using difference operators and the image of extracted edges is binarized by a block binarization method. In order to extract license plate area, noise areas are eliminated by applying morphological features of new and existing types of license plate to the 8-directional edge tracking algorithm in the binarized image. After the extraction of license plate area, mean binarization and mini-max binarization methods are applied to the extracted license plate area in order to eliminated noises by morphological features of individual elements in the license plate area, and then each character is extracted and combined by Labeling algorithm. The extracted and combined characters(letter and number symbols) are recognized after the learning by ART2 algorithm. In order to evaluate the extraction and recognition performances of the proposed method, 200 vehicle license plate images (100 for green type and 100 for white type) are used for experiment, and the experimental results show the proposed method is effective.

An Enhanced Fuzzy ART Algorithm for The Effective Identifier Recognition From Shipping Container Image (효과적인 운송 컨테이너 영상의 식별자 인식을 위한 개선된 퍼지 ART 알고리즘)

  • 김광백
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.5C
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    • pp.486-492
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    • 2003
  • The vigilance threshold of conventional fuzzy ART algorithm decide whether to permit the mismatch between any input pattern and stored pattern. If the vigilance threshold was large, despite of little difference among input and stored patterns, the input pattern may be classified to new category. On the other hand, if the vigilance threshold was small, the similarity between two patterns may be accepted in spite of lots of difference and the input pattern are classified to category of the stored pattern. Therefore, the vigilance threshold for the image recognition must be experientially set for the good result. Moreover, it may occur in the fuzzy ART algorithm that the information of stored patterns is lost in the weight-adjusting process and the rate of pattern recognition is dropped. In this paper, I proposed the enhanced fuzzy ART algorithm that supports the dynamical setting of the vigilance threshold using the generalized intersection operator of fuzzy logic and the weight value being adaptively set in proportional to the current weight change and the previous weight by reflecting the frequency of the selection of winner node. For the performance evaluation of the proposed method, we applied to the recognition of container identifiers from shipping container images. The experiment showed that the proposed method produced fewer clusters than conventional ART2 and fuzzy ART algorithm. and had tile higher recognition rate.

Container Image Recognition using Fuzzy-based Noise Removal Method and ART2-based Self-Organizing Supervised Learning Algorithm (퍼지 기반 잡음 제거 방법과 ART2 기반 자가 생성 지도 학습 알고리즘을 이용한 컨테이너 인식 시스템)

  • Kim, Kwang-Baek;Heo, Gyeong-Yong;Woo, Young-Woon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.11 no.7
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    • pp.1380-1386
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    • 2007
  • This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is blacker white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm. Experiments using real images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed.

Multiple faults diagnosis of a linear system using ART2 neural networks (ART2 신경회로망을 이용한 선형 시스템의 다중고장진단)

  • Lee, In-Soo;Shin, Pil-Jae;Jeon, Gi-Joon
    • Journal of Institute of Control, Robotics and Systems
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    • v.3 no.3
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    • pp.244-251
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    • 1997
  • In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

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Musical Score Recognition Using Hierarchical ART2 Algorithm (Hierarchical ART2 알고리즘을 이용한 악보 인식)

  • Kim, Kwang-Baek;Woo, Young-Woon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.10
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    • pp.1997-2003
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    • 2009
  • Methods for effective musical score recognition and efficient editing of musical scores are demanded because functions of computers for researches on musical activities become more and more important parts in recent days. In the conventional methods for handling musical scores manually, there are weak points such as incorrect score symbols in input process and requirement of much time to adjust the incorrect symbols. And also there is another weak point that the scores edited by each application program can be remodified by a specific application program only. In this paper, we proposed a method for automatic musical score recognition of printed musical scores in order to make up for the weak points. In the proposed method, staffs in a scanned score image are eliminated by horizontal histogram, noises are removed by 4 directional edge tracking algorithm, and then musical score symbols are extracted by using Grassfire algorithm. The extracted symbols are recognized by hierarchical ART2 algorithm. In order to evaluate the performance of the proposed method, we used 100 musical scores for experiment. In the experiment, we verified that the proposed method using hierarchical ART2 algorithm is efficient.

ZPerformance Improvement of ART2 by Two-Stage Learning on Circularly Ordered Learning Sequence (순환 배열된 학습 데이터의 이 단계 학습에 의한 ART2 의 성능 향상)

  • 박영태
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.5
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    • pp.102-108
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    • 1996
  • Adaptive resonance theory (ART2) characterized by its built-in mechanism of handling the stability-plasticity switching and by the adaptive learning without forgetting informations learned in the past, is based on an unsupervised template matching. We propose an improved tow-stage learning algorithm for aRT2: the original unsupervised learning followed by a new supervised learning. Each of the output nodes, after the unsupervised learning, is labeled according to the category informations to reinforce the template pattern associated with the target output node belonging to the same category some dominant classes from exhausting a finite number of template patterns in ART2 inefficiently. Experimental results on a set of 2545 FLIR images show that the ART2 trained by the two-stage learning algorithm yields better accuracy than the original ART2, regardless of th esize of the network and the methods of evaluating the accuracy. This improvement shows the effectiveness of the two-stage learning process.

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Developing an Intelligent Self-Health Pre-Diagnosing System based on ART2 (ART2 기반 지능형 자가 건강 진단 시스템의 개발)

  • Kim, Kwang-Baek
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.5
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    • pp.11-18
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    • 2014
  • In this paper, we propose a self-diagnosis system that is based on the ART2 algorithm in order to extract more detailed disease information by fuzzy reasoning method especially when the boundary of perceived symptoms are not clearly classified into disease categories. With that modification from previous version of the self health pre-diagnosis system, the proposed one is verified as more effective by field experts' evaluation as an intelligent assistant tool for public users before they consult with medical experts.