• Title, Summary, Keyword: ART2 algorithm

Search Result 197, Processing Time 0.04 seconds

An Intelligent System for Recognition of Identifiers from Shipping Container Images using Fuzzy Binarization and Enhanced Hybrid Network

  • Kim, Kwang-Baek
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.14 no.3
    • /
    • pp.349-356
    • /
    • 2004
  • The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. In this paper we propose and evaluate a novel recognition algorithm for container identifiers that effectively overcomes these difficulties and recognizes identifiers from container images captured in various environments. The proposed algorithm, first, extracts the area containing only the identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper. Then a contour tracking method is applied to the binarized area in order to extract the container identifiers which are the target for recognition. In this paper we also propose and apply a novel ART2-based hybrid network for recognition of container identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm performs better for extraction and recognition of container identifiers compared to conventional algorithms.

Contents-based Image Retrieval using Fuzzy ART Neural Network (퍼지 ART 신경망을 이용한 내용기반 영상검색)

  • 박상성;이만희;장동식;김재연
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.4 no.2
    • /
    • pp.12-17
    • /
    • 2003
  • This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

  • PDF

Network based Intrusion Detection System using Adaptive Resonance Theory 2 (Adaptive Resonance Theory 2를 이용한 네트워크 기반의 침입 탐지 모델 연구)

  • 김진원;노태우;문종섭;고재영;최대식;한광택
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.12 no.3
    • /
    • pp.129-139
    • /
    • 2002
  • As internet expands, the possibility of attack through the network is increasing. So we need the technology which can detect the attack to the system or the network spontaneously. The purpose of this paper proposes the system to detect intrusion automatically using the Adaptive Resonance Theory2(ART2) which is one of artificial neural network The parameters of the system was tunned by ART2 algorithm using a lot of normal packets and various attack packets which were intentionally generated by attack tools. The results were compared and analyzed with conventional methods.

Automatic Extraction of Canine Cataract Area with Fuzzy Clustering (퍼지 클러스터링을 이용한 반려견의 백내장 영역 자동 추출)

  • Kim, Kwang Baek
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.22 no.11
    • /
    • pp.1428-1434
    • /
    • 2018
  • Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. In this paper, we propose a method for extracting cataract suspicious areas automatically with FCM(Fuzzy C_Means) algorithm to overcome the weakness of previously attempted ART2 based method. The proposed method applies the fuzzy stretching technique and the Max-Min based average binarization technique to the dog eye images photographed by simple devices such as mobile phones. After applying the FCM algorithm in quantization, we apply the brightness average binarization method in the quantized region. The two binarization images - Max-Min basis and brightness average binarization - are ANDed, and small noises are removed to extract the final cataract suspicious areas. In the experiment with 45 dog eye images with canine cataract, the proposed method shows better performance in correct extraction rate than the ART2 based method.

Self Health Diagnosis System of Oriental Medicine Using Enhanced Fuzzy ART Algorithm (개선된 퍼지 ART 알고리즘을 이용한 한방 자가 진단 시스템)

  • Kim, Kwang-Baek;Woo, Young-Woon
    • Journal of the Korea Society of Computer and Information
    • /
    • v.15 no.2
    • /
    • pp.27-34
    • /
    • 2010
  • Recently, lots of internet service companies provide on-line health diagnosis systems. But general persons not having expert knowledge are difficult to use, because most of the health diagnosis systems present prescriptions or dietetic treatments for diseases based on western medicine. In this paper, a self health diagnosis system of oriental medicine coinciding with physical characteristics of Korean using fuzzy ART algorithm, is proposed. In the proposed system, three high rank of diseases having high similarity values are derived by comparing symptoms presented by a user with learned symptoms of specific diseases based on treatment records using neural networks. And also the proposed system shows overall symptoms and folk remedies for the three high rank of diseases. Database on diseases and symptoms is built by several oriental medicine books and then verified by a medical specialist of oriental medicine. The proposed self health diagnosis system of oriental medicine showed better performance than conventional health diagnosis systems by means of learning diseases and symptoms using treatment records.

A Fault Diagnosis Based on Multilayer/ART2 Neural Networks (다층/ART2 신경회로망을 이용한 고장진단)

  • Lee, In-Soo;Yu, Du-Hyoung
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.14 no.7
    • /
    • pp.830-837
    • /
    • 2004
  • Neural networks-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the multilayer neural network-based nominal model output cross a Predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by nultilayer/ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

Adaptive Random Testing through Iterative Partitioning with Enlarged Input Domain (입력 도메인 확장을 이용한 반복 분할 기반의 적응적 랜덤 테스팅 기법)

  • Shin, Seung-Hun;Park, Seung-Kyu
    • The KIPS Transactions:PartD
    • /
    • v.15D no.4
    • /
    • pp.531-540
    • /
    • 2008
  • An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

Music Image Recognition using Hierarchical ART2 Algorithm (Hierarchical ART2 알고리즘을 이용한 악보 영상 인식)

  • Kim, Mi-Jeong;Kim, Jae-Kun;Park, Choong-Shik;Kim, Kwang-Baek
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.369-374
    • /
    • 2008
  • 음악 연구에 따른 컴퓨터의 역할이 점자 중요한 비중을 차지함에 따라 보다 효과적인 악보 인식과 효율적인 악보의 편집 및 수정 방법이 요구된다. 기존의 수동 입력 방식에서는 악보를 부정확하게 입력하여 수정하는 경우에는 작업 시간이 많이 소요되며, 각 수정 프로그램에서 만든 악보는 특정 프로그램에서만 재수정이 가능하다는 단점이 있다. 본 논문에서는 이러한 단점을 보완하기 위하여 이미 작성 되어있는 악보들을 자동으로 인식하는 방법을 제안한다. 제안된 악보 인식 방법은 수평 히스토그램을 이용하여 악보 이미지의 오선을 제거한 후, 4방향 윤곽선 추적 알고리즘을 적용하여 잡음을 제거하고 Grassfire 알고리즘을 적용하여 악보 구성 기호들을 추출한다. 추출된 악보 구성 기호들은 Hierarchical ART2 알고리즘을 적용하여 인식한다. 인식된 악보구성 기초들을 이용하여 악보 구성 기호들이 속하는 마디의 위치 정보를 각각 저장하고 향후에 악보 구성 기호의 편집과 수정이 용이하게 한다. 제안된 악보 인식 방법의 성능을 평가하기 위해 100장의 악보 영상을 대상으로 실험한 결과, 제시된 Hierarchical ART2 알고리즘을 이용한 악보 영상의 인식 방법이 실험을 통해서 효율적인 것을 확인하였다.

  • PDF

An Enhanced Fuzzy ART Algorithm for Effective Image Recognition (효과적인 영상 인식을 위한 개선된 퍼지 ART 알고리즘)

  • Kim, Kwang-Baek;Park, Choong-Shik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.262-267
    • /
    • 2007
  • 퍼지 ART 알고리즘에서 경계 변수는 패턴들을 클러스터링하는데 있어서 반지름 값이 되며 임의의 패턴과 저장된 패턴과의 불일치(mismatch) 허용도를 결정한다. 이 경계 변수가 크면 입력 벡터와 기대 벡터 사이에 약간의 차이가 있어도 새로운 카테고리(category)로 분류하게 된다. 반대로 경계 변수가 작으면 입력 벡터와 기대 벡터 사이에 많은 차이가 있더라도 유사성이 인정되어 입력 벡터들을 대략적으로 분류한다. 따라서 영상 인식에 적용하기 위해서는 경험적으로 경계 변수를 설정해야 단점이 있다. 그리고 연결 가중치를 조정하는 과정에서 학습률의 설정에 따라 저장된 패턴들의 정보들이 손실되는 경우가 발생하여 인식율을 저하시킨다. 본 논문에서는 퍼지 ART 알고리즘의 문제점을 개선하기 위하여 퍼지 논리 접속 연산자를 이용하여 경계 변수를 동적으로 조정하고 저장 패턴들과 학습 패턴간의 실제적인 왜곡 정도를 충분히 고려하여 승자 노드로 선택된 빈도수를 학습률로 설정하여 가중치 조정에 적용한 개선된 퍼지 ART 알고리즘을 제안하였다. 제안된 방법의 성능을 확인하기 위해서 실제 영문 명함에서 추출한 영문자들을 대상으로 실험한 결과, 기존의 ART1과 ART2 알고리즘이나 퍼지 ART 알고리즘보다 클러스터의 수가 적게 생성되었고 인식 성능도 기존의 방법들보다 우수한 성능이 있음을 확인하였다.

  • PDF

Development of Human Detection Algorithm for Automotive Radar (보행자 탐지용 차량용 레이더 신호처리 알고리즘 구현 및 검증)

  • Hyun, Eugin;Jin, Young-Seok;Kim, Bong-Seok;Lee, Jong-Hun
    • Transactions of the Korean Society of Automotive Engineers
    • /
    • v.25 no.1
    • /
    • pp.92-102
    • /
    • 2017
  • For an automotive surveillance radar system, fast-chirp train based FMCW (Frequency Modulated Continuous Wave) radar is a very effective method, because clutter and moving targets are easily separated in a 2D range-velocity map. However, pedestrians with low echo signals may be masked by strong clutter in actual field. To address this problem, we proposed in the previous work a clutter cancellation and moving target indication algorithm using the coherent phase method. In the present paper, we initially composed the test set-up using a 24 GHz FMCW transceiver and a real-time data logging board in order to verify this algorithm. Next, we created two indoor test environments consisting of moving human and stationary targets. It was found that pedestrians and strong clutter could be effectively separated when the proposed method is used. We also designed and implemented these algorithms in FPGA (Field Programmable Gate Array) in order to analyze the hardware and time complexities. The results demonstrated that the complexity overhead was nearly zero compared to when the typical method was used.