• Title/Summary/Keyword: Pattern Vector

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Density based Fuzzy Support Vector Machines for multicategory Pattern Classification (밀도에 기반한 펴지 서포트 벡터 머신을 이용한 멀티 카데고리에서의 패턴 분류)

  • Park Jong-Hoon;Choi Byung-In;Rhee Frank Chung-Hoon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2006.11a
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    • pp.251-254
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    • 2006
  • 본 논문은 multiclass 문제에서 기존에 나와 있는 fuzzy support vector mahchines 이 decision boundary 를 설정하는데 있어 모든 훈련 데이터에 대해서 바람직한 decision boundary 를 만들지 못하므로 그러한 경우를 예로 제시한다. 그리고 그에 대한 개선점으로 밀도를 이용해 decision boundary 를 조정하여 기존 FSVM 의 decision boundary 보다 더 타당한 decision boundary 를 설정하는 것을 보인다.

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A Fast Block Matching Algorithm by using the Cross Pattern and Flat-Hexagonal Search Pattern (크로스 패턴과 납작한 육각 탐색패턴을 이용한 고속 블록 정합 알고리즘)

  • 남현우;김종경
    • Journal of the Korea Computer Industry Society
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    • v.4 no.12
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    • pp.953-964
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    • 2003
  • In the block matching algorithm, search patterns of different shapes or sizes and the distribution of motion vectors have a large impact on both the searching speed and the image quality. In this paper, we propose a new fast block matching algorithm using the cross pattern and the flat-hexagon search pattern. Our algorithm first finds the motion vectors that are close to the center of search window using the cross pattern, and then lastly finds the other motion vectors that are not close to the center of search window using the flat-hexagon search pattern. Through experiments, compared with the hexagon-based search algorithm(HEXBS), the proposed cross pattern and flat-hexagonal pattern search algorithm(CFHPS ) improves about 0.2-6.2% in terms of average number of search point per motion vector estimation and improves about 0.02-0.31dB in terms of PSNR(Peak Signal to Noise Ratio).

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Two-wheelers Detection using Uniform Local Binary Pattern for Projection Vectors (투영 벡터의 단일 이진패턴 가중치을 이용한 이륜차 검출)

  • Lee, Yeunghak
    • Journal of Korea Multimedia Society
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    • v.18 no.4
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    • pp.443-451
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    • 2015
  • In this paper we suggest a new two-wheelers detection algorithm using uniform local binary pattern weighting value for projection vectors. The first, we calculate feature vectors using projection method which has robustness for rotation invariant and reducing dimensionality for each cell from origin image. The second, we applied new weighting values which are calculated by the modified local binary pattern showing the fast compute and simple to implement. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

A Fast Motion Estimation Algorithm with Motion Analysis (움직임 해석을 통한 고속 움직임 예측 알고리즘)

  • Jun, Young-Hyun;Yun, Jong-Ho;Cho, Hwa-Hyun;Choi, Myung-Ryul
    • Proceedings of the IEEK Conference
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    • 2005.11a
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    • pp.339-342
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    • 2005
  • We present an efficient block-based motion estimation algorithm with motion analysis. The motion analysis determines a size of search pattern and a maximum repeated count of search pattern. In case of large movement in large image, we reduce search points and the local minimum which caused by low performance. The proposed algorithm employs with searching step of 2. The first step determines an initial search point with neighbor block vector and a size of initial search pattern. The second step determines a size of search pattern and a maximum repeated count with motion analysis. We improve motion prediction accuracy while reducing required computational complexity compared to other fast block-based motion estimation algorithms.

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Projected Local Binary Pattern based Two-Wheelers Detection using Adaboost Algorithm

  • Lee, Yeunghak;Kim, Taesun;Shim, Jaechang
    • Journal of Multimedia Information System
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    • v.1 no.2
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    • pp.119-126
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    • 2014
  • We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.

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Target-to-Clutter Ratio Enhancement of Images in Through-the-Wall Radar Using a Radiation Pattern-Based Delayed-Sum Algorithm

  • Lim, Youngjoon;Nam, Sangwook
    • Journal of electromagnetic engineering and science
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    • v.14 no.4
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    • pp.405-410
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    • 2014
  • In this paper, we compare the quality of images reconstructed by a conventional delayed-sum (DS) algorithm and radiation pattern-based DS algorithm. In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The radiation pattern-based DS algorithm enhances the TCR of the image by focusing the target signals and preventing contamination of the radar scene. We first consider synthetic data obtained through GprMax2D/3D, a finite-difference time-domain (FDTD) forward solver. Experimental data of a 2-GHz bandwidth stepped-frequency signal are collected using a vector network analyzer (VNA) in an anechoic chamber setup. The radiation pattern-based DS algorithm shows a 6.7-dB higher TCR compared to the conventional DS algorithm.

Pattern Classification Method using SOFM and Multilayer Neural Network (SOFM과 다층신경회로망을 이용한 패턴 분류 방식)

  • 박진성;공휘식;이현관;김주웅;엄기환
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2002.11a
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    • pp.296-300
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    • 2002
  • We proposed a method of a pattern classification using unsupervised teaming rules, SOFM, and supervised teaming rules, Multilayer neural network. Establish result that classify and get input pattern using SOFM by initial weighting vector of Multilayer neural network and target value. Got superior Performance as result that do simulation about face image to confirm usefulness of way that propose.

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EMG Pattern Recognition based on Evidence Accumulation for Prosthesis Control

  • Lee, Seok-Pil;Park, Sand-Hui
    • Journal of Electrical Engineering and information Science
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    • v.2 no.6
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    • pp.20-27
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    • 1997
  • We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

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Tool Breakage Detection using Pattern Characteristics of Feed Motor Current in Milling Operations (이송모터 전류신호의 패턴특성을 이용한 밀링공구의 파손검출)

  • KIM, Sun-ho;Ahn, Jung-hwan;Park, Hwa-young
    • Journal of the Korean Society for Precision Engineering
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    • v.12 no.2
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    • pp.23-34
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    • 1995
  • This paper is concerned with effective and reliable tool breakage detection method using pattern characteristics of feed motor current in milling operations. Correlation coefficient is derived from the feature vector of signal for two consecutive which are extracted feed motor current over three spindle revolutions. The changing pattern of correlation coefficient is continuously compared to detect tool breakage and monitor cutting conditions. This proposed monitoring scheme is not affected by different tools, friction of motion, and varying cutting conditions and material shapes. Experimental results are presented to support the proposed monitoring scheme.

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Development of Traditional Baeja Design Applied Jogakbo and Knot (조각보와 매듭을 활용한 전통 배자 디자인 개발)

  • Yang, Suk-Hyang
    • Journal of the Korea Fashion and Costume Design Association
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    • v.16 no.4
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    • pp.189-203
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    • 2014
  • In this study, Creative motifs using face composition of Jogakbo and Knot Symbol were developed, and applied to traditional Baeja of Joseon Dynasty to develop design contents of traditional clothes. As for study method, 7 motifs of new formative image that integrates traditional beauty and contemporary sense were developed by applying Knot Symbols and face compositions of Jogakbo with the use of Adobe Illustrator CS6 and Adobe Photoshop CS6 vector graphic software. The motifs were designed in contemporary image in face compositions like rectangle pattern, triangle pattern, dual rectangle pattern, vertical and horizontal pattern, pinwheel pattern, gojunmun pattern and free pattern by involving various changes like repetition, rotation, reduction, expansion and decomposition and using the colors used in the Jogakbo. It is desired that through this study, traditional Baeja may develop to bear traditional and contemporary image so that our traditional clothes design may become global. Also it is anticipated that this study will contribute to development of culture products of Hanbok like Jeogori, pants and skirt that require change of design in the global era while maintaining traditional beauty to appeal to the emotions of world citizens.

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