• Title, Summary, Keyword: Extraction Feature Vector

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Optimal feature extraction for normally distributed multicall data (가우시안 분포의 다중클래스 데이터에 대한 최적 피춰추출 방법)

  • 최의선;이철희
    • Proceedings of the IEEK Conference
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    • pp.1263-1266
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    • 1998
  • In this paper, we propose an optimal feature extraction method for normally distributed multiclass data. We search the whole feature space to find a set of features that give the smallest classification error for the Gaussian ML classifier. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we compute the classification error. Then we move the feature vector slightly and compute the classification error with this vector. Finally we update the feature vector such that the classification error decreases most rapidly. This procedure is done by taking gradient. Alternatively, the initial vector can be those found by conventional feature extraction algorithms. We propose two search methods, sequential search and global search. Experiment results show that the proposed method compares favorably with the conventional feature extraction methods.

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Morphological Feature Extraction of Microorganisms Using Image Processing

  • Kim Hak-Kyeong;Jeong Nam-Su;Kim Sang-Bong;Lee Myung-Suk
    • Fisheries and aquatic sciences
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    • v.4 no.1
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    • pp.1-9
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    • 2001
  • This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.

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SVM-based Drone Sound Recognition using the Combination of HLA and WPT Techniques in Practical Noisy Environment

  • He, Yujing;Ahmad, Ishtiaq;Shi, Lin;Chang, KyungHi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.10
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    • pp.5078-5094
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    • 2019
  • In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

The Important Frequency Band Selection and Feature Vecotor Extraction System by an Evolutional Method

  • Yazama, Yuuki;Mitsukura, Yasue;Fukumi, Minoru;Akamatsu, Norio
    • 제어로봇시스템학회:학술대회논문집
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    • pp.2209-2212
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    • 2003
  • In this paper, we propose the method to extract the important frequency bands from the EMG signal, and for generation of feature vector using the important frequency bands. The EMG signal is measured with 4 sensor and is recorded as 4 channel’s time series data. The same frequency bands from 4 channel’s frequency components are selected as the important frequency bands. The feature vector is calculated by the function formed using the combination of selected same important frequency bands. The EMG signals acquired from seven wrist motion type are recognized by changing into the feature vector formed. Then, the extraction and generation is performed by using the double combination of the genetic algorithm (GA) and the neural network (NN). Finally, in order to illustrate the effectiveness of the proposed method, computer simulations are done.

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Implementation of a Feature Extraction Chip for High Speed OCR (고속 문자 인식을 위한 특정 추출용 칩의 구현)

  • 김형구;강선미;김덕진
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.6
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    • pp.104-110
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    • 1994
  • We proposed a high speed feature extraction algorithm and developed a feature vector extraction chip for high speed character recognition. It is hard to implement a high speed OCR by software alone with statistical method . Thus, the whole recognition process is divided into functional steps, then pipeline processed so that high speed processing is possible with temporal parallelism of the steps. In this paper we discuss the feature extraction step of the functional steps. To extract feature vector, a character image is normalized to 40$\times$40 pixels. Then, it is divided into 5$\times$5 subregions and 4x4 subregions to construct 41 overlapped subregions(10x10 pixels). It requires to execute more than 500 commands to extract a feature vector of a subregion by software. The proposed algorithm, however, requires only 10 cycles since it can extract a feature vector of a columm of subregion in one cycle with array structure. Thus, it is possible to process 12.000 characters per second with the proposed algorithm. The chip is implemented using EPLD and the effectiveness is proved by developing an OCR using it.

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Automatic Classification of Power Quality Disturbances Using Efficient Feature Vector Extraction and Neural Networks (효율적 특징벡터 추출기법와 신경회로망을 이용한 전력외란 자동 식별)

  • Ban, Ji-Hoon;Kim, Hyun-Soo;Nam, Sang-Won
    • Proceedings of the KIEE Conference
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    • pp.1030-1032
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    • 1998
  • In this paper, an efficient feature vector extraction method and MLP neural network are utilized to automatically detect and classify power quality disturbances, where the proposed classification procedure consists of the following three parts: i.e., (i) PQ disturbance detection using discrete wavelet transform. (ii) feature vector extraction from the detected disturbance. using several methods, such as FFT, DWT, Fisher's criterion. etc.. and (iii) classification of the corresponding type of each PQ disturbance by recognizing the pattern of the extracted feature vector. To demonstrate the performance and, applicability of the proposed classification algorithm. some test results obtained by analyzing 10-class PQ disturbances are also provided.

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Classification of Power Quality Disturbances Using Feature Vector Combination and Neural Networks (특징벡터 결합과 신경회로망을 이용한 전력외란 식별)

  • Nam, Sang-Won
    • Proceedings of the KIEE Conference
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    • pp.671-674
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    • 1997
  • The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FIT, DWT(Discrete Wavelet Transform), and Fisher's criterion are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 10-class power quality disturbances are also provided.

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A Feature Vector Extraction Method For the Automatic Classification of Power Quality Disturbances (전력 외란 자동 식별을 위한 특징 벡터 추출 기법)

  • Lee, Chul-Ho;Lee, Jae-Sang;Cho, Kwan-Young;Chung, Ji-Hyun;Nam, Sang-Won
    • Proceedings of the KIEE Conference
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    • pp.404-406
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    • 1996
  • The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FFT, DWT(Discrete Wavelet Transform), and data compression are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 7-class power quality disturbances generated by the EMTP are also provided.

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Drone Sound Identification and Classification by Harmonic Line Association Based Feature Vector Extraction (Harmonic Line Association 기반 특징벡터 추출에 의한 드론 음향 식별 및 분류)

  • Jeong, HyoungChan;Lim, Wonho;He, YuJing;Chang, KyungHi
    • Journal of Advanced Navigation Technology
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    • v.20 no.6
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    • pp.604-611
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    • 2016
  • Drone, which refers to unmanned aerial vehicles (UAV), industries are improving rapidly and exceeding existing level of remote controlled aircraft models. Also, they are applying automation and cloud network technology. Recently, the ability of drones can bring serious threats to public safety such as explosives and unmanned aircraft carrying hazardous materials. On the purpose of reducing these kinds of threats, it is necessary to detect these illegal drones, using acoustic feature extraction and classifying technology. In this paper, we introduce sound feature vector extraction method by harmonic feature extraction method (HLA). Feature vector extraction method based on HLA make it possible to distinguish drone sound, extracting features of sound data. In order to assess the performance of distinguishing sounds which exists in outdoor environment, we analyzed various sounds of things and real drones, and classified sounds of drone and others as simulation of each sound source.

A Study on the Feature Extraction for High Speed Character Recognition -By Using Interative Extraction and Hierarchical Formation of Directional Information- (고속 문자 인식을 위한 특징량 추출에 관한 연구 - 방향정보의 반복적 추출과 특징량의 계층성을 이용하여 -)

  • 강선미;이기용;양윤모;양윤모;김덕진
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.29B no.11
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    • pp.102-110
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    • 1992
  • In this paper, a new method of character recognition is proposed. It uses density information, in addition to positional and directional information generally used, to recognize a character. Four directional feature primitives are extracted from the thinning templates on the observation that the output of the templates have directional property in general. A simple and fast feature extraction scheme is possible. Features are organized from recursive nonary tree(N-tree) that corresponds to normalized character area. Each node of the N-tree has four directional features that are sum of the features of it's nine sub-nodes. Every feature primitive from the templates are added to the corresponding leaf and then summed to the upper nodes successively. Recognition can be accomplished by using appropriate feature level of N-tree. Also, effectiveness of each node's feature vector was tested by experiment. A method to implement the proposed feature vector organization algorithm into hardware is proposed as well. The third generation node, which is 4$\times$4, is used as a unit processing element to extract features, and it was implemented in hardware. As a result, we could observe that it is possible to extract feature vector for real-time processing.

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