• 한국콘텐츠학회논문지
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• 제9권4호
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• pp.121-130
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• 2009

이 논문은 고차원의 데이터를 저 차원으로 줄이는 방법 중 하나인 특징추출에 대한 방법들의 특성을 비교한다. 비교대상 방법은 전통적인 PCA(Principal Component Analysis)방법과 시각피질의 특성을 보인다고 알려진 ICA(Independent Component Analysis), 국소기반인식을 구현한 NMF(Non-negative Matrix Factorization), 그리고 이의 성능을 개선한 sNMF(Sparse NMF)로 정하였다. 추출된 특징들의 특성을 시각적으로 확인하기 위하여 필기체 숫자 영상을 대상으로 특징추출을 수행하였으며, 인식기에 적용한 효과의 확인을 위하여 추출된 특징을 다층퍼셉트론에 학습시켜보았다. 각 방법의 특성을 비교한 결과는 응용하고자 하는 문제에서 어떤 특징을 추출하기 원하느냐에 따라 특징추출 방법을 선정할 때 유용할 것이다.

• 한국정밀공학회지
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• 제19권11호
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• pp.103-111
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• 2002

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• 한국지능시스템학회논문지
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• 제13권3호
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• pp.281-286
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• 2003

웨이블릿 변환(Wavelet Transform)은 공간-주파수 영역에서 신호의 국소특성을 효율적으로 구현할 수 있다 하지만, 웨이블릿 변환을 패턴 인식을 위한 특징 추출에 적용할 경우, 입력 신호의 위치 이동에 따라 추출된 특징 값이 변화하게 되어 인식률이 낮아지는 결함이 있다. 본 논문에서는 웨이블릿 변환을 패턴 인식에 적용할 경우 발생하는 입력 신호의 위치 이동에 따른 문제점을 보완하여 노이즈에 강인한 홍채인식 알고리즘을 제안한다. 실험을 통하여 제안한 알고리즘의 우수성을 보여 준다.

• 제어로봇시스템학회논문지
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• 제11권12호
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• pp.1062-1067
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• 2005

This paper discusses a skeleton feature extraction method for ubiquitous robot system. The skeleton features are used to analyze human motion and pose estimation. In different conventional feature extraction environment, the ubiquitous robot system requires more robust feature extraction method because it has internal vibration and low image quality. The new hybrid silhouette extraction method and adaptive skeleton model are proposed to overcome this constrained environment. The skin color is used to extract more sophisticated feature points. Finally, the experimental results show the superiority of the proposed method.

• 전자공학회논문지B
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• 제31B권6호
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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.

• 전자공학회논문지B
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• 제30B권9호
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• pp.94-105
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• 1993

In this paper propose a Koaren character recognition system using a neural network is proposed. This system is a multilayer neural network based on the masking field model which consists of a input layer, four feature extraction layers which extracts type, direction, stroke, and connection features, and an output layer which gives us recognized character codes. First, 4x4 subpatterns of an NxN character pattern stored in the input buffer are applied into the feature extraction layers sequentially. Then, each of feature extraction layers extracts sequentially features such as type, direction, stroke, and connection, respectively. Type features for direction and connection are extracted by the type feature extraction layer, direction features for stroke by the direction feature extraction layer and stroke and connection features for stroke by the direction feature extraction layer and stroke and connection features for the recongnition of character by the stroke and the connection feature extractions layers, respectively. The stroke and connection features are saved in the sequential buffer layer sequentially and using these features the characters are recognized in the output layer. The recognition results of this system by tests with 8 single consonants and 6 single vowels are promising.

• 한국지능시스템학회논문지
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• 제19권5호
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• pp.629-634
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• 2009

실시간 시스템 적용에 있어서, 수동 소나 표적의 식별을 위한 특징정보 추출 및 스코어링 알고리즘은 다음과 같은 문제점들을 가지고 있다. 즉, 주파수 스펙트럼으로부터 PSR(Propeller Shaft Rate) 및 BR(Blade rate) 등의 특징정보를 실시간으로 구별하는 것은 매우 어렵기 때문에 정확하고 효율적인 특징정보 추출(extraction)법을 요구한다. 또한, 추출된 특징정보들로 구성된 식별 DB(DataBase)는 잡음 및 불완전한 구성을 갖기 때문에 강인하고 효과적인 특징정보 스코어링(scoring)법을 요구한다. 나아가, 구조와 파라메터에 있어서 용이한 설계 절차를 요구한다. 이러한 문제들을 해결하기 위해서 진화 전략(ES : Evolution Strategy) 및 퍼지(fuzzy) 이론을 이용하는 지능형 특징정보 추출 및 스코어링 알고리즘이 제안되었다. 제안된 알고리즘의 성능을 검증하기 위해서는 수동 소나 표적의 실시간 식별이 수행되었다. 시뮬레이션 결과는 제안된 알고리즘이 실시간 시스템 적용에서 존재하는 문제점들을 효과적으로 해결할 수 있음을 보여준다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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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.

• 품질경영학회지
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• 제36권2호
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• pp.36-44
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• 2008

In this paper a novel feature extraction and selection is carried out in order to improve the discriminating capability between healthy and damaged structure using vibration signals. Although many feature extraction and selection algorithms have been proposed for vibration signals, most proposed approaches don't consider the discriminating ability of features since they are usually in unsupervised manner. We proposed a novel feature extraction and selection algorithm selecting few wavelet coefficients with higher class discriminating capability for damage detection and class visualization. We applied three class separability measures to evaluate the features, i.e. T test statistics, divergence, and Bhattacharyya distance. Experiments with vibration signals from truss structure demonstrate that class separabilities are significantly enhanced using our proposed algorithm compared to other two algorithms with original time-based features and Fourier-based ones.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.137-140
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• 1996

Finding line segments in an intensity image has been one of the most fundamental issues in computer vision. In complex scenes, it is hard to detect the locations of point features. Line features are more robust in providing greater positional accuracy. In this paper we present a robust "line features extraction" algorithm which extracts line feature in a single pass without using any assumptions and constraints. Our algorithm consists of five steps: (1) edge scanning, (2) edge normalization, (3) line-blob extraction, (4) line-feature computation, and (5) line linking. By using edge scanning, the computational complexity due to too many edge pixels is drastically reduced. Edge normalization improves the local quantization error induced from the gradient space partitioning and minimizes perturbations on edge orientation. We also analyze the effects of edge processing, and the least squares-based method and the principal axis-based method on the computation of line orientation. We show its efficiency with some real images.al images.