• 제어로봇시스템학회논문지
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• 제12권9호
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• pp.935-944
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• 2006

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMC pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMC signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure, and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time pattern recognition system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the generation of control commands for myoelectric hand, are completed within 97 msec. These results confirm that our method is applicable to real-time EMG pattern recognition far myoelectric hand control.

• 한국지능시스템학회논문지
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• 제17권2호
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• pp.252-258
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• 2007

In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2005년도 추계학술대회 논문집
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• pp.213-216
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• 2005

자동차 번호판의 이미지에서 번호의 추출이나, 직물 이미지에서 오염 또는 훼손부분의 추출 또는 방사성 폐기물이나 기타 독극물 보관함의 이미지에서 오염이나 산화에 의한 훼손부위 등과 같은 목표물 이미지 추출은 흑백 이미지에서 명암의 차이를 이용하는 것보다는 컬러 이미지에서 색상의 차이를 이용하는 것이 더 효율적일 때가 많으며, 특히 배경과 목표물의 명암차이가 크지 않은 경우에 효과적이다. 배경과 목표물이 갖는 색상의 차이를 이용하여 분리하기 위해서 적색(R), 녹색(G), 청색(B) 의 RGB 평면 또는 순도(H), 포화도(S), 휘도(I)를 사용하는 HSI 컬러 평면 등이 많이 사용되며, 이 때 배경과 목표물의 색상의 히스토그램을 구해보면 보면 많은 경우 유사한 색 정보가 배경과 목표물에 공통으로 포함되어 분리에 어려움을 겪게 된다. 본 논문에서는 Fisher 선형 분리자(Fisher's linear discriminant)[1] 함수를 이용하여 3차원의 색상 특징 벡터를 1차원 직선에 투사하여 변환된 1차원 공간상에서 복잡성을 줄이고 효과적으로 분류할 수 있는 기법을 제안하였으며, 이를 도축된 식용 가금류의 영상에 적용하고 변질된 부분이 포함되어 식용으로 사용할 수 없는 것들을 효과적으로 분류할 수 있음을 보였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.299-301
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• 2005

본 연구는 생체정보를 이용하여 개인을 인증하고 확인하기 위한 방법으로 기존 단일 생체인식 기법의 단점을 보완하기 위해 홍채와 얼굴을 이용한 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구하였다. 중국 홍채 데이터베이스 CASIA(Chinese Academy of Science)에 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 사용하여 특징벡터를 획득하였으며, FERET(FERET(Face Recognition Technology) 얼굴영상데이터를 사용하여 FERET 연구에서 매우 우수한 성능을 보인 EBGM알고리듬으로 특징벡터를 획득하였다. 이로부터 얻어진 두 score 값에 대하여 다양한 균등화 과정을 시도해 보았으며, 등록자와 침입자를 구분하기 위한 Fusion Algorithm으로 Bayesian Classifier, Support vector machine, Fisher's linear discriminant를 사용하였다. 또한, 널리 사용되는 방법 중 Weighted Summation을 이용하여 다중생체인식의 성능을 비교해 보았다.

• 대한음성학회지:말소리
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• 제66호
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• Journal of Multimedia Information System
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• 제2권1호
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• pp.171-178
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• 2015

This article describes 3 dimensional (3D) face recognition system using histogram of oriented gradients (HOG) based on face curvature. The surface curvatures in the face contain the most important personal feature information. In this paper, 3D face images are recognized by the face components: cheek, eyes, mouth, and nose. For the proposed approach, the first step uses the face curvatures which present the facial features for 3D face images, after normalization using the singular value decomposition (SVD). Fisherface method is then applied to each component curvature face. The reason for adapting the Fisherface method maintains the surface attribute for the face curvature, even though it can generate reduced image dimension. And histogram of oriented gradients (HOG) descriptor is one of the state-of-art methods which have been shown to significantly outperform the existing feature set for several objects detection and recognition. In the last step, the linear discriminant analysis is explained for each component. The experimental results showed that the proposed approach leads to higher detection accuracy rate than other methods.

• 대한산업공학회지
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• 제32권2호
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• pp.74-81
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• 2006

In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear discriminant functions. Toward the end, we develop a new $l_1$-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming (MILP) model. Given a finite number of discriminant functions as an input, the proposed model considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear decision surface that best separates the data on hand. Hence, exploiting powerful MILP solvers, the model efficiently analyzes any given data set for its piecewise nonlinear separability. The classification of four sets of artificial data demonstrates the aforementioned strength of the proposed model. Classification results on five machine learning benchmark databases prove that the data separation via the proposed MILP model is an effective supervised learning methodology that compares quite favorably to well-established learning methodologies.

• 한국통신학회논문지
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• 제31권10C호
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• pp.942-947
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• 2006

In this paper, a new feature representation technique called Improved 2-directional 2-dimensional direct linear discriminant analysis (Improved $(2D)^2$ DLDA) is proposed. In the case of face recognition, thesmall sample size problem and need for many coefficients are often encountered. In order to solve these problems, the proposed method uses the direct LDA and 2-directional image scatter matrix. Moreover the selection method of feature vector and the method of similarity measure are proposed. The ORL face database is used to evaluate the performance of the proposed method. The experimental results show that the proposed method obtains better recognition rate and requires lesser memory than the direct LDA.

• 한국정밀공학회지
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• 제14권10호
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• pp.135-140
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• 1997

The detection of cutting tool states in machining is important for the automation. The information of cutting tool states in metal cutting process is uncertain. Hence a industry needs the system which can detect the cutting tool states in real time and control the feed motion. Cutting signal features must be sifted before the classification. In this paper the Fisher's linear discriminant function was applied to the pattern recognition of the cutting tool states successfully. Cutting conditions and cutting force para- meters have shown to be sensitive to tool states, so these cutting conditions and cutting force paramenters can be used as features for tool state detection.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.274-277
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• 2000

Linear discriminant analysis (LDA) is a basic tool of pattern recognition, and it is used in extensive fields, e.g. face identification. However, LDA is poor at adaptability since it is a batch type algorithm. To overcome this, a new algorithm of online LDA is proposed in the present paper. It is experimentally shown that the new algorithm is about two times faster than the previously proposed algorithm.