• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.647-650
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• 1999

In this Paper, we propose an optimal feature extraction for multiclass problems through proper choice of initial feature vectors. Although numerous feature extraction algorithms have been proposed, those algorithms are not optimal for multiclass problems. Recently, an optimal feature extraction algorithm for multiclass problems has been proposed, which provides a better performance than the conventional feature extraction algorithms. In this paper, we improve the algorithm by choosing good initial feature vectors. As a result, the searching time is significantly reduced. The chance to be stuck in a local minimum is also reduced.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.404-406
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• 2002

In this paper, the method of image feature extraction is proposed. This method employ the energy field analysis, outlier removal algorithm and ring projection. Using this algorithm, we achieve rotation-translation-scale invariant feature extraction. The force field are exploited to automatically locate the extrema of a small number of potential energy wells and associated potential channels. The image feature is acquired from relationship of local extrema using the ring projection method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2108-2111
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• 2003

This paper introduces a new local feature extraction method and image matching method for the localization and classification of targets. Proposed method is based on the block-by-block projection associated with directional pattern of blocks. Each pattern has its own eigen-vertors called as CEBs(Classified Eigen-Blocks). Also proposed block-based image matching method is robust to translation and occlusion. Performance of proposed feature extraction and matching method is verified by the face localization and FLIR-vehicle-image classification test.

• IEIE Transactions on Smart Processing and Computing
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• 제5권3호
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• pp.153-163
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• 2016

Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

• 한국멀티미디어학회논문지
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• 제12권1호
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• pp.50-57
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• 2009

본 논문은 3차원 다각형 모델에서 특징 선을 추출하기 위한 방법에 대해 제안한다. 이산 곡면으로 이루어진 다각형 모델에서 특징 선을 추출하기 위하여 기존 방법에서는 전역적인 음함수 곡면 맞춤 기법(Implicit Surface Fitting)을 이용하여 모델의 꼭지점에서 곡률과 곡률 미분 값을 측정하였다. 이러한 방법은 다각형 모델의 꼭지점에서 음함수 곡면으로 정확하게 투영할 수 있도록 사용자의 정의 파라미타를 찾아야 하며, 특징 추출을 위한 많은 계산 시간을 요구한다. 그러나 제안 방법은 지역적 음함수 곡면 맞춤 기법을 이용하여 모델의 꼭지점에 근사된 곡면을 통해 미분 정보를 측정한다. 측정된 미분 정보를 통해 쉽게 각각의 모서리에서 제로-클로싱을 통해 특징 점을 추출하고, 곡률 방향을 따라 추출된 점들을 연결하여 특징 선을 생성한다. 여러 가지 다각형 모델에서 실험을 하였고 기존 방법보다 빠르며 높은 품질의 특징 선을 추출한다.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.834-842
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• 2015

We propose a method to construct composite feature vector based on discriminant analysis for face recognition. For this, we first extract the holistic- and local-features from whole face images and local images, which consist of the discriminant pixels, by using a discriminant feature extraction method. In order to utilize both advantages of holistic- and local-features, we evaluate the amount of the discriminative information in each feature and then construct a composite feature vector with only the features that contain a large amount of discriminative information. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed composite feature vector has improvement of face recognition performance.

• 대한전자공학회논문지SP
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• 제40권6호
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• pp.1-9
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• 2003

본 논문에서는 지문 영상의 컨벡스 (convex) 구조를 이용하여 특징점을 추출하는 방법을 제안하였다. 지문영상에서 융선은 일정한 방향성을 가지며 융선의 단면은 주기성이 있는 사인파로 간주할 수 있다. 사인파 신호에서 국부 최대 위치를 검출함으로써 대략적인 한 화소 단위의 융선 추출이 가능하며 사인파 신호의 볼록한 컨벡스는 융선 영역에 해당한다. 이러한 지문의 특징을 이용하여 특징점을 효과적으로 찾는 방법을 제안한다. 이 과정에서 파라미터를 없애고 계산량을 줄임으로써 다양한 환경의 시스템에 적용 가능함을 보였다.

• 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.

• 한국군사과학기술학회지
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• 제20권1호
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• pp.40-47
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• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2485-2492
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• 2018

The rapid development of large-scale integrated circuits has brought great challenges to the circuit testing and diagnosis, and due to the lack of exact fault models, inaccurate analog components tolerance, and some nonlinear factors, the analog circuit fault diagnosis is still regarded as an extremely difficult problem. To cope with the problem that it's difficult to extract fault features effectively from masses of original data of the nonlinear continuous analog circuit output signal, a novel approach of feature extraction and dimension reduction for analog circuit fault diagnosis based on wavelet packet decomposition, local linear embedding algorithm, and clone selection algorithm (WPD-LLE-CSA) is proposed. The proposed method can identify faulty components in complicated analog circuits with a high accuracy above 99%. Compared with the existing feature extraction methods, the proposed method can significantly reduce the quantity of features with less time spent under the premise of maintaining a high level of diagnosing rate, and also the ratio of dimensionality reduction was discussed. Several groups of experiments are conducted to demonstrate the efficiency of the proposed method.