• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.724-726
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• 2004

본 논문에서는 말스버그가 주장한 가보 웨이블릿을 기반으로 하는 얼굴 인식과 표정에 강인한 얼굴 인식에 대한 내용을 소개하였다. 표정을 분류하는 방법론에 대한 연구는 활발한 편이지만, 유사한 표정을 지니는 타인에 대한 구분이라던가, 동일인의 다양한 표정을 한 사람으로 정확히 인식하는 연구는 전무한 실정이다. 본 논문에서는 얼굴을 구성하는 특징 중에서 표정에 가장 많이 영향을 받는 특징을 분석하기 위한 실험과정과 결과, 그리고 근거를 제시하였고, 그에 따르는 방법론에 대한 연구를 제안한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.106-109
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• 2019

최근 Convolutional Neural Network (CNN)에 영상이 아닌 비학습적 알고리즘으로부터 도출된 특징맵을 입력함으로써 영상처리 성능 및 계산자원 효율성 향상을 이룬 보고가 늘어나고 있다. 본 논문에서는 이러한 점을 바탕으로 가보웨이블릿 특징맵을 입력으로 하는 CNN 기반 영상잡음제거기를 제안하고 그 성능 및 특징을 고찰하였다. 즉 기존의 CNN 에서는 일반적인 영상을 입력하는 반면에 본 논문에서는 영상으로부터 추출한 웨이블릿 계수들을 입력하였고, 이를 통하여 기존의 방법에 비하여 성능을 유지하면서 계산량을 줄일 수 있는 가능성을 확인하였다.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1125-1134
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• 2007

In this paper, we propose a new approach for rotation invariant texture classification based on Gabor wavelet. Conventional methods have the low correct classification rate in large texture database. In our proposed method, we define two feature groups which are the global feature vector and the local feature matrix. The feature groups are output of Gabor wavelet filtering. By using the feature groups, we defined an improved discriminant and obtained high classification rates of large texture database in the experiments. From spectrum symmetry of texture images, the number of test times were reduced nearly 50%. Consequently, the correct classification rate is improved with $2.3%{\sim}15.6%$ values in 112 Brodatz texture class, which may vary according to comparison methods.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.745-747
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• 2004

본 논문에서는 여러 해상도로 입력되어지는 얼굴 이미지를 효율적으로 인식시키는 작업을 수행하는 방법에 대한 내용을 소개하고 있다. 정해지지 않은 예측이 불가능한 사람들이 드나드는 공공장소인 공항이나 항만 같은 곳에서의 얼굴인식은 고정된 크기가 아닌 다양한 크기와 조명을 갖는 등, 매우 많은 가지 수의 환경 변수를 가지고 있다. 이러한 환경에서의 얼굴인식은 그만큼 다양한 변수와 그 변수의 조건에 대한 대응을 요구하게 된다. 여기서 제안하는 방법은 다양한 해상도를 갖는 입력 얼굴 이미지에 대하여 최적의 가보 커널과 그에 따르는 적절한 파라미터를 찾는 것으로 효과적인 얼굴인식을 수행하는 방법을 제안한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.121-130
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• 2005

In this paper, we propose a feature extraction method for iris recognition using wavelet transforms. The wavelet transform is fast and has a good localization characteristic. In particular, the low frequency band can be used as an effective feature vector. In iris recognition, the noise caused by eyelid the eyebrow, glint, etc may be included in iris. The iris pattern is distorted by noises by itself, and a feature extraction algorithm based on filter such as Wavelets, Gabor transform spreads noises into whole iris region. Namely, such noises degrade the performance of iris recognition systems a major problem. This kind of noise has adverse effect on performance. In order to solve these problems, we propose to divide the iris image into a number of sub-region and apply the wavelet transform to each sub-region. Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform and region division noticeably improves recognition performance. However, it is noted that the processing time of the wavelet transform is much faster than that of the existing methods.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.26-32
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• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.10-18
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• 2011

Medical asset protection is important in each medical institution especially because of the law on private medical record protection and face recognition for this protection is one of the most interesting and challenging problems. In recognizing human faces, the distortion of face images can be caused by the change of pose, illumination, expressions and scale. It is difficult to recognize faces due to the locations of lights and the directions of lights. In order to overcome those problems, this paper presents an analysis of coefficients of Gabor wavelets, kernel decision, feature point, size of kernel, for face recognition in CCTV surveillance. The proposed method consists of analyses. The first analysis is to select of the kernel from images, the second is an coefficient analysis for kernel sizes and the last is the measure of changes in garbo kernel sizes according to the change of image sizes. Face recognitions are processed using the coefficients of experiment results and success rate is 97.3%. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed method. Accordingly, the satisfaction and the quality of services will be improved in the face recognition area.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.835-837
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• 2005

본 논문에서는 가보 웨이블릿을 통해 얼굴 이미지로부터 특징을 추출하고, 그에 Genetic Algorithm 을 통해 구한 특징점별 가중치를 적용하여 얼굴 인식을 하는 방법을 소개한다. 각 특징점별로 가중치를 적용하는 방법은, 기존의 Gabor wavelet 을 사용한 얼굴 인식 방법들에 비해 높은 인식률을 보인다. 특징점별 가중치들은 진화 알고리즘을 통해 학습 되어진다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5A
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• pp.421-431
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• 2009

This paper is the study on the verification of structural health monitoring (SHM) algorithm based on the ultrasonic guided wave. An active inspection system using Lamb wave (LW) for SHM was considered. The basic study about the application of this algorithm was performed for detecting the circular notch defect in steel plate. LW testing technique, pitch-catch method, was used for interpretation of circular notch defect with depth of 50% of plate thickness and 7 mm width. Damage characterization takes place by comparing $S_0$ mode sensor signals collected before and after the damage event. By subtracting the signals of both conditions from each other, a scatter signal is produced which can be used for damage localization. The continuous Gabor wavelet transform is used to attain the time between the arrivals of the scatter and sensor signals. A new practical damage monitoring algorithm, based on damage monitoring polygon and pitch-catch method, has been proposed and verified with good accuracy. The possible damage location can be estimated by the average on calculated location points and the damage extent by the standard deviation.