• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 춘계학술발표대회
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• pp.106-109
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• 2009

Recently, image matching becomes important in Computer Aided Diagnosis (CAD) due to the huge amount of medical images. Specially, texture feature is useful in medical image matching. However, texture features such as co-occurrence matrices can't describe well the spatial distribution of gray levels of the neighborhood pixels. In this paper we propose a frequency domain-based texture feature extractor that describes the local spatial distribution for medical image retrieval. This method is based on 2D Local Discrete Fourier transform of local images. The features are extracted from local Fourier histograms that generated by four Fourier images. Experimental results using 40 classes Brodatz textures and 1 class of Emphysema CT images show that the average accuracy of retrieval is about 93%.

• 자원환경지질
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• 제28권5호
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• pp.527-533
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• 1995

A disadvantage of Fourier analysis is that frequency information can only be extracted for the complete duration of a signal f(t). Since the Fourier transform integral extends over all time, from $-{\infty}$ to $+{\infty}$), the information it provides arises from an average over the whole length of the signal. If there is a local oscillation representing a particular feature, this will contribute to the calculated Fourier transform $F({\omega})$, but its location on the time axis will be lost There is no way of knowing whether the value of $F({\omega})$ at a particular ${\omega}$ derives from frequencies present throughout the life of f(t) or during just one or a few selected periods. This disadvantage is overcome in wavelet analysis which provides an alternative way of breaking a signal down into its constituent parts. The main advantage of the wavelet transform over the conventional Fourier transform is that it can not only provide the combined temporal and spectral features of the signal, but can also localize the target information in the time-frequency domain simultaneously. The wavelet transform distinguishes itself from Short Time Fourier Transform for time-frequency analysis in that it has a zoom-in and zoom-out capability.

• 한국소음진동공학회논문집
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• 제15권3호
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• pp.266-271
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• 2005

Although time-frequency analysis is useful for dispersive wave analysis, conventional methods such as the short-time Fourier transform do not take the dispersion phenomenon into consideration in the tiling of the time-frequency domain. The objective of this paper is to develop an adaptive time-frequency analysis method whose time-frequency tiling is determined with the consideration of signal dispersion characteristics. To achieve the adaptive time-frequency tiling, each of time-frequency atoms is rotated in the time-frequency plane depending on the local wave dispersion. To carry out this adaptive time-frequency transform, dispersion characteristics hidden in a signal are first estimated by an iterative scheme. To examine the effectiveness of the present method, the flexural wave signals measured in a plate were analyzed.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.606-610
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• 2004

Although time-frequency analysis is useful for dispersive wave analysis, conventional methods such as the short-time Fourier transform do not take the dispersion phenomenon into consideration in the tiling of the time-frequency domain. The objective of this paper is to develop an adaptive time-frequency analysis method whose time-frequency tiling is determined with the consideration of signal dispersion characteristics. To achieve the adaptive time-frequency tiling, each of time-frequency atoms is rotated in the time-frequency plane depending on the local wave dispersion. To carry out this adaptive time-frequency transform, dispersion characteristics hidden in a signal are first estimated by an iterative scheme. To examine the effectiveness of the proposed method, the flexural wave signals measured in a plate were analyzed.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문초록집
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• pp.363.1-363
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• 2002

This paper presents that the Wavelet Transform can be used to detect the various local defects in a gearbox. Two types of defects which are broken tooth and localized wear, are experimented and the signals are collected by accerometer and acoustic sensors and analyized. Because of the complecity of the signals acquired form sensors, it is needed to identify the interesting signal. (omitted)

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.617-622
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• 2002

This paper presents that the Wavelet Transform can be used to detect the various local defects in a gearbos. Two types of defects which are broken tooth and localized wear, are experimented and the signals are collected by accerometer and analyzed. Because of the complecity of the signals acquired from sensor, it is needed to identify the interesting signal. The natural frequencies of shafts and the gear mesh frequency(GMF) is calculated theretically. DWT, CWT and the aplication are used to extract a gear-localized defect feature from the vibration signal of the gearbox with the defective gear. The results shows the transform is more effective to detect the failures than the Fourier Transform.

• Journal of Information Processing Systems
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• 제12권4호
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• pp.555-576
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• 2016

In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

• 대한전자공학회논문지SD
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• 제40권5호
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• pp.298-311
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• 2003

Iterative Fourier transform algorithm (IFTA)은 회절광학소자 (DOE)의 위상 분포를 구하기 위한 반복적 수치 해석 알고리즘으로서 회절광학소자의 위상 분포는 반복 과정을 통하여 국소 최적해로 수렴하게 된다. Ink의 수렴은 위상 분포 초기치, 관측면에서의 자유도의 허용 범위 및 알고리즘에 내재된 매개 변수들의 설정 값에 영향을 받는다. 본 논문에서는 IFTA의 내부적 매개 변수인 완화 변수(relaxation parameter)가 IFTA의 수렴에 미치는 영향을 분석하고 이를 토대로 보다 정확한 최적화 해를 얻기 위한 유전 알고리즘과 IFTA의 하이브리드 알고리즘을 제안한다.

• 대한전자공학회논문지SP
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• 제44권1호
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• pp.10-16
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• 2007

본 논문에서는 색상의 공간적인 상관관계와 질감 모멘트를 이용한 내용기반 영상 검색 기법을 제안한다. 이를 위해, 색상의 공간적인 상관관계를 표현하는 새로운 색상 기술자를 제안하고, 또한 제안된 색상 기술자와 국부적인 푸리에 변환에 기반한 질감 특성을 결합한 영상 검색 방법을 제안한다. 일반적으로 색상의 공간적인 상관관계를 표현하기 위해서 컬러 코렐로그램(color correlogram)이 사용되고 있다. 하지만 컬러 코렐로그램은 중심화소에 따른 이웃한 화소들의 색상 분포를 확률적으로 잘 나타내는 장점이 있지만, 색상의 구조적인 정보를 표현하지 못하는 단점이 있다. 그러므로 본 논문에서는 색상의 분포와 구조적인 정보를 표시할 수 있는 새로운 색상 기술자를 제안한다. 제안하는 새로운 색상 기술자는 중심 화소와 이웃 화소들과의 색상 거리를 계산한 후 최소 거리의 색상과 최대 거리의 색상을 추출한 후 최소-최대 색상 쌍이 이루는 각에 대한 각각의 빈도수를 계산한다. 그런 다음, 각각의 이루는 각에 대해서 최소 거리 색상에 대한 최대 거리 색상들의 평균값과 분산값으로 구성된 새로운 기술자(min-max color correlation descriptor, MMCCD)를 생성한다. 제안한 색상 기술자를 이용하여 검색한 결과는 기존 방법들과 비교했을 경우 정확률에서 최소 5.2%에서 최대 13.21% 향상된 검색 결과를 확인할 수 있었다. 또한, 국부적인 푸리에 변환에 기반한 질감 기술자를 새로운 색상 기술자와 결합하여 특징 벡터의 크기를 절반으로 줄이면서도 새로운 색상 기술자만을 사용할 경우와 비교하여 향상된 검색 결과를 확인할 수 있었다.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.225-232
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• 2017

Understanding actions in videos is an important task. It helps in finding the anomalies present in videos such as fights. Detection of fights becomes more crucial when it comes to sports. This paper focuses on finding fight scenes in Hockey sport videos using blur & radon transform and convolutional neural networks (CNNs). First, the local motion within the video frames has been extracted using blur information. Next, fast fourier and radon transform have been applied on the local motion. The video frames with fight scene have been identified using transfer learning with the help of pre-trained deep learning model VGG-Net. Finally, a comparison of the methodology has been performed using feed forward neural networks. Accuracies of 56.00% and 75.00% have been achieved using feed forward neural network and VGG16-Net, respectively.