• 한국멀티미디어학회논문지
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• 제5권4호
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• pp.415-425
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• 2002

본 논문에서는 웨이브릿 변환 영역에서 추출된 특징을 기반으로 한 내용기반 영상검색 방법에 관해 연구하였다. 기존의 웨이브릿 기반의 방법에서의 문제점인 특징벡터의 크기를 줄이기 위해 웨이브릿 계수의 영역별 에너지 값을 이용하였으며, 대상물의 이동, 회전, 크기 변화에 영향을 받지 않는 모멘트 특성을 이용한 검색방법을 제안하였다. 본 방법은 특징벡터의 크기를 줄이고, 기존의 특징벡터와 비교해서 검색시간을 단축하면서 분류검색의 효율성을 향상시켰다. 영역기반 영상검색 기능을 제공하기 위해 영상분할 방법에 대해 연구하였으며, 불규칙한 광원에 의한 영향을 최소화할 수 있는 영상분할 방법을 제안하였다 영상분할은 영역병합을 이용하였고, 병합후보영역은 웨이브릿 변환의 고주파 대역 에너지 값을 이용하여 선정하였다 분할된 영역정보를 이용하여 칼라와 질감, 모양 특징벡터를 구성하여 영역기반 영상검색을 수행하였다.

• 정보처리학회논문지B
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• 제10B권4호
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• pp.459-464
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• 2003

본 논문에서 제안한 위치 이동에 무관한 웨이블렛 변환을 이용한 홍채 인식 방법은 영상 획득 장비에 의해 획득한 사용자의 눈 영상에 대하여 홍채 영역만을 추출하기 위한 전처리를 수행하고 전처리를 거친 홍채 영상에 의하여 사용자의 신원을 식별하는데 있어서 홍채 영상의 기울어짐 및 이동 문제를 해결하였다. 이를 위해서 일반적인 웨이블렛을 사용하는 대신, 위치 이동에 무관한 웨이블렛 변환을 통하여 최적의 특징값을 추출한후, 이를 코드화하여 저장한 후, 비교하여 본인 여부를 식별하였다. 실험결과 제안된 방법으로 생성된 특징 벡터와 기존에 등록된 특징 벡터의 일치도 측정에 있어서 종래의 웨이블렛 변환 홍채 인식 방법보다 오인식률(FAR) 및 오거부율(FRR)이 현저하게 감소하였다.

• 대한원격탐사학회지
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• 제28권6호
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• pp.623-632
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• 2012

In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.327-344
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• 2013

Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.733-736
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• 1998

In this paper, we propose a new method for content-based retrieval system using wavelet transform and correlation, which has were used in signal processing and image compressing. The matching method is used not perfect matching but similar matching. Used feature vector is the lowest frequency(LL) itself, energy value, and edge information of 4-layer, after computng a 4-layer 2-D fast wavelet transform on image. By the proosed algorithm, we got the result that was faste rand more accurate than the traditional algorithm. Because used feature vector was compressed 256:1 over original image, retrieval speed was highly improved. By using correlation, moving object with size variation was reterieved without additional feature information.

• 한국안전학회지
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• 제20권3호
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• pp.64-70
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• 2005

In this literature, fault detection and classification of faulty induction motors are carried out through Z-index and frequency analysis. Above frequency analysis refer Fourier transformation and Wavelet transformation. Z-index is defined as the similar form of energy function, also the faulty and healthy conditions are classified through Z-index. For the detection and classification feature extraction for the fault detection of an induction motor is carried out using the information from stator current. Fourier and Wavelet transforms are applied to detect the characteristics under the healthy and various faulty conditions. We can obtain feature vectors from two transformations, and the results illustrate that the feature vectors are complementary each other.

• Journal of Information Processing Systems
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• 제13권6호
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• pp.1628-1639
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• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 1998년도 춘계학술발표논문집
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• pp.34-39
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• 1998

영상검색을 위해 Wavelet 변환을 사용한 특징추출 접근방법은 영상들을 압축과 동시에 인덱스 할 수 있어서 영상 데이터베이스 저장과 관리의 복잡성이 상당히 감소될 수 있다. 본 연구는 각 영상의 Hue값에 대해 위치 정보의 주파수 정보를 가지는 Wavelet 변환의 성질을 이용하여 2단계 Wavelet 변환 후 생성된 저대역 부밴드에서 칼라 특징을 추출하고 나머지 부밴드에서 질감 특징을 추출하여 영상 데이터베이스의 검색에 이용한다. 200개 영상을 사용하여 실험한 결과, 제안된 방법은 recall과 precision에서 약 97%, 81%의 검색 효율을 보였다.

• 한국해양공학회지
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• 제28권6호
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• pp.552-559
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• 2014

In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.

• 한국산학기술학회논문지
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• 제11권11호
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• pp.4568-4575
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• 2010

홍채인식은 홍채의 무늬 패턴 정보를 이용하는 생체인식 기술로 안정성, 보안성과 같은 특징을 가지고 있기 때문에 높은 보안을 요구하는 환경에 특히 적합하다. 최근 들어 홍채정보를 이용하여 출입통제, 정보보안등의 분야에 많이 활용되고 있다. 홍채 특징 추출시 크기, 조명, 회전에 무관한 홍채 특징을 추출하는 것이 바람직하다. 홍채크기 및 조명 문제는 전처리를 통해 쉽게 해결할 수 있지만 회전에 무관한 홍채 특징 추출은 여전히 문제가 된다. 본 논문에서는 회전 보정으로 인한 인식률 및 속도 저하를 개선하기 위해 Zernike 모멘트와 Daubechies Wavelet을 이용한 홍채인식 방법을 제안한다. 제안한 방법은 회전에 불변한 Zernike 모멘트의 통계적 특성을 이용하여 회전된 홍채에 대해서 1단계로 유사홍채를 분류함으로서 홍채인식에 필요한 시간을 단축하였고, 인식성능 역시 기존 방법과 대등함을 보였다. 따라서 제안한 방법이 대용량의 홍채 인식 시스템에 효과적인 적용이 가능함을 확인할 수 있었다.