• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.258-267
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• 2002

We present an image retrieval method for improving retrieval performance by effective fusion of entropy features in wavelet region and wavelet moments. In this method, entropy features are sensitive to the local variation of gray level and well extract valley and edges. These features are effectively applied to contend-based image retrieval by well fusing to wavelet moments that represent texture property in multi-resolution. In order to evaluate the performance of the proposed method. We use Corel Draw Photo DB. Experiment results show that the proposed yields 11% better performance for Corel Draw Photo DB over wavelet moments method.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.632-639
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• 2007

This paper presents a wavelet-based watermarking algorithm to securely hide biometric features such as face and fingerprint and effectively extract them with less distortion of the concealed data. To hide the biometric features, we proposed a determination method of insert location based on wavelet transform and adaptive weight method according to the image characteristics. The hidden features are effectively extracted by applying the inverse wavelet transform to the watermarked image. To show the effectiveness, we analyze the various performance such as PSNR and correlation of watermark features before and after applying watermarking. Also, we evaluate the effect of watermaking algorithm with respect to biometric system such as recognition rate. Recognition rate shows 98.67% for multimodal biometric systems consisted of face and fingerprint. From these, we confirm that the proposed method makes it possible to effectively hide and extract the biometric features without lowering recognition rate.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.8-18
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• 2008

In this paper, we propose an efficient algorithm using wavelet transform for still image database retrieval. Especially, it uses only the lowest frequency sub-band in multi-level wavelet transform so that a retrieval system uses a smaller quantity of memory and takes a faster processing time. We extract different textured features, statistical information such as mean, variance and histogram, from low frequency sub-band. Then we measure the distances between the query image and the images in a database in terms of these features. To obtain good retrieval performance, we use the first feature (mean and variance of wavelet coefficients) to filter out most of the unlikely images. The rest of the images are considered to be candidate images. Then we apply the second feature (histogram of wavelet coefficient) to rank all the candidate images. To evaluate the algorithm, we create various distorted image databases using MIT VisTex texture images and PICS natural images. Through simulations, we demonstrate that our method can achieve performance satisfactorily in terms of the retrieval accuracy as well as the both memory requirement and computational complexity. Therefore it is expected to provide good retrieval solution for JPEG-2000 using wavelet transform.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.667-674
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• 2004

Extraction of texts from images is a fundamental and important problem to understand the images. This paper suggests a text region verification method by statistical means of stroke features of the characters. The method extracts 36 dimensional features from $16\times16$sized text and non-text images using wavelet transform - these 36 dimensional features express stroke and direction of characters - and select 12 sub-features out of 36 dimensional features which yield adequate separation between classes. After selecting the features, SVM trains the selected features. For the verification of the text region, each $16\times16$image block is scanned and classified as text or non-text. Then, the text region is finally decided as text region or non-text region. The proposed method is able to verify text regions which can hardly be distin guished.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.592-594
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• 2003

이 논문에서는 wavelet과 sobel filter를 사용하여 영상의 객관적인 평가 점수를 계산하는 새로운 기법을 제안한다. 이 기법은 orthogonal wavelet 변환을 기초로 하고 있으며 원본 영상과 처리된 영상 데이터가 모두 가용하다는 것을 전제로 한다. Wavelet을 이용해 주파수에 따라 분할된 영상 정보를 이용해 각각의 부영역 별 차영상을 획득하고 이 획득된 영상의 에너지를 이용해 화질 평가 수치를 계산한다. 부영역 별로 획득된 영상은 일정한 크기의 블록으로 분할되어 동일한 블록 내에서 가용한 영상의 특징에 관한 정보(contrast, edge 영역의 분포 정도) 벡터와 내적하여 새로운 특징 벡터로 사용되고, 이 특징 벡터의 가중치를 최적화하여 높은 상관도의 화질평가 점수를 산출하게 된다.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.127-130
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• 2000

In this paper, we explore the possibility to use wavelet decomposition based on modified octave structured 5-level filter banks as a set of features for speech recognition. The HMM (Hidden Markov Model) is used as a recognizer 〔l〕. We compared the performance of the wavelet decomposition with the mel-cepstrum and LPC cepstrum. Experimental results show favorable results.

• Journal of Korean Society for Quality Management
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• v.36 no.2
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• pp.36-44
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• 2008

In this paper a novel feature extraction and selection is carried out in order to improve the discriminating capability between healthy and damaged structure using vibration signals. Although many feature extraction and selection algorithms have been proposed for vibration signals, most proposed approaches don't consider the discriminating ability of features since they are usually in unsupervised manner. We proposed a novel feature extraction and selection algorithm selecting few wavelet coefficients with higher class discriminating capability for damage detection and class visualization. We applied three class separability measures to evaluate the features, i.e. T test statistics, divergence, and Bhattacharyya distance. Experiments with vibration signals from truss structure demonstrate that class separabilities are significantly enhanced using our proposed algorithm compared to other two algorithms with original time-based features and Fourier-based ones.

• Journal of Power Electronics
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• v.9 no.3
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• pp.377-385
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• 2009

In this paper, a diagnosis method for switch open-circuit faults in three-phase PWM inverters is proposed, which employs support vector machine (SVM) as classifying method. At first, a discrete wavelet transform (DWT) is used to detect a discontinuity of currents due to the fault, and then the features for fault diagnosis are extracted. Next, these features are employed as inputs for the SVM training. After training, the SVM produces an optimized boundary which is used identifying the fault. Finally, the fault classification is performed online with instantaneous features. The experimental results have verified the validity of the proposed estimation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.28.5-28
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• 2001

This paper investigates a classification method of the electrocardiogram (ECG) into different disease categories. The features for the classification of the ECG are the coefficients of the discrete wavelet transform (DWT) of ECG signals. The coefficients are calculated with Haar wavelet, and after DWT we can get 64 coefficients. Each coefficient has morphological information and they may be good features when conventional time-domain features are not available. Since all of them are not meaningful, it is needed to reduce the size of meaningful coefficients set. The distributions of each coefficient can be the rules to classify ECG signal. The optimally reduced feature set is obtained by fuzzy c-means algorithm and rough set theory. First, the each coefficient is clustered by fuzzy c-means algorithm and the clustered ...

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2005.11a
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• pp.45-53
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• 2005

A novel method for evaluating the anisotropy of the deterministic features in a stochastic 2D data is introduced. The ability of the wavelet transform for the identification of the abrupt discontinuities could be used to characterize the boundary of the deterministic area in a 2D stochastic data, such as flocs in paper structure. The one-dimensional wavelet transform with a small-scale range in MD and CD could quantify the amount of the edge in both directions, depending on the intensity of each floc. The flocs that are aligned in the MD direction result in a higher value of local wavelet energy in the CD direction. Therefore, the ratio of the total wavelet energy in CD and MD directions can be used as a new anisotropy index. This index is a measure of the floc-orientation and can provide an excellent tool to obtain the orientation distribution and the major oriented angle of flocs. Various simulated images and real stochastic data such as local gloss variation of printed image and formation image, have been tested and the results show this analysis method is very reliable to measure the anisotropy of the deterministic features.