• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.190-192
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• 2004

In this paper, we propose a low bit-rate embedded video compression scheme with 3-D block partition coding in the wavelet domain. The proposed video compression scheme includes multi-level 3-dimensional dyadic wavelet decomposition, raster scanning within each subband, formation of block, 3-D partitioning of block, and adaptive arithmetic entropy coding. Although the proposed video compression scheme is quit simple, it produces bit-stream with good features, including SNR scalability from the embedded nature. Experimental results demonstrate that the proposed video compression scheme is quit competitive to other good wavelet-based video coders in the literature.

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

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.133-140
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• 2006

Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.3
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• pp.121-129
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• 2001

In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)

• The Pure and Applied Mathematics
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• v.31 no.2
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• pp.179-187
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• 2024

In image and signal processing, the wavelet transform is frequently employed. However, it has the drawback of having weak directionality, which has limited its use in many applications. A recent addition to the wavelet transform, the curvelet transform attempts to address crossing phenomena that occur along curved edges in 2-D images. As an extension of the wavelet transform, we discuss various curvelet transform features in this paper. There are numerous uses for the curvelet and wavelet transforms in image and signal processing.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.236-241
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• 2010

This paper presents a wear diagnosis method for centrifugal impellers by using an accelerometer. The features are calculated from raw and wavelet transformed signals with several statistical methods applied in time or frequency domains. From the effectiveness coefficient test, it is shown that 7th level of wavelet transformed signal is suitable for wear classification problems. A neural network with 5 feature sets is applied to diagnose the wear magnitude of pump impellers. The verification result reveals that high accuracy for the wear diagnosis of impellers can be obtained by using wavelet features transformed from acceleration signals.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.457-469
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• 2003

In this paper, we have proposed the efficient algorithm that can segment the video scene change using a various statistical characteristics obtained from by applying the wavelet transformation for each frames. Our method firstly extracts the histogram features from low frequency subband of wavelet-transformed image and then uses these features to detect the abrupt scene change. Second, it extracts the edge information from applying the mesh method to the high frequency subband of transformed image. We quantify the extracted edge information as the values of variance characteristic of each pixel and use these values to detect the gradual scene change. And we have also proposed an algorithm how extract the proper key frame from segmented video scene. Experiment results show that the proposed method is both very efficient algorithm in segmenting video frames and also is to become the appropriate key frame extraction method.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.143-148
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• 2008

A real-time face detection is to find human faces robustly under the cluttered background free from the effect of occlusion by other objects or various lightening conditions. We propose a face detection system for real-time applications using wavelet decomposition method based on Gabor features. Firstly, skin candidate regions are extracted from the given image by skin color filtering and projection method. Then Gabor-feature based template matching is performed to choose face cadidate from the skin candidate regions. The chosen face candidate region is transformed into 2-level wavelet decomposition images, from which feature vectors are extracted for classification. Based on the extracted feature vectors, the face candidate region is finally classified into either face or nonface class by the Levenberg-Marguardt back-propagation neural network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4118-4136
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• 2014

To preserve the spatial consistency of low-level features, generalized Riesz-wavelet transform (GRWT) is adopted for fusing multi-modality images. The proposed method can capture the directional image structure arbitrarily by exploiting a suitable parameterization fusion model and additional structural information. Its fusion patterns are controlled by a heuristic fusion model based on image phase and coherence features. It can explore and keep the structural information efficiently and consistently. A performance analysis of the proposed method applied to real-world images demonstrates that it is competitive with the state-of-art fusion methods, especially in combining structural information.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.5
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• pp.211-217
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• 2001

This paper proposes a new fault detection criterion logic that extracts the features of magnetizing inrush and internal faults by making use of Daubechies Wavelet Transform which analyzes distinct features. To prove the effectiveness of proposed method, the paper constructs power system model including power transformer by using EMTP, and collects data through simulation using various fault inception angle and magnetizing inrush. The conclusions implemented by the C program and the Wavemenu of MATLAB Toolbos are more effective and simpler to distinguish inter-turn faults from magnetizing inrush states.