• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.215-218
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• 2003

The method of vocal tract normalization has been known as a successful method for improving the accuracy of speech recognition. A frequency warping procedure based low complexity and maximum likelihood has been generally applied for vocal tract normalization. In this paper, we propose a new power spectrum warping procedure that can be improve on vocal tract normalization performance than a frequency warping procedure. A mechanism for implementing this method can be simply achieved by modifying the power spectrum of filter bank in Mel-frequency cepstrum feature(MFCC) analysis. Experimental study compared our Proposal method with the well-known frequency warping method. The results have shown that the power spectrum warping is better 50% about the recognition performance than the frequency warping.

• ETRI Journal
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• v.38 no.6
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• pp.1190-1196
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• 2016

We propose a new bandpass filter (BPF)-based online channel normalization method to dynamically suppress channel distortion when the speech and channel noise components are unknown. In this method, an adaptive modulation frequency filter is used to perform channel normalization, whereas conventional modulation filtering methods apply the same filter form to each utterance. In this paper, we only normalize the two mel frequency cepstral coefficients (C0 and C1) with large dynamic ranges; the computational complexity is thus decreased, and channel normalization accuracy is improved. Additionally, to update the filter weights dynamically, we normalize the learning rates using the dimensional power of each frame. Our speech recognition experiments using the proposed BPF-based blind channel normalization method show that this approach effectively removes channel distortion and results in only a minor decline in accuracy when online channel normalization processing is used instead of batch processing

• Journal of Applied Reliability
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• v.18 no.1
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• pp.49-55
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• 2018

Purpose: To propose improved method for normalization, compare to the de facto international standard which is IESNA TM-21 or conventional normalization methods. Methods: Firstly, we analysed conventional methods and specified the problem of normalization method which is based on first measured data. Secondly, we proposed our approach which is based on the design specification. Lastly, we studied a real degradation data which is conducted for 15,000 hours. Conclusion: Proposed normalization method is better approach because it can reflect real data and design specification, and reduce distortion when analysing degradation data. Also, It is appliable to other long-life reliability items.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.323-330
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• 2019

Radiometric normalization with multi-temporal satellite images is essential for time series analysis and change detection. Generally, relative radiometric normalization, which is an image-based method, is performed, and histogram matching is a representative method for normalizing the non-linear properties. However, since it utilizes global statistical information only, local information is not considered at all. Thus, this paper proposes a histogram matching method considering local information. The proposed method divides histograms based on density, mean, and standard deviation of image intensities, and performs histogram matching locally on the sub-histogram. The matched histogram is then further partitioned and this process is performed again, iteratively, controlled with the wasserstein distance. Finally, the proposed method is compared to global histogram matching. The experimental results show that the proposed method is visually and quantitatively superior to the conventional method, which indicates the applicability of the proposed method to the radiometric normalization of multi-temporal images with non-linear properties.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2385-2388
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• 2005

In this paper, we propose the estimation method for the image affine information for computer vision. The first estimation method is given based on the XYS image normalization and the second estimation method is based on the image normalization by Pei and Lin. The XYS normalization method turns out to have better performance than the method by Pei and Lin. In addition, we show that rotation and aspect ratio information can be obtained using the central moments of both the original image and the sensed image. Finally, we propose the modified version of the normalization method so that we may control the size of the image.

• Genomics & Informatics
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• v.2 no.2
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• pp.92-98
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• 2004

A cDNA microarray experiment is one of the most useful high-throughput experiments in medical informatics for monitoring gene expression levels. Statistical analysis with a cDNA microarray medical data requires a normalization procedure to reduce the systematic errors that are impossible to control by the experimental conditions. Despite the variety of normalization methods, this. paper suggests a more general and synthetic normalization algorithm with a control gene set based on previous studies of normalization. Iterative normalization method was used to select and include a new control gene set among the whole genes iteratively at every step of the normalization calculation initiated with the housekeeping genes. The objective of this iterative normalization was to maintain the pattern of the original data and to keep the gene expression levels stable. Spatial plots, M&A (ratio and average values of the intensity) plots and box plots showed a convergence to zero of the mean across all genes graphically after applying our iterative normalization. The practicability of the algorithm was demonstrated by applying our method to the data for the human photo aging study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.515-523
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• 2018

In order to obtain consistent change detection result for multi-temporal satellite images, preprocessing must be performed. In particular, the preprocessing related to the spectral values can be performed by the radiometric normalization, and relative radiometric normalization is generally utilized. However, most relative radiometric normalization methods assume a linear relationship between the two images, and nonlinear spectral characteristics such as phenological differences are not considered. Therefore, this study proposes a relative radiometric normalization which assumes nonlinear relationships that can perform compositive normalization of radiometric and phenological characteristics. The proposed method selects the subject and reference images, and then extracts the radiometric control set samples through the no-change method. In addition, spectral indexes as well as pixel values are extracted in order to consider sufficient information, and modeling of nonlinear relationships is performed through multilayer perceptron. Finally, the proposed method is compared with the conventional relative radiometric normalization methods, which shows that the proposed method is visually and quantitatively superior.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.267-271
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• 2002

The Hotelling transform is based on statistical properties of an image. The principal uses of this transform are in data compression. The basic concept of the Hotelling transform is that the choice of basis vectors pointing the direction of maximum variance of the data. This property can be used for rotation normalization. Many objects of interest in pattern recognition applications can be easily standardized by performing a rotation normalization that aligns the coordinate axes with the axes of maximum variance of the pixels in the object. However, this transform can not be used to rotation normalization of color images directly. In this paper, we propose a new method for rotation normalization of color images based on the Hotelling transform. The Hotelling transform is performed to calculate basis vectors of each channel. Then the summation of vectors of all channels are processed. Rotation normalization is performed using the result of summation of vectors. Experimental results showed the proposed method can be used for rotation normalization of color images effectively.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.21-28
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• 2015

Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale in each feature dimension and error rate in classification. Most of the existing normalization methods, however, do not use the class labels of data points and, as a result, do not guarantee the optimality of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature normalization to reduce classification error more. Training sample selection is a common technique for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.513-524
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• 2006

Normalization transform is very useful for finding the overall trend of the time-series data since it enables finding sequences with similar fluctuation patterns. The previous subsequence matching method with normalization transform, however, would incur index overhead both in storage space and in update maintenance since it should build multiple indexes for supporting arbitrary length of query sequences. To solve this problem, we propose a single index approach for the normalization transformed subsequence matching that supports arbitrary length of query sequences. For the single index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. The inclusion-normalization transform normalizes a window by using the mean and the standard deviation of a subsequence that includes the window. Next, we formally prove correctness of the proposed method that uses the inclusion-normalization transform for the normalization transformed subsequence matching. We then propose subsequence matching and index building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to $2.5{\sim}2.8$ times over the previous method. Our approach has an additional advantage of being generalized to support many sorts of other transforms as well as normalization transform. Therefore, we believe our work will be widely used in many sorts of transform-based subsequence matching methods.