• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.64-70
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• 2009

This paper presents a speech enhancement technique based on generalized Gamma distribution in order to obtain robust speech recognition performance. For robust speech enhancement, the noise estimation based on a spectral noise floor controled recursive averaging spectral values is applied to speech estimation under the generalized Gamma distribution and spectral gain floor. The proposed speech enhancement technique is based on spectral component, spectral amplitude, and log spectral amplitude. The performance of three different methods is measured by recognition accuracy of automatic speech recognition (ASR).

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.39-46
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• 2001

In this paper we propose a new method that can be estimation the spectral power distribution of the light source from three-band images. the light source is estimated by dividing the reflected spectral power distribution of the maximum achromatic region(L(λ)) by the corresponding surface reflectance(Ο(λ)). In order to obtain reflected spectral power distribution of the maximum achromatic region from three-bend images, a modified gray world assumption algorithm is adapted. And the maximum surface reflectance is estimated using the principal component analysis method along with achromatic population. The achromatic population is created from a set of given Munsell color chips whose chroma vector is less than threshold. Cumulative contribution ratio of principal components from the first to the third for classified achromatic population was about 99.75%. The reconstruction of illumination spectral power distribution by using achromatic population and three-band digital images captured under various light source was examined, and evaluated by RMSE between the original and reconstructed illumination spectral power distribution. This work was supported by grant No (2000-1-30200-005-3) from the Basic Research Program of the Korea Science & Engineering Foundation.

• Journal of the Korean Graphic Arts Communication Society
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• v.16 no.3
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• pp.121-131
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• 1998

The object color does not look same under the different light source. It depends on the surface spectral reflectance and the spectral distribution of light source. Therefore we should find the surface spectral reflectance of object color and the spectral distribution of light source for color reproduction. Using Wiener estimation, we can estimate the spectral reflectance from low dimensional images obtained with multi-band image acquisition system. The kind and the number of imaging filters have the effect on the estimation of the spectral reflectance. Therefore it is important that optimal filters are selected to minimize the error of the result. In this paper, we describe methods to select optimal filters with minimum error between measured and estimated surface spectral reflectance and to estimate surface spectral reflectance of Munsell color chart from six multi-band images by using Wiener estimation.

• Proceedings of the Korean Printing Society Conference
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• 1998.10a
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• pp.1-6
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• 1998

The object color does not look same under the different light source. It depends on the surface spectral reflectance and the spectral distribution of light source. Therefore we should find the surface spectral reflectance of object color and the spectral distribution of light source for color reproduction. Using Winer estimation, we can reconstruct the spectral reflectance from low dimensional images obtained with a few filters. The kind and the number of filters have the effect on the estimation of the spectral reflectance. Therefore it is important that optimal filters are selected to minimize the error of the result. In this paper, we describe methods to select optimal filters with minimum error between measured and estimated surface spectral reflectance and to estimate surface spectral reflectance of Munsell color from six band images by using Wiener estimation.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.465-474
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• 1998

Results on the analytic behavior to the limiting spectral distribution of matrices of sample convariance type. studied in Marcenko and Pastur [2] are derived. using the Stieltjes transform it is shown that the limiting distrbution has a continuous derivative away from zero the derivative being analytic whenever it is positive and the behavior of it resembles the behavior of a square root function near the boundary of its support.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.483-488
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• 2004

Results on the analytic behavior of the limiting spectral distribution of large dimensional random matrices, studied in Marcenko and Pastur [2], are derived. Using the Stieltjes transform, it is shown that the limiting distribution has a continuous derivative away from zero, the derivative being analytic whenever it is positive [3]. In the present paper, it is derived that the behavior of it resembles the behavior of a square root function near the boundary of its support.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.69-72
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• 2000

This study proposes a new calculation method for generating real nighttime lamp-lit images. In order to improve the color appearance in the prediction of a nighttime lamp-lighted scene, the lamp-lit image is synthesized based on spectral distribution using the estimated local spectral distribution of the headlamps and the surface reflectance of every object. The principal component analysis method is introduced to estimate the surface color of an object, and the local spectral distribution of the headlamps is calculated based on the illuminance data and spectral distribution of the illuminating headlamps. HID and halogen lamps are utilized to create beam patterns and captured road scenes are used as background images to simulate actual headlamp-lit images on a monitor. As a result, the reproduced images presented a color appearance that was very close to a real nighttime road image illuminated by single and multiple headlamps.

• KIEAE Journal
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• v.12 no.3
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• pp.101-106
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• 2012

Lighting energy accounts for approximately 20% of the electrical energy used worldwide. Thus, High efficiency Light emitting diode(LED)lighting is getting more popular as the next generation lighting replaced to traditional lighting fixtures. Also, LED lighting not only has a long lifetime but also can realize a variety visual environments through the wavelengh control. The lighting energy varies depending on the Spectral Power Distribution(SPD) even though the Illuminance level is same. Therefore, This study indicates that the difference of indoor energy consumption under the same illuminance level when Spectral Power Distribution(SPD) is different. As a result, Lighting energy consumption under red-color emphasizing SPD is about 10% lower than under blue-color emphasizing SPDs.

• Water for future
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• v.10 no.1
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• pp.101-117
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• 1977

The statatistical characteristics and spectra of sea waves at Mookho were analysed by several statistical methods. As the results, the following conclusions are obtained: 1. Values of surface elevation of sea wave are better fitted to Gram Charlier distribution than Gaussian distribution. This proves that sea waves have not only characters of irregularity but also non-linearity. 2. Distribution of maxima of surface elevation practically follows the distribution of Cartwright and Longuet-Higgins, also spectral width parameter is found to be increased with the increase of root mean square of surface elevation. 3. Sea wave may have spectrum of broad frequency band, however distributions of wave heights and periods follow the Rayleigh distribution which is derived from the assumption of narrow frequency band. 4. Ratios among mean wave heights from observed data show good agreements with theoretical values from Rayleigh distribution. 5. Spectral density and spectral width parameter increase with increase of wind velocity. And wave period at optimum band gas higher value than significant wave period by about 10 percent.