• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2001.05a
• /
• pp.280-287
• /
• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evacuate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth ColorChecker. As a result, the proposed method showed a lower mean square ems between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.37 no.4
• /
• pp.232-239
• /
• 1988

The approximate overdetermined method is proposed for high resolution spectral estimation in case of short data record length or narrow band signal. And a new recursive AR parameter estimation is derived in the form of fast algorithm. For ARMA spectral estimation, two stage procedure is used in estimating ARMA parameters. First AR parameters are estimated by using the modified Yule-Walker equations, and then MA parameters are implicitly estimated by estimating numerator spectral(NS) coefficients.

• Journal of the Korean Graphic Arts Communication Society
• /
• v.16 no.3
• /
• pp.121-131
• /
• 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
• /
• 1998.10a
• /
• pp.1-6
• /
• 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.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.446-450
• /
• 1987

In this paper, overdetermined method is used for high resolution spectral estimation in case of short data record length. To reduce the computational effort and to obtain recursive form of estimation algorithm, we modify data matrix to have near-Toeplitz structure. Then, new recursive algorithm is derived in the form of fast Kalman algorithm. Two stage procedure is used for the estimation of ARMA parameters. First AR parameters are estimated by using overdetermined modified Yule-walker equation, and then MA parameters are implicitly estimated by estimating numerator spectral coefficients(NS).

• Journal of Korea Multimedia Society
• /
• v.4 no.1
• /
• pp.37-45
• /
• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evaluate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth Color Checker. As a result, the proposed method showed a lower mean square error between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• ETRI Journal
• /
• v.31 no.3
• /
• pp.292-297
• /
• 2009

The power spectral density of a signal can be estimated most accurately by using a window with a narrow bandwidth and large sidelobe attenuation. Conventional windows generally control these characteristics by only one parameter, so there is a trade-off problem: if the bandwidth is reduced, the sidelobe attenuation is also reduced. To overcome this problem, we propose using a Butterworth window with two control parameters for power spectral density estimation and analyze its characteristics. Simulation results demonstrate that the sidelobe attenuation and the 3 dB bandwidth can be controlled independently. Thus, the trade-off problem between resolution and spectral leakage in the estimated power spectral density can be overcome.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.33.5-33
• /
• 2001

We consider here the problem of spectral estimation of multidimensional wide sense stationary (WSS) random process. A method, employing a special difference equation of correlation function, is proposed to solve the problem of multidimensional spectral estimation. In this approach, the special difference equation of correlation function is derived by modal decomposition method. Maximum likelihood estimator and Kalman filter are used to estimate the model parameters of the difference equation and the decomposed spectral residues. An algorithm is presented to estimate the multidimensional spectral density. According to the result of the simulation, these methods are feasible to estimate the spectral density of WSS process, which is realized by finite dimensional multivariable lineal system driven by white noise.

• The Korean Journal of Applied Statistics
• /
• v.29 no.3
• /
• pp.463-470
• /
• 2016

In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.