• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.1-7
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• 2016

In this paper, we proposed real-time relative radiometric calibration processing method for SWIR(Short Wavelength Infra-Red) sensor using 'Hyper-Spectral Imager'. Until now domestic research for Hyper-Spectral Imager has been performing with foreign sensor device. So we have been studying hyper spectral sensor device to meet domestic requirement, especially military purpose. To improve detection & identify capability in 'Hyper-Spectral Imager', it is necessary to expend sensing wavelength from visual and NIR(Near Infra-Red) to SWIR. We aimed to design real-time processor for SWIR sensor which can control the sensor ROIC(Read-Out IC) and process calibrate the image. To build Hyper-Spectral sensor device, we will review the SWIR sensor and its signal processing board. And we will analyze relative radiometric calibration processing method and result. We will explain several SWIR sensors, our target sensor and its control method, steps for acquisition of reference images and processing result.

• 한국인쇄학회지
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• 제22권1호
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• pp.9-18
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• 2004

Recently as color image processing to be become independent have been desired at the light source in an image processing and it have been enlarged. An image processing of the light source which is become independent means an image processing which uses a spectral reflectance information. We improved it in the spectral reflectance estimation method which uses existing 3-band image in this research that the improvement of an identity color population generation method which uses the hue angle and the processing speed improvement and introduces a labelling method. The precision of a spectral reflectance estimation appeared to the ${\Delta}E^*_{ab}$ of an average 2.7 comparing with the measurement price. The practical use possibility came to be fast and appeared a processing speed compared with existing method.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.748-764
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• 2019

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

• 융합신호처리학회논문지
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• 제9권2호
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• pp.121-128
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• 2008

정확한 음성 구간 검출은 음성 인식 및 음성 코딩 그리고 음성 통신 시스템 등과 같은 음성 어플리케이션의 성능에 큰 영향을 미친다. 본 논문에서는 실제 운전하고 있는 상태에서 다양한 차량 노이즈 환경의 음성 구간 검출 방법을 제안한다. 기존의 음성 구간 검출은 시간 에너지, 주파수 에너지, 영 교차율, spectral entropy 등 다양한 방법을 사용하였으며 잡음 환경에서 급격하게 성능이 저하되는 단점이 있었다. 본 논문에서는 기존의 spectral entropy를 기반으로 하여 MFB(Mel-frequency Filter Banks) spectral entropy, 기울기 FFT(Fast Fourier Transform) spectral entropy, 기울기 MFB spectral entropy를 이용한 음성 구간 검출 방법을 제안한다. MFB는 멜 스케일과 FFT를 곱한 것으로 멜 스케일은 인간이 소리를 인지할 때 주파수에 대해 비선형적인 스케일이며 음성의 특징을 잘 반영한다. 제안한 MFB spectral entropy 방법은 다양한 차량 잡음 환경에서 음성 및 비음성 분별 능력을 향상시킬 수 있으며 실험 결과 93.21%의 음성 구간 검출율을 나타내었다. 이는 기존의 spectral entropy 방법과 비교할 때 MFB를 이용한 음성 구간 검출 방법이 3.2%의 검출율이 향상되었다.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 춘계 학술대회 발표논문집
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• pp.53-56
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• 2005

In this paper, we propose a transformation based robust adaptation technique that uses the maximum mutual information(MMI) estimation for the objective function and the linear spectral transformation(LST) for adaptation. LST is an adaptation method that deals with environmental noises in the linear spectral domain, so that a small number of parameters can be used for fast adaptation. The proposed technique is called MMI-LST, and evaluated on TIMIT and FFMTIMIT corpora to show that it is advantageous when only a small amount of adaptation speech is used.

• Journal of Multimedia Information System
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• 제5권1호
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• pp.63-66
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• 2018

In this work, the smoothing and the interpolation basis splines are analyzed. As well as the possibility of using the spectral properties of the basis splines for digital signal processing are shown. This takes into account the fact that basic splines represent finite, piecewise polynomial functions defined on compact media.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.49-52
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• 2000

The Estimation method for spectral reflectance of the object using five-band and nine-band have been developed. The five-band acquisition are required of five or three times same work for color image acquisition process. To solve the above problems, we proposed a new method that can be reconstructed spectral reflectance of object. The proposed method was to classify same hues corresponding a color stimulus, by using hue angle and chroma vector of a color stimulus. The reconstruction of spectral reflectance was examined by computer simulation, and evaluated by MSE(Mean Square Error) and color difference between the original and reconstructed spectral reflectance.

• 한국군사과학기술학회지
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• 제14권6호
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• pp.1073-1080
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• 2011

In this study, a target detection algorithm was proposed for using hyperspectral imagery. The proposed algorithm is designed to have minimal processing time, low false alarm rate, and flexible threshold selection. The target detection procedure can be divided into two steps. Initially, candidates of target pixel are extracted using matching ratio of spectral pattern that can be calculated by spectral derivation. Secondly, spectral distance is computed only for those candidates using Euclidean distance. The proposed two-step method showed lower false alarm rate than the Euclidean distance detector applied over the whole image. It also showed much lower processing time as compared to the Mahalanobis distance detector.

• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.162-167
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• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2003년도 춘계학술대회 논문집
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• pp.324-329
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• 2003

Spectral analysis is an important signal processing tool for time series data. The transformation of a time series into the frequency domain is the basis for a significant number of processing algorithms and interpretive methods. Recently developed transforms based on the new mathematical field of wavelet analysis bypass the resolution limitation and offer superior spectral decomposition. The discrete wavelet transform of Sonar data provides spectral localization of noises, hence noises can be filtered out successfully.