• The Journal of the Acoustical Society of Korea
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• v.28 no.7
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• pp.668-673
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• 2009

In this paper, we propose the minima controlled speech presence uncertainty tracking method to improve a speech enhancement. In the conventional tracking speech presence uncertainty, we propose a method for estimating distinct values of the a priori speech absence probability for different frames and channels. This estimation is inherently based on a posteriori SNR and used in estimating the speech absence probability (SAP). In this paper, we propose a novel estimation of distinct values of the a priori speech absence probability, which is based on minima controlled speech presence uncertainty tracking method, for different frames and channels. Subsequently, estimation is applied to the calculation of speech absence probability for speech enhancement. Performance of the proposed enhancement algorithm is evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various noise environments. We show that the proposed algorithm yields better results compared to the conventional tracking speech presence uncertainty.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.89-97
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• 2012

In this paper, we propose a novel approach to noise power estimation for speech enhancement in noisy environments. The method based on IMCRA (improved minima controlled recursive averaging) which is widely used in speech enhancement utilizes a rough VAD (voice activity detection) algorithm which excludes speech components during speech periods in order to improves the performance of the noise power estimation by reducing the speech distortion caused by the conventional algorithm based on the minimum power spectrum derived from the noisy speech. However, since the VAD algorithm is not sufficient to distinguish speech from noise at non-stationary noise and low SNRs (signal-to-noise ratios), the speech distortion resulted from the minimum tracking during speech periods still remained. In the proposed method, minimum power estimate obtained by IMCRA is modified by SMT (spectral minima tracking) to reduce the speech distortion derived from the bias of the estimated minimum power. In addition, in order to effectively estimate minimum power by considering the distribution characteristic of the speech and noise spectrum, the presented method combines the minimum estimates provided by IMCRA and SMT depending on the weighting factor based on the subband. Performance of the proposed algorithm is evaluated by subjective and objective quality tests under various environments and better results compared with the conventional method are obtained.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.363-370
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• 2017

In this paper, we proposed a low-complexity speech enhancement algorithm based on a improved minima controlled recursive averaging (IMCRA) and log minimum mean square error (logMMSE). The IMCRA algorithm track the minima value of input power within buffers in local window and identify the speech presence using ratio between input power and its minima value. In this process, many number of operations are required. To reduce the number of operations of IMCRA algorithm, minima value is tracked using time-varying frequency-dependent smoothing based on speech presence probability. The proposed algorithm enhanced speech quality by 2.778%, 3.481%, 2.980% and 2.162% in 0, 5, 10 and 15dB SNR respectively and reduced computational complexity by average 9.570%.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.205-208
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• 2005

본 논문은 특징 가중치 벡터를 적용하여 능동형태 모델(Active Shape Model)기반에서 눈동자의 움직임 추적 속도를 향상시키는 방법을 제안한다. 일반적인 능동형태 모델에서는 객체 추적을 위한 PDM 구성을 위해 각 특징점 구성 벡터의 유클리디안 거리의 최소 값으로 Training Set정렬 과정을 수행한다. 이 과정에서 적절하지 못한 샘플 영상으로 인해 안정된 PDM을 구성하지 못하는 문제점이 발생한다. 이러한 문제점을 해결하기 위하여 본 논문에 서는 형태를 구성하는 특징점마다 가중치를 부여하는 벡터를 작성하고, 최소자승근사법으로 최유사 특징점 벡터를 산출하기 위한 선형방정식을 구상하였다. 이로 인해 안정된 PDM을 구성할 수 있었으며, 눈동자 추적실험을 통해 형태적 움직임을 보정하는 실험을 수행하였다. 실험결과 기존의 능동형태 모델에 비해 반복연산의 횟수가 줄어들고, 다양한 형태로 나타나는 눈동자의 움직임 추적에 보다 안정적인 결과를 얻을 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.

• Aerospace Engineering and Technology
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• v.10 no.2
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• pp.170-176
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• 2011

This paper analyzes the orbit determination results using azimuth and elevation angle from ground tracking data, which has the standard data interface format, GEOS-C. The ground tracking data is very useful for initial orbit determination after a satellite launch. In this paper, the quality of the measurement data has been investigated using a variety of real tracking passes, compared with the high precision orbit data of KOMPSAT-2. The accumulated tracking data from consecutive satellite-ground passes is processed for orbit determination using least square method. The accuracy of orbit determination result is also presented.

• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.123-128
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• 2011

In this paper, we propose the improved noise power estimation in speech enhancement under various noise environments. The previous MS algorithm tracking the minimum value of finite search window uses the optimal power spectrum of signal for smoothing and adopts minimum probability. From the investigation of the previous MS-based methods it can be seen that a fixed size of the minimum search window is assumed regardless of the various environment. To achieve the different search window size, we use the noise classification algorithm based on the Gaussian mixture model (GMM). Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 perceptual evaluation of speech quality (PESQ) under various noise environments. Based on this, we show that the proposed algorithm yields better result compared to the conventional MS method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.318-322
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• 2006

본 연구에서는 한정된 전원으로 구동되는 센서 네트워크 환경 하에서 물체의 이동을 검출하고 예측을 통해 효과적인 추적을 가능케 함으로써 missing-rate를 최소로 하는 새로운 형태의 알고리즘을 제안하고 시뮬레이션을 통해 제안된 방법의 유용성을 입증하고자 한다. 제안된 기법에서는 물체의 이동과 관련된 센서 노드들로부터의 정보 및 이를 기반으로 센서 노드에 장착된 A/D변환기의 임계값을 적응적으로 변화시킴으로써 물체의 missing-rate를 최소화 시키고자 하였다.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.767-774
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• 2005

In this paper, we present a snake-based scheme for tracking object contour using disparity information taken from a stereo image sequence with cluttered background. The proposed method is composed of two steps. First, 3-D motion of object is estimated and candidate snake points are selected in disparity space. Second, object contour is extracted by using a modified snake algorithm with disparity information. The proposed algorithm can successfully extract the concave contour of objects and track the object contour in complex image. Performance of the proposed algorithm has been verified by simulation.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.11
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• pp.789-794
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• 2013

A vehicle tracking system makes it possible to induce the vehicle movement path for avoiding traffic congestion and to prevent traffic accidents in advance by recognizing traffic flow, monitoring vehicles, and detecting road accidents. To track the vehicles effectively, those which appear in a sequence of video frames need to identified by extracting the features of each object in the frames. Next, the identical vehicles over the continuous frames need to be recognized through the matching among the objects' feature values. In this paper, we identify objects by binarizing the difference image between a target and a referential image, and the labelling technique. As feature values, we use the center coordinate of the minimum bounding rectangle(MBR) of the identified object and the averages of 1D FFT(fast Fourier transform) coefficients with respect to the horizontal and vertical direction of the MBR. A vehicle is tracked in such a way that the pair of objects that have the highest similarity among objects in two continuous images are regarded as an identical object. The experimental result shows that the proposed method outperforms the existing methods that use geometrical features in tracking accuracy.