• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.4
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• pp.16-25
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• 2000

In this paper, a new disparity detection algorithm which is robust to noise is presented. It detects the disparity of an arbitrary pixel through the iterative competition with neighbor pixels in the range of disparity. A diffusion process to improve stereo matching confidence is used prior to detecting disparity of an arbitrary pixel. It is used for aggregating initial matching measure of the difference map. If the image region for matching is too small, a wrong match might be found due to noise. On the contrary, the region is too big, it results in blurring of object boundaries. Therefore, we decide the image region for matching by using the diffusion process for aggregating matching measure, then detect the true disparity with proposed competition method to the distribution of matching measure. Through the proposed method we get the result of improving matching rate of 6.96% with real stereo imge. From the simulation with the stereo imge, the proposed disparity detection method significantly outperforms the conventional method to matching rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.469-474
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• 2011

This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2324-2330
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• 2015

In blind signal processing in impulsive noise environment the maximum cross-correntropy (MCC) algorithm shows superior performance compared to MSE-based algorithms. But optimum weight conditions of MCC algorithm and its properties related with robustness to impulsive noise have not been studied sufficiently. In this paper, through the analysis of the behavior of its optimum weight and the relationship with the MSE-based LMS algorithm, it is shown that the optimum weight of MCC and MSE-based LMS have an equal solution. Also the factor that keeps optimum weight of MCC undisturbed and stable under impulsive noise is proven to be the magnitude controlled input through simulation.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.3
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• pp.264-271
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• 2021

Raman spectroscopy is an equipment that is widely used for classifying chemicals in chemical defense operations. However, the classification performance of Raman spectrum may deteriorate due to dark current noise, background noise, spectral shift by vibration of equipment, spectral shift by pressure change, etc. In this paper, we compare the classification accuracy of various machine learning algorithms including k-nearest neighbor, decision tree, linear discriminant analysis, linear support vector machine, nonlinear support vector machine, and convolutional neural network under noisy and spectral shifted conditions. Experimental results show that convolutional neural network maintains a high classification accuracy of over 95 % despite noise and spectral shift. This implies that convolutional neural network can be an ideal classification algorithm in a real combat situation where there is a lot of noise and spectral shift.

• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.451-459
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• 2016

While an object detection algorithm plays a key role in many computer vision applications, it requires extensive computation to show robustness under varying lightning and geometrical distortions. Recently, some approaches formulate the problem in a classification framework and show improved performances in object recognition. Among them, random fern algorithm drew a lot of attention because of its simple structure and high recognition rates. However, it reveals performance degradation under the illumination changes and noise addition, since it computes patch features based only on pixel intensities. In this paper, we propose a new structure of combined random fern which incorporates depth information into the conventional random fern reflecting 3D structure of the patch. In addition, a new structure of object tracker which exploits the combined random fern is also introduced. Experiments show that the proposed method provides superior performance of object detection under illumination change and noisy condition compared to the conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.56-62
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• 2001

In this paper, we adopt a missing data theory to speech recognition. It can be used in order to maintain high performance of speech recognizer when the missing data occurs. In general, hidden Markov model (HMM) is used as a stochastic classifier for speech recognition task. Acoustic events are represented by continuous probability density function in continuous density HMM(CDHMM). The missing data theory has an advantage that can be easily applicable to this CDHMM. A marginalization method is used for processing missing data because it has small complexity and is easy to apply to automatic speech recognition (ASR). Also, a spectral subtraction is used for detecting missing data. If the difference between the energy of speech and that of background noise is below given threshold value, we determine that missing has occurred. We propose a new method that examines the reliability of detected missing data using voicing probability. The voicing probability is used to find voiced frames. It is used to process the missing data in voiced region that has more redundant information than consonants. The experimental results showed that our method improves performance than baseline system that uses spectral subtraction method only. In 452 words isolated word recognition experiment, the proposed method using the voicing probability reduced the average word error rate by 12％ in a typical noise situation.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.1-6
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• 2017

For the algorithm of cross-information potential with delta functions (CIPD) which has superior performance in impulsive noise environments, a new method of employing the information of power variations of magnitude controlled input (MCI) in the weight update equation of the CIPD is proposed in this paper where the input of CIPD is modified by the Gaussian kernel of error. To prove its effectiveness compared to the conventionalCIPD algorithm, the distance between the current weight vector and its previous one is analyzed and compared under impulsive noise. In the simulation results the proposed method shows a two-fold improvement in steady state stability, faster convergence speed by 1.8 times, and 2 dB - lower minimum MSE in the impulsive noise situation.

• The Journal of the Acoustical Society of Korea
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• v.23 no.6
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• pp.473-480
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• 2004

In this paper we propose an effective feature compensation scheme based on the speech model in order to achieve robust speech recognition. The proposed feature compensation method is based on parallel combined mixture model (PCMM). The previous PCMM works require a highly sophisticated procedure for estimation of the combined mixture model in order to reflect the time-varying noisy conditions at every utterance. The proposed schemes can cope with the time-varying background noise by employing the interpolation method of the multiple mixture models. We apply the‘data-driven’method to PCMM tot move reliable model combination and introduce a frame-synched version for estimation of environments posteriori. In order to reduce the computational complexity due to multiple models, we propose a technique for mixture sharing. The statistically similar Gaussian components are selected and the smoothed versions are generated for sharing. The performance is examined over Aurora 2.0 and speech corpus recorded while car-driving. The experimental results indicate that the proposed schemes are effective in realizing robust speech recognition and reducing the computational complexities under both simulated environments and real-life conditions.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.543-548
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• 2014

Multiple-model based speech recognition framework (MMSR) has been known to be very successful in speech recognition. Since it uses multiple hidden Markov modes (HMMs) that corresponds to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can have a close match with the test noisy speech. However, since the number of HMM sets is limited in practical use, the acoustic mismatch still remains as a problem. In this study, we experimentally determined the optimal SNR mapping between the test noisy speech and the HMM set to mitigate the mismatch between them. Improved performance was obtained by employing the SNR mapping instead of using the estimated SNR from the test noisy speech. When we applied the proposed method to the MMSR, the experimental results on the Aurora 2 database show that the relative word error rate reduction of 6.3% and 9.4% was achieved compared to a conventional MMSR and multi-condition training (MTR), respectively.

• The Journal of Engineering Research
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• v.8 no.1
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• pp.59-65
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• 2006

본 논문에서는 마이크로폰 어레이 시스템을 이용하여 여러 화자의 음성 정보로부터 각 화자가 위치한 방향을 추정하는 기술 개발 내용을 다룬다. 성능 향상을 위한 전처리 과정으로 비선형 증폭기를 사용하여 거리에 따른 영향을 최소화하는 과정과 잡음에 대한 강인성을 얻기 위해 음성활성 영역을 검출하는 과정을 포함한다. 등간격으로 배치된 마이크로폰 어레이 시스템의 기하학적 특성에 따른 음원의 위치와 신호의 지연시간차이와의 상관관계로부터 화자의 위치를 역으로 추정하는 알고리즘을 기본으로 하여 가능성 척도를 계산하고 이를 활용하여 가능성이 높은 것들을 클러스터링하여 가능성이 있는 후보를 선정하여 화자의 방향을 검지한다. 이 과정에서 오인식을 최소화하기 위하여 가능성이 희박한 영역에 대한 추정 억제 방법으로 부정식 추론법을 적용하였다. 2 화자의 음성 신호를 입력으로 한 실험을 통하여 제안한 방법에 의한 다화자 방향검지의 가능성을 알아보았다.