• Journal of Biomedical Engineering Research
• /
• v.16 no.4
• /
• pp.439-446
• /
• 1995

In this paper, we present an algorithm for the detection of blood vessel contours in coronary aiteriograms. The proposed algorithm is based on both matched filtering and adaptive tracking. The proposed algorithm has merits in that it overcomes the bifurcation section problem as well as narrow vessel problem. Moreover, the algorithm has fast performance as well as insensitivity to noise.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.1961-1963
• /
• 2001

In a target tracking problem, the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In this study, an extended robust $H_{\infty}$ was developed which can significantly improve the tracking performance when glint noise is present. Through the computer simulations, the proposed filter showed superior and robust tracking performance compared with other extended Kalman filters.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2537-2539
• /
• 2003

본 논문에서는 의료 영상에 대한 저작권보호를 위한 새로운 워터마킹 알고리즘을 제안한다. 본 알고리즘에서는 이산 웨이블릿 변환 대신에 계산량이 적은 정수 웨이블릿 변환을 이용하였다. 본 논문에서는 정수 웨이블릿 공간에서 유사-잡음 수열을 워터마크로 삽입 하였다. 워터마크를 추출할 때 확산스펙트럼 기법을 이용하고 유사도는 공분산 수열에서 결정한다. 실험을 통하여 제안한 알고리즘이 노이즈, 압축 등 공격에 강인함을 보임을 확인하였다.

• Spatial Information Research
• /
• v.21 no.3
• /
• pp.11-19
• /
• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.3
• /
• pp.147-153
• /
• 2004

This paper proposes an efficient edge detection using Van der Waerden statistic in original and noisy images. An edge is where the intensity of an image moves from a low value to a high value or vice versa. We describe a nonparametric Wilcoxon test and a parametric T test based on statistical hypothesis testing for the detection of edges. We use the threshold determined by specifying significance level $\alpha$, while Bovik, Huang and Munson consider the range of possible values of test statistics for the threshold. From the experimental results of edge detection, the T and Wilcoxon method perform sensitively to the noisy image, while the proposed Waerden method is robust over both noisy and noise-free images under $\alpha$=0.0005. Comparison with our statistical test and Sobel, LoG, Canny operators shows that Waerden method perform more effectively in both noisy and noise-free images.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.1
• /
• pp.75-80
• /
• 2010

Noise handing is very important in voice recognition of vehicle environment. that has been studying about to hardware and software approach. hardware method that is noise filter circuit design, basically using Low-pass filter. it was shown a good result. and the side of software that has been developing about to algorithm for Noise canceler, NN(neural network), etc. in this paper we have analysis about to classified parameter pattern matting level for voice recognition on car noise environment that use of DTW(Dynamic Time Warping) which is applicable time series pattern recognition algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.3
• /
• pp.67-77
• /
• 2004

The recent research has been focused on fusion of audio and visual features for reliable speech recognition in noisy environments. In this paper, we propose a neural network based model of robust speech recognition by integrating audio, visual, and contextual information. Bimodal Neural Network(BMNN) is a multi-layer perception of 4 layers, each of which performs a certain level of abstraction of input features. In BMNN the third layer combines audio md visual features of speech to compensate loss of audio information caused by noise. In order to improve the accuracy of speech recognition in noisy environments, we also propose a post-processing based on contextual information which are sequential patterns of words spoken by a user. Our experimental results show that our model outperforms any single mode models. Particularly, when we use the contextual information, we can obtain over 90% recognition accuracy even in noisy environments, which is a significant improvement compared with the state of art in speech recognition. Our research demonstrates that diverse sources of information need to be integrated to improve the accuracy of speech recognition particularly in noisy environments.

• Proceedings of the KSPS conference
• /
• 2007.05a
• /
• pp.37-38
• /
• 2007

This paper reviews the issues in implementing sound recognizers in real environments. First is the signal corruption caused by background noises and reverberation. Second is the open-set problem which is the problem of rejecting out-of-vocabulary words and noises. These two issues must be solved for noise robust recognizers.

• MALSORI
• /
• no.64
• /
• pp.121-135
• /
• 2007

Speech signal is distorted by channel characteristics or additive noise and then the performances of speaker or speech recognition are severely degraded. To cope with the noise problem, we propose a modified HMM decoder algorithm using SNR-based observation confidence, which was successfully applied for GMM in speaker identification task. The modification is done by weighting observation probabilities with reliability values obtained from SNR. Also, we apply PSO (particle swarm optimization) method to the confidence function for maximizing the speaker identification performance. To evaluate our proposed method, we used the ETRI database for speaker recognition. The experimental results showed that the performance was definitely enhanced with the modified HMM decoder algorithm.

• MALSORI
• /
• no.48
• /
• pp.107-116
• /
• 2003

This paper deals with a new parameter for voice detection which is used for many areas of speech engineering such as speech synthesis, speech recognition and speech coding. CV (Coefficient of Variation) of speech spectrum as well as other feature parameters is used for the detection of speech. CV is calculated only in the specific range of speech spectrum. Average magnitude and spectral magnitude are also employed to improve the performance of detector. From the experimental results the proposed voice detector outperformed the conventional energy-based detector in the sense of error measurements.