• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(1)
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• pp.195-198
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• 2002

One-dimensional radar signature, such as range profile, is highly dependent on the aspect angle. Therefore, radar target recognition over wide angular region is a very difficult task. In this paper, we propose the Bayes classifier with Gaussian mixture model for radar target recognition over wide-angular region and compare performances of proposed technique and radar target recognition with subclasses concept in the literature of probability of correct classification ratio.

• 응용통계연구
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• 제21권5호
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• pp.825-833
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• 2008

혼합모형을 이용한 판별분석은 다중 분류문제를 해결하는데 유용한 방법으로서 준지도 학습으로 확장될 수 있다. 본 논문에서는 정규 혼합분포를 이용한 준지도 학습 방법에서 혼합 모형의 하위 구성요소 개수 선택 기준을 연구하고자 한다. 하위 구성요소 선택 기준으로서 베이지안 정보량을 사용하였고 모의실험을 통해 이 방법의 유용성을 규명하였다.

• 스마트미디어저널
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• 제2권2호
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• pp.14-19
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• 2013

Recently, due to the increase of the use of scanned images, the text segmentation techniques, which play critical role to optimize the quality of the scanned images, are required to be updated and advanced. In this study, an algorithm has been developed based on the modification of Gaussian mixture model (GMM) by integrating the calculation of Gaussian detection gradient and the estimation of the number clusters. The experimental results show an efficient method for text segmentation in natural scenes such as storefronts, street signs, scanned journals and newspapers at different size, shape or color of texts in condition of lighting changes and complex background. These indicate that our model algorithm and research approach can address various issues, which are still limitations of other senior algorithms and methods.

• 음성과학
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• 제5권1호
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• pp.7-21
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• 1999

This paper proposes an improved training procedure in speech recognition based on the continuous density of the Hidden Markov Model (CDHMM). Of the three parameters (initial state distribution probability, state transition probability, output probability density function (p.d.f.) of state) governing the CDHMM model, we focus on the third parameter and propose an efficient algorithm that determines the p.d.f. of each state. It is known that the resulting CDHMM model converges to a local maximum point of parameter estimation via the iterative Expectation Maximization procedure. Specifically, we propose two independent algorithms that can be embedded in the segmental K -means training procedure by replacing relevant key steps; the adaptation of the number of mixture Gaussian p.d.f. and the initialization using the CDHMM parameters previously estimated. The proposed adaptation algorithm searches for the optimal number of mixture Gaussian humps to ensure that the p.d.f. is consistently re-estimated, enabling the model to converge toward the global maximum point. By applying an appropriate threshold value, which measures the amount of collective changes of weighted variances, the optimized number of mixture Gaussian branch is determined. The initialization algorithm essentially exploits the CDHMM parameters previously estimated and uses them as the basis for the current initial segmentation subroutine. It captures the trend of previous training history whereas the uniform segmentation decimates it. The recognition performance of the proposed adaptation procedures along with the suggested initialization is verified to be always better than that of existing training procedure using fixed number of mixture Gaussian p.d.f.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.407-409
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• 2015

가우시안 혼합 모델(GMM)과 배경 차분 기법을 이용한 이동 객체 검출(MOD) 알고리듬을 하드웨어로 구현하였다. 구현된 MOD 프로세서는 EGML(Effective Gaussian Mixture Learning)을 기반으로 배경을 생성하고 업데이트하며, EGML 계산 일부의 근사화를 통해 하드웨어 복잡도를 줄였고, 파이프라이닝 기법을 통해 동작속도를 개선하였다. 또한 가우시안 파라미터들을 가변시킬 수 있도록 함으로써 다양한 조건에서 이동 객체 검출 성능이 향상되도록 구현하였다. 설계된 회로는 FPGA-in-the-loop방식으로 하드웨어 동작을 검증하였으며, XC5VSX95T FPGA 디바이스에서 최대 109 MHz의 클록 주파수로 동작 가능한 것으로 평가되었다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.808-810
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• 2011

입력영상에 대하여 전처리과정으로 배경을 분리하는 것이 영상처리 및 인식 성능에 중요한 영향을 준다. 본 논문에서는 화재검출을 위한 영상인식 전처리로 활용하는 다양한 배경추정 알고리즘에 대하여 계산량과 배경추정 성능 분석하였다. 비교하는 배경추정알고리즘은 Gaussian Running Average 추정기법, Mixture of Gaussian 모델, 그리고 KDE (kernel density estimate) 알고리즘에 대한 성능을 평가하였다. 입력영상에 대하여 배경영상차로부터 연기를 검출하는데 있어 KDE 알고리즘이 배경추정 성능은 우수한 것을 확인하였다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.1161-1166
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• 2011

A camera is mounted vertically downwards viewing the people heads from the top. This configuration is successful in people counting technique especially when only a few isolated people pass through a counting region in a non-crowded situation. Thus, this paper describes object counting which detects and count moving people using mixture of gaussian and motion vector. This method is intended to estimates the number of people in outdoor environment. This method use single gaussian background modeling which is more robust an noise and has adaptiveness. The experimental results that is based on mixture of gaussian and motion vector is also helpful to design intelligent surveillance.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2022년도 제66차 하계학술대회논문집 30권2호
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• pp.35-36
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• 2022

본 논문은 딥러닝 사람 자세 추정 모델이 사람의 관절 키포인트를 예측하는데 관절의 2차원 면적에 의해 키포인트별 𝜎, 즉, 표준 편차를 가지는 가우시안 커널(Gaussian Kernel)을 예측하는 방법을 제안한다. 각 관절 키포인트에 대해 다른 𝜎를 가지는 정답 히트맵(Ground Truth Heatmap)과 제안한 Gaussian Mixture Block를 모델에 추가해서 관절의 크기를 맞는 히트맵을 예측한다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.246-249
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• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• 한국통신학회:학술대회논문집
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• 한국통신학회 2006년도 하계종합학술발표회 논문 초록집
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• pp.434-434
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• 2006