• Title, Summary, Keyword: Gaussian mixture model

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An analysis of hardware design conditions of EGML-based moving object detection algorithm (EGML 기반 이동 객체 검출 알고리듬의 하드웨어 설계조건 분석)

  • An, Hyo-sik;Kim, Keoung-hun;Shin, Kyung-wook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.371-373
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    • 2015
  • This paper describes an analysis of hardware design conditions of moving object detection algorithm which is based on effective Gaussian mixture learning (EGML). The simulation model of EGML algorithm is implemented using OpenCV, and it is analyzed that the effects of parameter values on background learning time and moving object detection sensitivity for various images. In addition, optimal design conditions for hardware implementation of EGML-based MOD algorithm are extracted from fixed-point simulations for various bit-width parameters.

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Rockfall Source Identification Using a Hybrid Gaussian Mixture-Ensemble Machine Learning Model and LiDAR Data

  • Fanos, Ali Mutar;Pradhan, Biswajeet;Mansor, Shattri;Yusoff, Zainuddin Md;Abdullah, Ahmad Fikri bin;Jung, Hyung-Sup
    • Korean Journal of Remote Sensing
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    • v.35 no.1
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    • pp.93-115
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    • 2019
  • The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

Noise Reduction Using Gaussian Mixture Model and Morphological Filter (가우스 혼합모델과 형태학적 필터를 이용한 잡음 제거)

  • Eom Il-Kyu;Kim Yoo-Shin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.1
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    • pp.29-36
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    • 2004
  • Generally, wavelet coefficients can be classified into two categories: large coefficients with much signal information and small coefficients with little signal component. This statistical characteristic of wavelet coefficient is approximated to Gaussian mixture model and efficiently applied to noise reduction. In this paper, we propose an image denoising method using mixture modeling of wavelet coefficients. Binary mask value is generated by proper threshold which classifies wavelet coefficients into two categories. Information of binary mask value is used to remove image noise. We also develope an enhancement method of mask value using morphological filter, and apply it to image denoising for improvement of the proposed method. Simulation results shows the proposed method have better PSNRs than those of the state of art denoising methods.

Smoothed Local PC0A by BYY data smoothing learning

  • Liu, Zhiyong;Xu, Lei
    • 제어로봇시스템학회:학술대회논문집
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    • pp.109.3-109
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    • 2001
  • The so-called curse of dimensionality arises when Gaussian mixture is used on high-dimensional small-sample-size data, since the number of free elements that needs to be specied in each covariance matrix of Gaussian mixture increases exponentially with the number of dimension d. In this paper, by constraining the covariance matrix in its decomposed orthonormal form we get a local PCA model so as to reduce the number of free elements needed to be specified. Moreover, to cope with the small sample size problem, we adopt BYY data smoothing learning which is a regularization over maximum likelihood learning obtained from BYY harmony learning to implement this local PCA model.

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GMM based Speaker Identification using Pitch Information (피치 정보를 이용한 GMM 기반의 화자 식별)

  • Park Taesun;Hahn Minsoo
    • MALSORI
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    • no.47
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    • pp.121-129
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    • 2003
  • This paper describes the use of pitch information for speaker identification. The recognition system is a GMM based one with 4 connected Korean digits speech database. The mean of the pitch period in voiced sections of speech are shown to be ,useful at discriminating between speakers. Utilizing this feature with Gaussian mixture model in the speaker identification system gave a marked improvement, maximum 6% improvement comparing to the baseline Gaussian mixture model.

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Fuzzy Clustering with Improving Gustafson-Kessel Algorithm (개선된 Gustafson-Kessel 알고리즘을 이용한 퍼지 클러스터링)

  • 김승석;곽근창;유정웅;전명근
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.239-242
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    • 2003
  • 본 논문에서는 Gaussian Mixture Model을 이용한 Gustafson-Kessel 알고리즘의 성능을 개선하였다. 분포 및 밀도가 다른 데이터에 대하여 적절한 클러스터 파라미터를 추정함으로써 클러스터링의 성능을 개선한다. 일반적인 클러스터링 알고리즘의 경우, 데이터가 편중되거나 각 데이터의 밀도가 서로 틀린 경우 클러스터의 파라미터가 정확하게 클러스터를 표현하지 못하는 문제점을 가지고 있다. 제안된 방법에서는 Gustafson-Kessel 알고리즘을 이용하여 클러스터 파라미터를 추정하며 알고리즘내의 파라미터 일부를 Gaussian Mixture Model을 이용하여 동적으로 갱신하였다 시뮬레이션을 통하여 제안된 방법의 유용성을 보인다.

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Implementation of the Timbre-based Emotion Recognition Algorithm for a Healthcare Robot Application (헬스케어 로봇으로의 응용을 위한 음색기반의 감정인식 알고리즘 구현)

  • Kong, Jung-Shik;Kwon, Oh-Sang;Lee, Eung-Hyuk
    • Journal of IKEEE
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    • v.13 no.4
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    • pp.43-46
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    • 2009
  • This paper deals with feeling recognition from people's voice to fine feature vectors. Voice signals include the people's own information and but also people's feelings and fatigues. So, many researches are being progressed to fine the feelings from people's voice. In this paper, We analysis Selectable Mode Vocoder(SMV) that is one of the standard 3GPP2 codecs of ETSI. From the analyzed result, we propose voices features for recognizing feelings. And then, feeling recognition algorithm based on gaussian mixture model(GMM) is proposed. It uses feature vectors is suggested. We verify the performance of this algorithm from changing the mixture component.

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Speaker Normalization using Gaussian Mixture Model for Speaker Independent Speech Recognition (화자독립 음성인식을 위한 GMM 기반 화자 정규화)

  • Shin, Ok-Keun
    • The KIPS Transactions:PartB
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    • v.12B no.4
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    • pp.437-442
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    • 2005
  • For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of a test speaker by making use of the obtained probabilistic model. The purpose of this study is twofold: improving the existing ML based methods, and comparing the performance of what is called 'soft decision' method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained tv adjusting the number of clusters in GMM appropriately.

Performance Comparison of Background Estimation in the Video (영상에서의 배경추정알고리즘 성능 비교)

  • Do, Jin-Kyu;Kim, Gyu-Yeong;Park, Jang-Sik;Kim, Hyun-Tae;Yu, Yun-Sik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.808-810
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    • 2011
  • The background estimation algorithms had a significant impact on the performance of image processing and recognition. In this paper, background estimation algorithms were analysis of complexity and performance as preprocessing of image recognition. It was evaluated the performance of Gaussian Running Average, Mixture of Gaussian, and KDE algorithm. The simulation results show that KDE algorithm outperforms compared to the other algorithms.

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