• Title, Summary, Keyword: Gaussian mixture model

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Voice-Pishing Detection Algorithm Based on 3GPP2 SMV (3GPP2 SMV 기반의 보이스 피싱 검출 알고리즘)

  • Lee, Kye-Hwan;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.4
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    • pp.92-99
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    • 2008
  • We propose an effective voice-pishing detection algorithm based on the 3GPP2 selectable mode vocoder (SMV). The detection of voice pishing is performed based on a Gaussian mixture model (GMM) using decoding parameters of the SMV directly extracted from the decoding process of the transmitted speech information in the mobile phone. The experimental results indicate that SMV decoding parameters are effective in discriminating between general voice and phisher's voice and the performance is significantly acceptable when the proposed technique is applied.

Fire Detection in Outdoor Using Statistical Characteristics of Smoke (연기의 통계적 특성을 이용한 실외 화재 감지)

  • Kim, Hyun-Tae;Park, Jang-Sik
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.2
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    • pp.149-154
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    • 2014
  • Detection performance of fire detection in the outdoor depends on weather conditions, the shadow by the movement of the sun, or illumination changes. In this paper, a smoke detection in conjunction with a robust background estimate algorithm to environment change in the outdoor in daytime is proposed. Gaussian Mixture Model (GMM) is applied as background estimation, and also, statistical characteristics of smoke is applied to detect the smoke for separated candidate region. Through the experiments with input videos obtained from a various weather conditions, the proposed algorithms were useful to detect smoke in the outdoor.

Design of Moving Object Detector Based on Gaussian Mixture Model (Gaussian Mixture Model 기반 이동 객체 검출기의 하드웨어 구조 설계)

  • Cho, Jae-Chan;Jung, Yong-Chul;Yoon, Kyunghan;Jung, Yunho
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.1571-1572
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    • 2015
  • 본 논문에서는 GMM (Gaussian mixture model) 기반의 BS (background subtraction) 알고리즘을 이용한 이동 객체 검출기의 하드웨어 구조 설계 결과를 제시하였다. 설계된 이동객체 검출기는 1280 * 720 HD 해상도의 영상을 30 frames per second로 실시간 처리가 가능하다. 하드웨어 구현은 Verilog-HDL을 이용하였으며, FPGA 기반 구현 결과, 설계된 이동 객체 검출기는 582 Slice, 1,698 Slice LUT, 8 DSP48s, 1,769 Flip Flop, 691.2 KByte BRAM으로 구성되었음을 확인하였다.

An Effective Denoising Method for Images Contaminated with Mixed Noise Based on Adaptive Median Filtering and Wavelet Threshold Denoising

  • Lin, Lin
    • Journal of Information Processing Systems
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    • v.14 no.2
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    • pp.539-551
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    • 2018
  • Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

Color Image Segmentation Based on Morphological Operation and a Gaussian Mixture Model (모폴로지 연산과 가우시안 혼합 모형에 기반한 컬러 영상 분할)

  • Lee Myung-Eun;Park Soon-Young;Cho Wan-Hyun
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.3
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    • pp.84-91
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    • 2006
  • In this paper, we present a new segmentation algorithm for color images based on mathematical morphology and a Gaussian mixture model(GMM). We use the morphological operations to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the GMM to represent the probability distribution of color feature vectors and used the deterministic annealing expectation maximization (DAEM) algorithm to estimate the parameters of the GMM that represents the multi-colored objects statistically. Finally, we segment the color image by using posterior probability of each pixel computed from the GMM. The experimental results show that the morphological operation is efficient to determine a number of components and initial modes of each component in the mixture model. And also it shows that the proposed DAEM provides a global optimal solution for the parameter estimation in the mixture model and the natural color images are segmented efficiently by using the GMM with parameters estimated by morphological operations and the DAEM algorithm.

Particle Filters using Gaussian Mixture Models for Vision-Based Navigation (영상 기반 항법을 위한 가우시안 혼합 모델 기반 파티클 필터)

  • Hong, Kyungwoo;Kim, Sungjoong;Bang, Hyochoong;Kim, Jin-Won;Seo, Ilwon;Pak, Chang-Ho
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.47 no.4
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    • pp.274-282
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    • 2019
  • Vision-based navigation of unmaned aerial vehicle is a significant technology that can reinforce the vulnerability of the widely used GPS/INS integrated navigation system. However, the existing image matching algorithms are not suitable for matching the aerial image with the database. For the reason, this paper proposes particle filters using Gaussian mixture models to deal with matching between aerial image and database for vision-based navigation. The particle filters estimate the position of the aircraft by comparing the correspondences of aerial image and database under the assumption of Gaussian mixture model. Finally, Monte Carlo simulation is presented to demonstrate performance of the proposed method.

Effective Parameter Estimation of Bernoulli-Gaussian Mixture Model and its Application to Image Denoising (베르누이-가우스 혼합 모델의 효과적인 파라메터 추정과 영상 잡음 제거에 응용)

  • Eom, Il-Kyu;Kim, Yoo-Shin
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.5
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    • pp.47-54
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    • 2005
  • In general, wavelet coefficients are composed of a few large coefficients and a lot of small coefficients. In this paper, we propose image denoising algorithm using Bernoulli-Gaussian mixture model based on sparse characteristic of wavelet coefficient. The Bernoulli-Gaussian mixture is composed of the multiplication of Bernoulli random variable and Gaussian mixture random variable. The image denoising is performed by using Bayesian estimation. We present an effective denoising method through simplified parameter estimation for Bernoulli random variable using local expected squared error. Simulation results show our method outperforms the states-of-art denoising methods when using orthogonal wavelets.

Implementation of Variational Bayes for Gaussian Mixture Models and Derivation of Factorial Variational Approximation (변분 근사화 분포의 유도 및 변분 베이지안 가우시안 혼합 모델의 구현)

  • Lee, Gi-Sung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.9 no.5
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    • pp.1249-1254
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    • 2008
  • The crucial part of graphical model is to compute the posterior distribution of parameters plus with the hidden variables given the observed data. In this paper, implementation of variational Bayes method for Gaussian mixture model and derivation of factorial variational approximation have been proposed. This result can be used for data analysis tasks like information retrieval or data visualization.

Advanced Gaussian Mixture Learning for Complex Environment (개선된 적응적 가우시안 혼합 모델을 이용한 객체 검출)

  • Park Dae-Yong;Kim Jae-Min;Cho Seong-Won
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.283-289
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    • 2005
  • Background Subtraction은 움직이는 물체 검출에 가장 많이 사용되는 방법 중 하나이다. 배경이 복잡하고 변화가 심한 경우, 배경을 실시간으로 얼마나 정확하게 학습하는가가 물체 검출의 정확도를 결정한다. Gaussian Mixture Model은 이러한 배경의 모델링에 가장 많이 쓰이는 방법이다. Gaussian Mixture Model은 확률적 학습 방법을 사용하는데, 이러한 방법은 물체가 자주 지나다니거나 물체가 멈춰있는 경우, 배경을 정확하게 모델링하지 못한다. 본 논문에서는 밝기 값에 대한 확률적 모델링과 밝기 값의 변화에 따른 처리를 결합하여 혼잡한 환경에서 배경을 정확하게 모델링할 수 있는 학습 방법을 제안한다.

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Performance of GMM and ANN as a Classifier for Pathological Voice

  • Wang, Jianglin;Jo, Cheol-Woo
    • Speech Sciences
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    • v.14 no.1
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    • pp.151-162
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    • 2007
  • This study focuses on the classification of pathological voice using GMM (Gaussian Mixture Model) and compares the results to the previous work which was done by ANN (Artificial Neural Network). Speech data from normal people and patients were collected, then diagnosed and classified into two different categories. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chosen. Then the classification method based on the artificial neural network and Gaussian mixture method was employed to discriminate the data into normal and pathological speech. The GMM method attained 98.4% average correct classification rate with training data and 95.2% average correct classification rate with test data. The different mixture number (3 to 15) of GMM was used in order to obtain an optimal condition for classification. We also compared the average classification rate based on GMM, ANN and HMM. The proper number of mixtures on Gaussian model needs to be investigated in our future work.

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