IEEE Organizations related to Mixture Models

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Conferences related to Mixture Models

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ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)

The conference focuses on all areas of machine learning and its applications in medicine, biology, industry, manufacturing, security, education, virtual environments, game playing big data, deep learning, and problem solving.


2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)

The main goal of the IDAACS is to provide a forum for high quality reports on the state-of-the-art Theory, Technology and Applications of Intelligent Data Acquisition and Advanced Computer Systems as used in measurement, automation, and scientific research, in industry and in business. Rapid developments in these areas have resulted in more intelligent, sensitive, and accurate methods of data acquisition and data processing in manufacturing, in the environmental and medical monitoring systems, in laboratory measurement equipment. The importance of IDAACS is its vision to establish scientific contacts between research teams and scientists from different countries for future joint research collaborations.


2011 International Conference on Multimedia Technology (ICMT)

ICMT aims to provide a high-level international forum for researchers and engineers to present and discuss recent advances, new techniques and applications in the field of image&video processing and multimedia technology.



Periodicals related to Mixture Models

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No periodicals are currently tagged "Mixture Models"


Most published Xplore authors for Mixture Models

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Xplore Articles related to Mixture Models

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Combinatorial bounds on the α-divergence of univariate mixture models

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.


Model-based superpixel segmentation of SAR images

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

We propose a superpixel segmentation method for synthetic aperture radar (SAR) images. The method uses the SAR image amplitudes and pixels coordinates as features. The feature vectors are modeled statistically by taking into account the SAR image statistics. Nakagami and bivariate Gaussian distributions are used for amplitudes and position vectors, respectively. A finite mixture model (FMM) is proposed for pixel ...


Improved cell segmentation with adaptive bi-Gaussian mixture models for image contrast enhancement pre-processing

2017 IEEE Life Sciences Conference (LSC), 2017

The accurate detection and segmentation of cells from time-lapse microscopic video sequences provides a critical foundation for understanding dynamic cell behaviours and cell characteristics when using automatic cell tracking systems. However, general object segmentation methods in computer vision are susceptible to errors due to the severe microscopic imaging conditions in time-lapse cell videos. To address the low image intensity contrast ...


Classification of hyperspectral images using mixture of probabilistic PCA models

2016 24th European Signal Processing Conference (EUSIPCO), 2016

We propose a supervised classification and dimensionality reduction method for hyperspectral images. The proposed method contains a mixture of probabilistic principal component analysis (PPCA) models. Using the PPCA in the mixture model inherently provides a dimensionality reduction. Defining the mixture model to be spatially varying, we are also able to include spatial information into the classification process. In this way, ...


Acoustic echo suppression based on speech presence probability

2016 IEEE International Conference on Digital Signal Processing (DSP), 2016

This paper proposes a novel acoustic echo suppression (AES) algorithm based on speech presence probability in a frequency domain. Double talk detection algorithm based on two cross-correlation coefficients modeled by Beta distribution controls the update of echo path response to improve the quality of near-end speech. The near-end speech presence probability combined with the Wiener gain function is used to ...


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Educational Resources on Mixture Models

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IEEE.tv Videos

Single Frame Super Resolution: Fuzzy Rule-Based and Gaussian Mixture Regression Approaches
IMS 2011 Microapps - Local Fundamental Frequency Enhancements for X-Parameter Models
LPIRC: Developing Mobile Computer Vision Models
Piero P Bonissone - Lazy Meta-Learning - Creating Customized Model Ensembles on Demand
IMS 2011 Microapps - Beyond the S-Parameter: The Benefits of Nonlinear Device Models
Reconstructed Brain Models for Virtual Bodies and Robots
Brooklyn 5G Summit: Channel Models: Key to 5G Air-Interface Technology
BSIM Spice Model Enables FinFET and UTB IC Design
Receiver Design and Analysis: RF Boot Camp
EMBC 2011-Keynote-The Importance of Neuromechanical Limb Models in the Design of Leg Prostheses and Orthoses -Hugh Herr
Brooklyn 5G - 2015 - George MacCartney - MmWave Channel Models - A Unified Approach for 5G Standardization and Modern Design
Recording and Using 3D Object Models with RoboEarth
Abstraction and Modeling of Cyber Security tutorial, Part 2
Brooklyn 5G Summit 2014: Dr. Ali Sadri on the Evolution of the mmWave Technologies
Deep Learning and the Representation of Natural Data
Hausi Muller: Models At Runtime and Networked Control for Smart Cyber Physical Systems: WF IoT 2016
Requirements, Models, and Properties: Their Relationship and Validation
Abstraction and Modeling of Cyber Security tutorial, Part 1
Dictionary Learning: Principles, Algorithms, Guarantees
Panel 1: Critical Modeling Aspects & Effects on System Design & Performance - Brooklyn 5G 2015

IEEE-USA E-Books

  • Combinatorial bounds on the α-divergence of univariate mixture models

    We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.

  • Model-based superpixel segmentation of SAR images

    We propose a superpixel segmentation method for synthetic aperture radar (SAR) images. The method uses the SAR image amplitudes and pixels coordinates as features. The feature vectors are modeled statistically by taking into account the SAR image statistics. Nakagami and bivariate Gaussian distributions are used for amplitudes and position vectors, respectively. A finite mixture model (FMM) is proposed for pixel clustering. Learning and clustering steps are performed using posterior distributions. Based on the classification results obtained on real TerraSAR-X image, it is shown that the proposed method is capable of obtaining more accurate superpixels compared to state-of-the-art superpixel segmentation methods.

  • Improved cell segmentation with adaptive bi-Gaussian mixture models for image contrast enhancement pre-processing

    The accurate detection and segmentation of cells from time-lapse microscopic video sequences provides a critical foundation for understanding dynamic cell behaviours and cell characteristics when using automatic cell tracking systems. However, general object segmentation methods in computer vision are susceptible to errors due to the severe microscopic imaging conditions in time-lapse cell videos. To address the low image intensity contrast typical in cell images, this paper investigates the use of an adaptive, shifted bi- Gaussian mixture model to enhance the contrast prior to cell segmentation. Rather than using a model with fixed parameters across an entire video sequence as in existing approaches, this paper proposes the adaptive derivation of the mixture model parameters to match the intensity histogram for each video frame to adaptively address changes in the video background. Experimental results across a cell database show improved segmentation accuracy compared with existing image contrast enhancement methods. The pre- processed cell image exhibits greater differentiation between the cell foreground and background, whilst also maintaining the original intensity histogram features.

  • Classification of hyperspectral images using mixture of probabilistic PCA models

    We propose a supervised classification and dimensionality reduction method for hyperspectral images. The proposed method contains a mixture of probabilistic principal component analysis (PPCA) models. Using the PPCA in the mixture model inherently provides a dimensionality reduction. Defining the mixture model to be spatially varying, we are also able to include spatial information into the classification process. In this way, the proposed mixture model allows dimensionality reduction and spectral-spatial classification of hyperspectral image at the same time. The experimental results obtained on real hyperspectral data show that the proposed method yields better classification performance compared to state of the art methods.

  • Acoustic echo suppression based on speech presence probability

    This paper proposes a novel acoustic echo suppression (AES) algorithm based on speech presence probability in a frequency domain. Double talk detection algorithm based on two cross-correlation coefficients modeled by Beta distribution controls the update of echo path response to improve the quality of near-end speech. The near-end speech presence probability combined with the Wiener gain function is used to reduce the residual echo. The performance of the proposed algorithm is evaluated by objective tests. High echo-return-loss enhancement and perceptual evaluation of speech quality (PESQ) scores are obtained by comparing with the conventional AES method.

  • Spatially constrained Generalized Dirichlet mixture model for image segmentation

    A new color image segmentation of noisy images based on spatial information with the Generalized Dirichlet mixture model is presented. The methodology uses Markov Random Field distribution with a novel factor that is induced in mixture model. The model is learned using Expectation Maximization (EM) algorithm based on Newton-Raphson approach. The obtained results using real images are more encouraging than those proposed in earlier algorithms/models.

  • Comix: Joint estimation and lightspeed comparison of mixture models

    The Kullback-Leibler divergence is a widespread dissimilarity measure between probability density functions, based on the Shannon entropy. Unfortunately, there is no analytic formula available to compute this divergence between mixture models, imposing the use of costly approximation algorithms. In order to reduce the computational burden when a lot of divergence evaluations are needed, we introduce a sub-class of the mixture models where the component parameters are shared between a set of mixtures and the only degree-of-freedom is the vector of weights of each mixture. This sharing allows to design extremely fast versions of existing dissimilarity measures between mixtures. We demonstrate the effectiveness of our approach by evaluating the quality of the ordering produced by our method on a real dataset.

  • Unmixing based Change Detection for Hyperspectral Images with Endmember Variability

    Unmixing based change detection (UBCD) provides subpixel level information on the nature of the changes that occur in a temporal image series, in addition to providing a multi-output change detection map. These advantages have recently carried UBCD to prominence among change detection approaches for hyperspectral images. However, most, if not all, works on UBCD operate with the assumption that the endmembers do not vary in character temporally or locally. This assumption often fails to uphold, as intrinsic variability of endmembers is a significant concern for most real datasets, in addition to endmember variability occurring in temporal images due to changes in lighting or acquisition conditions. This paper proposes unmixing with the recently proposed extended linear mixture model (ELMM) to address spectral variability for UBCD and highlights its advantage with respect to UBCD with the linear mixture model (LMM) through experiments on synthetic and real datasets.

  • Higher order log-cumulants for texture analysis of PolSAR data

    The log-cumulants of the second and third order are widely used to determine the statistical model of PolSAR data. However, same values of these statistics could result from both the product model and the mixture model, which represent two different physical scenarios. In other words, there is an ambiguity between the texture and the mixture according to these statistics. In this work, the log-cumulant of the fourth order is demonstrated to be useful to eliminate this ambiguity. The use of higher order statistics is helpful and necessary when analyzing the texture of PolSAR data.

  • Ultra-thin Camera – Compound Eye Vision Approach

    In this paper we issue the camera form factor and look into the optical limitations of the conventional camera system. Our research objective is to develop a planar (very thin) form factor camera. We proposed a multi-aperture optics, which is inspired by the compound eyes of insects, and a light field mixture model that describes how the light field information is captured and mixed by a geometric relation between the sensor and the lens. We verified the feasibility of the proposed idea at the thin camera design with a total track length less than 1 mm.



Standards related to Mixture Models

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Jobs related to Mixture Models

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