'Gaussian mixture model' diaporamas de présentation

Gist

909 views • 71 slides

CSE 5290: Algorithms for Bioinformatics Fall 2011

CSE 5290: Algorithms for Bioinformatics Fall 2011. Suprakash D at ta datta@cse.yorku.ca Office: CSEB 3043 Phone: 416-736-2100 ext 77875 Course page: http://www.cse.yorku.ca/course/5290. Next. Clustering revisited: Expectation Maximization, and Gaussian mixture model fitting

718 views • 71 slides

Clustering Techniques and Applications to Image Segmentation

Clustering Techniques and Applications to Image Segmentation. Liang Shan shan@cs.unc.edu. Roadmap. Unsupervised learning Clustering categories Clustering algorithms K-means Fuzzy c-means Kernel-based Graph-based Q&A. Unsupervised learning. Definition 1

1.67k views • 65 slides

Being Deep & Being Dynamic New-Generation Models & Methodology for Advancing Speech Technology

Being Deep & Being Dynamic New-Generation Models & Methodology for Advancing Speech Technology. Li Deng Microsoft Research, Redmond, USA Keynote at Odyssey Speaker/Language Recognition Workshop Singapore, June. 26, 2012 (including joint work with colleagues at MSR, U of Toronto, etc.) .

766 views • 48 slides

SPM Course May 2012 Segmentation and Voxel-Based Morphometry

SPM Course May 2012 Segmentation and Voxel-Based Morphometry. Ged Ridgway With thanks to John Ashburner and the FIL Methods Group. Overview. Unified segmentation Voxel-based morphometry (VBM) Spatial normalisation with Dartel. Segmentation into principal tissue types.

471 views • 47 slides

Vibhav Vineet, Jonathan Warrell, Paul Sturgess, Philip H.S. Torr

Improved Initialisation and Gaussian Mixture Pairwise Terms for Dense Random Fields with Mean-field Inference. Vibhav Vineet, Jonathan Warrell, Paul Sturgess, Philip H.S. Torr. http://cms.brookes.ac.uk/research/visiongroup/. Labelling Problem. Assign a label to each image pixel.

609 views • 46 slides

Prakash Chockalingam

Non-Rigid Multi-Modal Object Tracking Using Gaussian Mixture Models. Prakash Chockalingam. Committee Members Dr Stan Birchfield (chair) Dr Robert Schalkoff Dr Brian Dean. Clemson University. Tracking Overview. Tracker Tasks. Feature Descriptors. Object Detection. Object Model.

433 views • 31 slides

Graph cut

Graph cut . Chien -chi Chen. Outline. Introduction Interactive segmentation Related work Graph cut Concept of graph cut Hard and smooth constrains Min cut/Max flow Extensive of Graph cut Grab cut Paint Selection Unsupervise graph cut Conclusion Reference. Outline. Introduction

698 views • 27 slides

Lecture 7. Kernel Smoothing Methods

Lecture 7. Kernel Smoothing Methods. Instructed by Jinzhu Jia. Outline. Description of Kernel One-dimensional Kernel smoothing Selecting the Width of the Kernel Local Regression in R p Kernel Density Estimation and Classification. Nearest Neighbor Smoothing.

677 views • 27 slides

ICASSP 2006 Robustness Techniques Survey

ICASSP 2006 Robustness Techniques Survey. ShihHsiang 2006.

386 views • 25 slides

A Quantum-Statistical-Mechanical Extension of Gaussian Mixture Model

A Quantum-Statistical-Mechanical Extension of Gaussian Mixture Model. Kazuyuki Tanaka Graduate School of Information Sciences, Tohoku University, Sendai, Japan http://www.smapip.is.tohoku.ac.jp/~kazu/. In collaboration with Koji Tsuda

258 views • 25 slides

Novel CI- Backoff Scheme for Real-time Embedded Speech Recognition

Novel CI- Backoff Scheme for Real-time Embedded Speech Recognition. Tao Ma Michael Deisher Mississippi State University Intel Corporation March 17, 2010 ICASSP 2010, Dallas Texas, USA. Results.

397 views • 22 slides

Speech Recognition

Speech Recognition. What makes speech recognition hard? . Speech Recognition. Task: Identify sequence of words uttered by speaker, given acoustic waveform. Uncertainty introduced by noise, speaker error, variation in pronunciation, homonyms, etc.

461 views • 17 slides

Dirichlet process tutorial

Dirichlet process tutorial. Bryan Russell. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A. Goals. Intuitive understanding of Dirichlet processes and applications Minimize math and maximize pictures

376 views • 17 slides

Aleksandar Ivanovic, Thomas S. Huang Backmen Institute for Advanced Science and Technology, UIUC

A Probabilistic Framework For Segmentation and Tracking of Multiple Non Rigid Objects for Video Surveillance. Aleksandar Ivanovic, Thomas S. Huang Backmen Institute for Advanced Science and Technology, UIUC. Outline. Introduction Pixel probability model Background model

278 views • 17 slides

Improved Adaptive Gaussian Mixture Model for Background

Improved Adaptive Gaussian Mixture Model for Background. Zoran Zivkovic Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. Outline. Introduction Gaussian Mixture Model Select the number of components Experiments Conclusion. Introduction.

824 views • 16 slides

Application of light fields in computer vision

310 views • 12 slides

Lecture 11

Lecture 11. Generalizations of EM. Last Time. Example of Gaussian mixture model. E-step: compute sufficient statistics w.r.t. posterior M-step: maximize Q. MoG_demo. Generalizations. Map-EM: include prior for parameters. EM computes maximum a-posteriori distribution.

127 views • 4 slides