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Automatic Locating of Anthropometric Landmarks on 3D Human Models

Automatic Locating of Anthropometric Landmarks on 3D Human Models

Automatic Locating of Anthropometric Landmarks on 3D Human Models. Zouhour Ben Azouz and Chang Shu. National Research Council of Canada Institute of Information Technology Visual Information Technology Group. Anthropometry. Traditional Anthropometry . 3D surface Anthropometry .

By rasia
(194 views)

Graphical Models and Applications

Graphical Models and Applications

Graphical Models and Applications. CNS/EE148 Instructors: M.Polito, P.Perona, R.McEliece TA: C. Fanti. Example from Medical Diagnostics. Visit to Asia. Smoking. Patient Information. Tuberculosis. Lung Cancer. Bronchitis. Medical Difficulties. Tuberculosis or Cancer. XRay Result.

By liona
(110 views)

Bayesian Methods

Bayesian Methods

Bayesian Methods. Will Penny and Guillaume Flandin. Wellcome Department of Imaging Neuroscience, University College London, UK. SPM Course, London, May 12 th 2006. Overview. Bayes rule and model comparison ANOVAs Normalisation Segmentation fMRI stats Hemodynamic models

By aulani
(155 views)

Super-Resolution

Super-Resolution

Super-Resolution. Digital Photography CSE558, Spring 2003 Richard Szeliski. Super-resolution. convolutions, blur, and de-blurring Bayesian methods Wiener filtering and Markov Random Fields sampling, aliasing, and interpolation multiple (shifted) images prior-based methods MRFs

By clem
(285 views)

Region description

Region description

Region description. Information that let’s you recognise a region. Introduction. Region detection isolates regions that differ from neighbours Description identifies property values Labelling identifies regions. Contents. Features derived from binary images Structure Region (CCA) Shape

By evelina
(129 views)

Computational Vision

Computational Vision

Computational Vision. Jitendra Malik University of California at Berkeley. Taxonomy of Vision Problems. Reconstruction: estimate parameters of external 3D world. Visual Control: visually guided locomotion and manipulation. Segmentation: partition I(x,y,t) into subsets of separate objects.

By colman
(97 views)

Stereo Matching & Energy Minimization

Stereo Matching & Energy Minimization

Stereo Matching & Energy Minimization. Vision for Graphics CSE 590SS, Winter 2001 Richard Szeliski. Stereo Matching. What are some possible algorithms? match “features” and interpolate match edges and interpolate match all pixels with windows (coarse-fine) use optimization:

By bowen
(207 views)

Recovering Intrinsic Images from a Single Image

Recovering Intrinsic Images from a Single Image

Recovering Intrinsic Images from a Single Image. Shadows Removal Seminar. 28/12/05. Dagan Aviv. Relies on:.

By hua
(206 views)

Lecture 24: More on Reflectance

Lecture 24: More on Reflectance

Lecture 24: More on Reflectance. CAP 5415. Recovering Shape. We’ve talked about photometric stereo, where we assumed that a surface was diffuse Could calculate surface normals and albedo What if the surface isn’t diffuse?. Basic Idea. Assume illumination is distant

By paulos
(67 views)

Structured Belief Propagation for NLP

Structured Belief Propagation for NLP

Structured Belief Propagation for NLP. Matthew R. Gormley & Jason Eisner ACL ‘14 Tutorial June 22, 2014. For the latest version of these slides, please visit: http://www.cs.jhu.edu/~mrg/bp-tutorial/. Structured representations of utterances Structured knowledge of the language .

By beck
(190 views)

Texture

Texture

Texture. Limitation of pixel based processing. Edge detection with different threshold. What is texture ?. There is no accurate definition. It is often used to represent all the “details” in the image. (F.e, sometimes images are divided to shape + texture.

By kalani
(180 views)

Midterm Review

Midterm Review

Midterm Review. Monday, November 4 7:15 – 9:15 p.m. in room 1240 CS Closed book One 8.5  x 11  sheet of notes on both sides allowed Bring a calculator (no phones) Main ideas as covered in lecture slides Readings give more details and therefore are supplementary material. Major Topics.

By heidi
(182 views)

Inpainting

Inpainting

Inpainting. Ya -Fan Su and Tao- Sheng Ou Group 31. A. Criminisi , P. Perez, and K. Toyama, "Region Filling and Object Removal by Exemplar-Based Image Inpainting ," IEEE Trans. Image Processing , 13(9), pp. 1200-1212, September 2004.

By alta
(201 views)

Abstract

Abstract

O. 2. O. 1. Total number of active molecules. O. 3. Photoblinking Model ( β ON > β OFF ). Photobleaching Model . ;. Denoising of Fluorescent Confocal Microscopy images affected by the Photoblinking/Photobleaching effects. Isabel Rodrigues and J.Miguel Sanches

By lise
(169 views)

Sébastien Leprince 1 , Jiao Lin 1 , Francois Ayoub 1 , B. Conejo 1 ,

Sébastien Leprince 1 , Jiao Lin 1 , Francois Ayoub 1 , B. Conejo 1 ,

3D High Resolution Tracking of Ice Flow using Multi-Temporal S tereo Imagery: Franz Josef Glacier, New Zealand. Sébastien Leprince 1 , Jiao Lin 1 , Francois Ayoub 1 , B. Conejo 1 , F. Herman 2 , and Jean-Philippe Avouac 1 1 California Institute of Technology

By gayora
(112 views)

Exact Inference on Graphical Models

Exact Inference on Graphical Models

Exact Inference on Graphical Models. Samson Cheung. Outline . What is inference? Overview Preliminaries Three general algorithms for inference Elimination Algorithm Belief Propagation Junction Tree. What is inference?.

By harlow
(177 views)

Epipolar lines

Epipolar lines

Epipolar lines. epipolar plane. epipolar lines. epipolar lines. Baseline. O’. O. Rectification.

By ponce
(101 views)

Bayesian Belief Propagation and Image Interpretation

Bayesian Belief Propagation and Image Interpretation

Bayesian Belief Propagation and Image Interpretation. March 13, 2002. Presenter: David Rosenberg. Overview. Deals with problems in which we want to estimate local scene properties that may depend, to some extent, on global properties

By imala
(153 views)

Identifying Differentially Regulated Genes

Identifying Differentially Regulated Genes

Identifying Differentially Regulated Genes. Nirmalya Bandyopadhyay, Manas Somaiya, Sanjay Ranka, and Tamer Kahveci Bioinformatics Lab., CISE Department, University of Florida. Gene interaction through regulatory networks.

By marcia-stephenson
(90 views)

A Markov Random Field Model for Term Dependencies

A Markov Random Field Model for Term Dependencies

A Markov Random Field Model for Term Dependencies. Hongyu Li & Chaorui Chang. Background. Dependencies exist between terms in a collection of text Estimating statistical models for general term dependencies is infeasible due to data sparsity

By gracejohnson
(0 views)

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