Figures from: Turk & Pentland, Eigenfaces for Recognition. - PowerPoint PPT Presentation

Apr 05, 2023 •340 likes •505 views

Figures from: Turk & Pentland, Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3 :1, 1991. Chan & Mozurkewich, Simplified representation of atmospheric aerosol size distributions using absolute principal components

Figures from: Turk & Pentland, “Eigenfaces for Recognition.” Journal of Cognitive Neuroscience 3 :1, 1991. Chan & Mozurkewich, “Simplified representation of atmospheric aerosol size distributions using absolute principal components analysis.” Atmospheric Chemistry and Physics 7 , 875–886, 2007.
20 signal components 15 1/2 mixed components ( Σ Unused Eigenvalues) noise components 10 5 0 0 5 10 15 20 25 30 Number of Components Retained 5
20 20 signal components 15 1/2 15 mixed components 1/2 ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) noise components 10 10 5 5 0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 Number of Components Retained Number Components Retained 30 5
Number of Components Retained 20 5 20 signal components 4 15 1/2 15 1/2 mixed components 1/2 ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) noise components 3 10 10 2 5 5 1 0 0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30 Number of Components Retained Number of Components Retained Number Components Retained 8 30 5
Number of Components Retained Number Components Retained 20 5 30 20 25 signal components 4 15 1/2 15 1/2 mixed components 1/2 1/2 ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) noise components 20 3 10 10 15 2 10 5 5 1 5 0 0 0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30 35 Number of Components Retained Number of Component Retained Number of Components Retained Number Components Retained 8 30 5
Number of Components Retained Number Components Retained 20 8 5 30 20 25 signal components 4 15 6 1/2 1/2 15 1/2 mixed components 1/2 1/2 ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) ( Σ Unused Eigenvalues) noise components 20 3 4 10 10 15 2 10 2 5 5 1 5 0 0 0 0 0 0 0 5 5 10 10 15 15 20 20 25 25 30 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30 35 Number of Components Retained Number of Component Retained Number of Components Retained Number of Components Retained Number Components Retained 8 30 5
4 3 2 1 0 2 3 4 5 6 2 3 4 5 6 10 100 Diameter (nm)

Recommend

Face Recognition Intro to recognition PCA and Eigenfaces LDA and Fisherfaces

COS 429: COMPUTER VISON Face Recognition Intro to recognition PCA and Eigenfaces LDA and Fisherfaces Face detection: Viola & Jones (Optional) generic object models for faces: the Constellation Model Reading: Turk

1.17k views • 92 slides

Early Face Recognition Systems in Computer Vision Kanade feature-based face recognition (1973!)

2/7/17 Early Face Recognition Systems in Computer Vision Kanade feature-based face recognition (1973!) (first complete automated system) Introduction to Principal Components Analysis Eigenfaces method for face recognition (Turk & Pentland,

92 views • 5 slides

Representation in Low-Level Visual Learning Erik Sudderth Brown University Department of

Representation in Low-Level Visual Learning Erik Sudderth Brown University Department of Computer Science Generative Models: A Caricature Turk & Pentland 1991, Moghaddam & Pentland 1995 Training Faces Mean Face Eigenfaces Gaussian

481 views • 26 slides

The Mug-Shot Search Problem Ellie Baker and Margo Seltzer Harvard University, Cambridge, MA., USA

The Mug-Shot Search Problem Ellie Baker and Margo Seltzer Harvard University, Cambridge, MA., USA ellie@eecs.harvard.edu Face Recognition plus Composite Creation Eigenfaces (Turk & Pentland 1991) Uses PCA to compress images to a

297 views • 14 slides

Face detection and recognition Detection Recognition Sally Face detection &

Face detection and recognition Detection Recognition Sally Face detection & recognition Viola & Jones detector Available in open CV Face recognition Eigenfaces for face recognition Eigenfaces for face

639 views • 53 slides

Facial Expression Recognition Facial Expression Recognition using a Dynamic Model using a

Facial Expression Recognition Facial Expression Recognition using a Dynamic Model using a Dynamic Model and Motion Energy and Motion Energy Irfan Essa, Essa, Irfan Alex Pentland Alex Pentland (a review by Paul Fitzpatrick for 6.892) (a

734 views • 25 slides

Discriminant Analysis Aleix M. Martinez aleix@ece.osu.edu PCA Eigenfaces (PCA) 1 Linear

Machine Learning & Pattern Recognition Discriminant Analysis Aleix M. Martinez aleix@ece.osu.edu PCA Eigenfaces (PCA) 1 Linear Discriminant Analysis If we have samples corresponding to two or more classes, we prefer to select those

442 views • 8 slides

Selective Search for Object Recognition Uijlings et al. (IJCV 2013) Some figures are from

Selective Search for Object Recognition Uijlings et al. (IJCV 2013) Some figures are from http://vision.stanford. edu/teaching/cs231b_spring1415/slides/ssearch_schuyler.pdf Object Recognition Find Object and Recognize it Contribution

391 views • 20 slides

Text-based and Image-based Recognition and Extraction of Molecular Information from Figures and

Second Workshop on Data, Text, Web, and Social Network Mining April 22, 2011 Text-based and Image-based Recognition and Extraction of Molecular Information from Figures and Figure Captions Jungkap Park, Gus R. Rosania & Kazuhiro Saitou

564 views • 22 slides

BBM406 Fundamentals of Machine Learning Lecture 23: Dimensionality Reduction Aykut Erdem //

Image credit: Matthew Turk and Alex Pentland BBM406 Fundamentals of Machine Learning Lecture 23: Dimensionality Reduction Aykut Erdem // Hacettepe University // Fall 2019 Administrative Project Presentations January 8,10, 2020 Each

1.6k views • 87 slides

Amazon Mechanical Turk IRB C ONSIDERATIONS February 9, 2017 Adam F. Bailey, MA, CIP Social and

Amazon Mechanical Turk IRB C ONSIDERATIONS February 9, 2017 Adam F. Bailey, MA, CIP Social and Behavioral IRB Manager Stanford University afbailey@stanford.edu The Mechanical Turk chess playing automaton Amazon Mechanical Turk HITs

511 views • 21 slides

Turk workflow and tools . mitcho + Hadas mitcho,hkotek@mit.edu Hackl Lab Turkshop March 2013

. Turk workflow and tools . mitcho + Hadas mitcho,hkotek@mit.edu Hackl Lab Turkshop March 2013 . . Review from week 1 How do you run something on Turk? 1 Log in to Turk . . 2 Create a new experiment (probably duplicate a previous one) .

453 views • 21 slides

CSE 152: Computer Vision Hao Su Lecture 10: Object Recognition How do we represent objects -

CSE 152: Computer Vision Hao Su Lecture 10: Object Recognition How do we represent objects - Bounding box Figures from https://github.com/facebookresearch/detectron2 How do we represent objects - Bounding box - Instance mask Figures from

489 views • 23 slides

Fitting a transformation: feature-based alignment Tues Oct 13 Motivation: Recognition Figures

10/12/2015 Fitting a transformation: feature-based alignment Tues Oct 13 Motivation: Recognition Figures from David Low e 1 10/12/2015 Motivation: medical image registration Motivation: mosaics Image f rom

320 views • 31 slides

Machine Learning for Signal Processing Eigenfaces and Eigenrepresentations Class 6. 17 Sep 2013

Machine Learning for Signal Processing Eigenfaces and Eigenrepresentations Class 6. 17 Sep 2013 Instructor: Bhiksha Raj 17 Sep 2013 11-755/18-797 1 Administrivia Project teams? By the end of the month.. Project proposals?

1.42k views • 106 slides

Eigenfaces John Schavemaker, Wessel Kraaij, Pieter Eendebak, Mark van Staalduinen John

Exhaustive SURF, small bag-of-words and Eigenfaces John Schavemaker, Wessel Kraaij, Pieter Eendebak, Mark van Staalduinen John Schavemaker TNO instance search submission TNO Netherlands research organization for Applied Scientific Research

569 views • 38 slides

8-Speech Recognition Speech Recognition Concepts Speech Recognition Approaches

8-Speech Recognition Speech Recognition Concepts Speech Recognition Approaches Recognition Theories Bayse Rule Simple Language Model P(A|W) Network Types 1 7-Speech Recognition (Cont d) HMM Calculating Approaches

1.08k views • 74 slides

EMPLOYEE RECOGNITION OBJECTIVES Types of recognition Creating a culture of recognition

EMPLOYEE RECOGNITION OBJECTIVES Types of recognition Creating a culture of recognition Recognition ideas EXERCISE: Think of a time when you felt appreciated or were recognized. What happened? What did the person do?

451 views • 12 slides

Experience in using Mechanical Turk for Network Measurement

Experience in using Mechanical Turk for Network Measurement Gokay Huz (NPS), Steven Bauer (MIT), kc claffy (CAIDA), Robert Beverly (NPS) ACM

528 views • 19 slides

on Facebook Q&A with Katie Richman, Facebook Media Solutions Craig Pentland SocialFlow VP,

Prioritizing and SUCCEEDING with video on Facebook Q&A with Katie Richman, Facebook Media Solutions Craig Pentland SocialFlow VP, Client Services Facebook receives 4 BILLION video views DAILY In June 2014 the number was just one billion,

445 views • 10 slides

Donor Recognition NPS ~ Donor Recognition Donor recognition is an important and critical for

Donor Recognition NPS ~ Donor Recognition Donor recognition is an important and critical for parks and as a component to a partner fundraising efforts The NPS wants to honor and appropriately recognize donors while respecting the visitor

540 views • 17 slides

Generalized Implicit Functions for Computer Graphics by Stan Sclaroff & Alex Pentland of MIT

Generalized Implicit Functions for Computer Graphics by Stan Sclaroff & Alex Pentland of MIT presented at SIGGRAPH in July 1991 The Problem The problem is common Detection and characterization of collisions is a common problem in both

286 views • 16 slides

1Q18 Key figures Figures in million YoY Var. Like for Like Var. 1 8,671 SALES +3,7 % +14,7 %

1Q18 Key figures Figures in million YoY Var. Like for Like Var. 1 8,671 SALES +3,7 % +14,7 % +16.9 % 473 +7.9 % EBIT +13.4 % 250 +7.4 % NET PROFIT 1,603 + 40.9 % NET OPERATING CASH FLOW (2) LTM 359 -78.8 % NET DEBT +10.3 % 66,677 -2,1

274 views • 11 slides

CS475/CS675 Lecture 23: July 19, 2016 Principal Component Analysis, Eigenfaces CS475/CS675 (c)

CS475/CS675 Lecture 23: July 19, 2016 Principal Component Analysis, Eigenfaces CS475/CS675 (c) 2016 P. Poupart 1 Principal Component Analysis (PCA) Data exploration technique: Dimensionality reduction Principal components are axes

660 views • 10 slides