PCA: Hand modelling Nikzad B.Rizvandi.

Slides:

Advertisements

Similar presentations

Face Recognition Sumitha Balasuriya.

Advertisements

Active Shape Models Suppose we have a statistical shape model –Trained from sets of examples How do we use it to interpret new images? Use an “Active Shape.

1 Registration of 3D Faces Leow Wee Kheng CS6101 AY Semester 1.

Medical Image Registration Kumar Rajamani. Registration Spatial transform that maps points from one image to corresponding points in another image.

Automatic determination of skeletal age from hand radiographs of children Image Science Institute Utrecht University C.A.Maas.

As applied to face recognition.  Detection vs. Recognition.

, Tim Landgraf Active Appearance Models AG KI, Journal Club 03 Nov 2008.

A 4-WEEK PROJECT IN Active Shape and Appearance Models

Face alignment using Boosted Appearance Model (BAM)

© 2003 by Davi GeigerComputer Vision September 2003 L1.1 Face Recognition Recognized Person Face Recognition.

3D Geometry for Computer Graphics

Active Appearance Models master thesis presentation Mikkel B. Stegmann IMM – June 20th 2000.

Pattern Recognition Topic 1: Principle Component Analysis Shapiro chap

Rodent Behavior Analysis Tom Henderson Vision Based Behavior Analysis Universitaet Karlsruhe (TH) 12 November /9.

Principal Component Analysis IML Outline Max the variance of the output coordinates Optimal reconstruction Generating data Limitations of PCA.

Face Recognition Based on 3D Shape Estimation

Announcements Take home quiz given out Thursday 10/23 –Due 10/30.

Active Appearance Models based on the article: T.F.Cootes, G.J. Edwards and C.J.Taylor. "Active Appearance Models", presented by Denis Simakov.

A Vision-Based System that Detects the Act of Smoking a Cigarette Xiaoran Zheng, University of Nevada-Reno, Dept. of Computer Science Dr. Mubarak Shah,

Statistical Shape Models Eigenpatches model regions –Assume shape is fixed –What if it isn’t? Faces with expression changes, organs in medical images etc.

PhD Thesis. Biometrics Science studying measurements and statistics of biological data Most relevant application: id. recognition 2.

Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.

Active Appearance Models for Face Detection

Statistical Models of Appearance for Computer Vision

Principle Component Analysis Presented by: Sabbir Ahmed Roll: FH-227.

CSE554AlignmentSlide 1 CSE 554 Lecture 8: Alignment Fall 2014.

Active Shape Models: Their Training and Applications Cootes, Taylor, et al. Robert Tamburo July 6, 2000 Prelim Presentation.

WP3 - 3D reprojection Goal: reproject 2D ball positions from both cameras into 3D space Inputs: – 2D ball positions estimated by WP2 – 2D table positions.

Out-of-plane Rotations Environment constraints ● Surveillance systems ● Car driver images ASM: ● Similarity does not remove 3D pose ● Multiple-view database.

Multimodal Interaction Dr. Mike Spann

CSE554AlignmentSlide 1 CSE 554 Lecture 5: Alignment Fall 2011.

Course 12 Calibration. 1.Introduction In theoretic discussions, we have assumed: Camera is located at the origin of coordinate system of scene.

1 ECE 738 Paper presentation Paper: Active Appearance Models Author: T.F.Cootes, G.J. Edwards and C.J.Taylor Student: Zhaozheng Yin Instructor: Dr. Yuhen.

Introduction EE 520: Image Analysis & Computer Vision.

CSE554AlignmentSlide 1 CSE 554 Lecture 8: Alignment Fall 2013.

Math 5364/66 Notes Principal Components and Factor Analysis in SAS Jesse Crawford Department of Mathematics Tarleton State University.

Medical Image Analysis Dr. Mohammad Dawood Department of Computer Science University of Münster Germany.

CSE 185 Introduction to Computer Vision Face Recognition.

Computer Vision Hough Transform, PDM, ASM and AAM David Pycock

Project 11: Determining the Intrinsic Dimensionality of a Distribution Okke Formsma, Nicolas Roussis and Per Løwenborg.

Project 11: Determining the Intrinsic Dimensionality of a Distribution Okke Formsma, Nicolas Roussis and Per Løwenborg.

Similar Figures, Day 2. Warm Up Two figures are similar if one can be obtained from the other using a sequence of transformations in the plane.

Last update Heejune Ahn, SeoulTech

Multimodal Interaction Dr. Mike Spann

EIGENSYSTEMS, SVD, PCA Big Data Seminar, Dedi Gadot, December 14 th, 2014.

Graph: A(4, 2) B(2, 0) C(6, -6) D(0, -4) E(-6, -6) F(-2, 0) G(-4, 2) H(0, 4)

Face Recognition Summary –Single pose –Multiple pose –Principal components analysis –Model-based recognition –Neural Networks.

Statistical Models of Appearance for Computer Vision 主講人：虞台文.

CSSE463: Image Recognition Day 10 Lab 3 due Weds Lab 3 due Weds Today: Today: finish circularity finish circularity region orientation: principal axes.

Obama and Biden, McCain and Palin Face Recognition Using Eigenfaces Justin Li.

Multimodal Interaction Dr. Mike Spann

Face detection and recognition Many slides adapted from K. Grauman and D. Lowe.

CSSE463: Image Recognition Day 10 Lab 3 due Weds, 11:59pm Lab 3 due Weds, 11:59pm Take-home quiz due Friday, 4:00 pm Take-home quiz due Friday, 4:00 pm.

Deformation Modeling for Robust 3D Face Matching Xioguang Lu and Anil K. Jain Dept. of Computer Science & Engineering Michigan State University.

Dilations A dilation is a transformation that produces an image that is the same shape as the original, but is a different size. A dilation stretches or.

CSE 554 Lecture 8: Alignment

Paper – Stephen Se, David Lowe, Jim Little

Spectral Methods Tutorial 6 1 © Maks Ovsjanikov

Statistical Shape Modelling

[ ] [ ] EXAMPLE 4 Use scalar multiplication in a dilation

Facial Recognition in Biometrics

Reconstructing Shredded Documents

Object Modeling with Layers

The SIFT (Scale Invariant Feature Transform) Detector and Descriptor

Recitation: SVD and dimensionality reduction

Digital Image Processing Lecture 21: Principal Components for Description Prof. Charlene Tsai *Chapter 11.4 of Gonzalez.

Paper Reading Dalong Du April.08, 2011.

Active Appearance Models theory, extensions & cases

Presented by Xu Miao April 20, 2005

Presentation transcript:

PCA: Hand modelling Nikzad B.Rizvandi

Hand tracking Looking for a tracking system: Face tracking Lips tracking Pose tracking

Dataset http://www2.imm.dtu.dk/~aam/datasets/datasets.html

Pre-processing Represent images with landmarks Images have: Different sizes Different rotations Different locations

Pre-processing: manual labeling Placing landmarks Manually placing N1 landmarks In the same location Placing intermediate points Automatic edge detection of edges between landmarks Points in total: N X=(x0, y0, x1, y1,…, xN-1, yN-1)T

Pre-processing Represent images with landmarks Alignment since images have: Different sizes Different rotations Different locations

Pre-processing: Alignment (1/3) Remove X-Y translation Move the Center of mass to coordinate origin Remove Scaling: Calculate L2-norm of the shape

Pre-processing: Alignment (2/3) Remove Rotation: Needs reference shape : Calculate Singular Value Decomposition(SVD) Rotation Matrix =

Pre-processing: Alignment (3/3) Align each shape to first shape by rotation, scaling, and translation Repeat Calculate the mean shape Normalize the orientation, scale, and origin of the current mean to suitable defaults Realign every shape with the current mean Until the mean shape does not change in two iterations

PCA to reduce dimension (1/4) The 2N elements are highly correlated, so we can represent them much more compactly Principal Component Analysis (PCA) Reduces the number of parameters from 2N to M, where M<<2N

PCA to reduce dimension (2/4)

PCA to reduce dimension (3/4) The shape of the model is given by: is the mean shape, most important eigenvectors, contains the shape parameters

PCA to reduce dimension (4/4) 40 shapes in database 56 landmarks (112 parameters) 95% of variation by 6 new parameters 112 parameters  6 new parameters

More information Dataset: http://www2.imm.dtu.dk/~aam/datasets/datasets.html Paper: Nikzad B.Rizvandi, A.Pizurica, W.Philips, “Deformable Shape Description Using Active Shape Model (ASM)”, 18th ProRISC Workshop on Circuits, Systems and Signal Processing (ProRISC), 2007, Netherlands