Geometric Registration for Deformable Shapes. 2.1 ICP + Tangent Space optimization for Rigid Motions

Size: px
Start display at page:

Download "Geometric Registration for Deformable Shapes. 2.1 ICP + Tangent Space optimization for Rigid Motions"

Transcription

1 Geometrc Regstraton for Deformable Shapes 2.1 ICP + Tangent Space optmzaton for Rgd Motons

2 Regstraton Problem Gven Two pont cloud data sets P (model) and Q (data) sampled from surfaces Φ P and Φ Q respectvely. Q P data model Assume Φ Q s a part of Φ P. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

3 Regstraton Problem Gven Two pont cloud data sets P and Q. Goal Regster Q aganst P by mnmzng the squared dstance between the underlyng surfaces usng only rgd transforms. Q P data model Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

4 Notatons P = { p } Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

5 Regstraton wth known Correspondence { p }and{ q }such that p q Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

6 Regstraton wth known Correspondence { p }and{ q }such that p q p Rp + t mn R, t Rp + t q 2 R obtaned usng SVD of covarance matrx. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

7 Regstraton wth known Correspondence { p }and{ q }such that p q p Rp + t mn R, t Rp + t q 2 R obtaned usng SVD of covarance matrx. t = q R p Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

8 ICP (Iterated Closest Pont) Iteratve mnmzaton algorthms (ICP) [Besl 92, Chen 92] 1. Buld a set of correspondng ponts 2. Algn correspondng ponts 3. Iterate Propertes Dense correspondence sets Converges f startng postons are close Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

9 No (explct) Correspondence Φ P Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

10 Squared Dstance Functon (F) x Φ P Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

11 Squared Dstance Functon (F) x d Φ P F( x, Φ P ) = d 2 Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

12 Regstraton Problem Rgd transform α that takes ponts q α ( q ) Our goal s to solve for, mn α q Q F ( α( q ), Φ ) P An optmzaton problem n the squared dstance feld of P, the model PCD. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

13 Regstraton Problem α = rotaton ( R ) + translaton( t) Our goal s to solve for, mn R, t q Q F ( Rq + t, Φ ) P Optmze for R and t. Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

14 Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes Regstraton n 2D ),, ( y t x t θ ε Mnmze resdual error = 1 M 2 M t t y x θ depends on F + data PCD (Q).

15 Approxmate Squared Dstance For a curve Ψ, Ψ d F( x, Ψ) = x1 + x2 = δ1x1 x2 d-ρ1 Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes [ Pottmann and Hofer 2003 ]

16 Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes ICP n Our Framework 0 )) ( ( ), ( 2 = = Φ j n F δ p x x P 1 ) ( ), ( 2 = = Φ j F δ p x x P Pont-to-plane ICP (good for small d) Pont-to-pont ICP (good for large d)

17 Example d2trees 2D 3D Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

18 Convergence Funnel Translaton n x-z plane. Rotaton about y-axs. Converges Does not converge Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

19 Convergence Funnel Plane-to-plane ICP Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes dstance-feld formulaton

20 Descrptors P = { p } closest pont based on Eucldean dstance Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

21 Descrptors P = { p } closest pont based on Eucldean dstance P = { p, a, b,...} closest pont based on Eucldean dstance between pont + descrptors (attrbutes) Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

22 (Invarant) Descrptors P = { p } closest pont based on Eucldean dstance P = { p, a, b,...} closest pont based on Eucldean dstance between pont + descrptors (attrbutes) Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

23 Integral Volume Descrptor 0.20 Relaton to mean curvature Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

24 When Objects are Poorly Algned Use descrptors for global regstratons global algnment refnement wth local (e.g., ICP) Eurographcs 2010 Course Geometrc Regstraton for Deformable Shapes

Efficient, General Point Cloud Registration with Kernel Feature Maps

Efficient, General Point Cloud Registration with Kernel Feature Maps Effcent, General Pont Cloud Regstraton wth Kernel Feature Maps Hanchen Xong, Sandor Szedmak, Justus Pater Insttute of Computer Scence Unversty of Innsbruck 30 May 2013 Hanchen Xong (Un.Innsbruck) 3D Regstraton

More information

Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors

Point cloud to point cloud rigid transformations. Minimizing Rigid Registration Errors Pont cloud to pont cloud rgd transformatons Russell Taylor 600.445 1 600.445 Fall 000-015 Mnmzng Rgd Regstraton Errors Typcally, gven a set of ponts {a } n one coordnate system and another set of ponts

More information

CS 468 Lecture 16: Isometry Invariance and Spectral Techniques

CS 468 Lecture 16: Isometry Invariance and Spectral Techniques CS 468 Lecture 16: Isometry Invarance and Spectral Technques Justn Solomon Scrbe: Evan Gawlk Introducton. In geometry processng, t s often desrable to characterze the shape of an object n a manner that

More information

1 Derivation of Point-to-Plane Minimization

1 Derivation of Point-to-Plane Minimization 1 Dervaton of Pont-to-Plane Mnmzaton Consder the Chen-Medon (pont-to-plane) framework for ICP. Assume we have a collecton of ponts (p, q ) wth normals n. We want to determne the optmal rotaton and translaton

More information

CS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras

CS4495/6495 Introduction to Computer Vision. 3C-L3 Calibrating cameras CS4495/6495 Introducton to Computer Vson 3C-L3 Calbratng cameras Fnally (last tme): Camera parameters Projecton equaton the cumulatve effect of all parameters: M (3x4) f s x ' 1 0 0 0 c R 0 I T 3 3 3 x1

More information

Body Models I-2. Gerard Pons-Moll and Bernt Schiele Max Planck Institute for Informatics

Body Models I-2. Gerard Pons-Moll and Bernt Schiele Max Planck Institute for Informatics Body Models I-2 Gerard Pons-Moll and Bernt Schele Max Planck Insttute for Informatcs December 18, 2017 What s mssng Gven correspondences, we can fnd the optmal rgd algnment wth Procrustes. PROBLEMS: How

More information

Solutions HW #2. minimize. Ax = b. Give the dual problem, and make the implicit equality constraints explicit. Solution.

Solutions HW #2. minimize. Ax = b. Give the dual problem, and make the implicit equality constraints explicit. Solution. Solutons HW #2 Dual of general LP. Fnd the dual functon of the LP mnmze subject to c T x Gx h Ax = b. Gve the dual problem, and make the mplct equalty constrants explct. Soluton. 1. The Lagrangan s L(x,

More information

Meshless Surfaces. presented by Niloy J. Mitra. An Nguyen

Meshless Surfaces. presented by Niloy J. Mitra. An Nguyen Meshless Surfaces presented by Nloy J. Mtra An Nguyen Outlne Mesh-Independent Surface Interpolaton D. Levn Outlne Mesh-Independent Surface Interpolaton D. Levn Pont Set Surfaces M. Alexa, J. Behr, D. Cohen-Or,

More information

Structure from Motion. Forsyth&Ponce: Chap. 12 and 13 Szeliski: Chap. 7

Structure from Motion. Forsyth&Ponce: Chap. 12 and 13 Szeliski: Chap. 7 Structure from Moton Forsyth&once: Chap. 2 and 3 Szelsk: Chap. 7 Introducton to Structure from Moton Forsyth&once: Chap. 2 Szelsk: Chap. 7 Structure from Moton Intro he Reconstructon roblem p 3?? p p 2

More information

2. PROBLEM STATEMENT AND SOLUTION STRATEGIES. L q. Suppose that we have a structure with known geometry (b, h, and L) and material properties (EA).

2. PROBLEM STATEMENT AND SOLUTION STRATEGIES. L q. Suppose that we have a structure with known geometry (b, h, and L) and material properties (EA). . PROBEM STATEMENT AND SOUTION STRATEGIES Problem statement P, Q h ρ ρ o EA, N b b Suppose that we have a structure wth known geometry (b, h, and ) and materal propertes (EA). Gven load (P), determne the

More information

Generalized Penetration Depth Computation based on Kinematical Geometry

Generalized Penetration Depth Computation based on Kinematical Geometry Generalzed Penetraton Depth Computaton based on Knematcal Geometry Georg Nawratl a Helmut Pottmann a Bahram Ravan b a Insttute of Dscrete Mathematcs and Geometry, Venna Unversty of Technology, Wedner Hauptstrasse

More information

Topic 5: Non-Linear Regression

Topic 5: Non-Linear Regression Topc 5: Non-Lnear Regresson The models we ve worked wth so far have been lnear n the parameters. They ve been of the form: y = Xβ + ε Many models based on economc theory are actually non-lnear n the parameters.

More information

T f. Geometry. R f. R i. Homogeneous transformation. y x. P f. f 000. Homogeneous transformation matrix. R (A): Orientation P : Position

T f. Geometry. R f. R i. Homogeneous transformation. y x. P f. f 000. Homogeneous transformation matrix. R (A): Orientation P : Position Homogeneous transformaton Geometr T f R f R T f Homogeneous transformaton matr Unverst of Genova T f Phlppe Martnet = R f 000 P f 1 R (A): Orentaton P : Poston 123 Modelng and Control of Manpulator robots

More information

Projective change between two Special (α, β)- Finsler Metrics

Projective change between two Special (α, β)- Finsler Metrics Internatonal Journal of Trend n Research and Development, Volume 2(6), ISSN 2394-9333 www.jtrd.com Projectve change between two Specal (, β)- Fnsler Metrcs Gayathr.K 1 and Narasmhamurthy.S.K 2 1 Assstant

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

Protein Structure Comparison

Protein Structure Comparison Proten Structure Comparson Proten Structure Representaton CPK: hard sphere model Ball-and-stck Cartoon Degrees of Freedom n Protens Bond length Dhedral angle 3 4 Bond angle + Proten Structure: Varables

More information

ρ some λ THE INVERSE POWER METHOD (or INVERSE ITERATION) , for , or (more usually) to

ρ some λ THE INVERSE POWER METHOD (or INVERSE ITERATION) , for , or (more usually) to THE INVERSE POWER METHOD (or INVERSE ITERATION) -- applcaton of the Power method to A some fxed constant ρ (whch s called a shft), x λ ρ If the egenpars of A are { ( λ, x ) } ( ), or (more usually) to,

More information

Chapter 11: Angular Momentum

Chapter 11: Angular Momentum Chapter 11: ngular Momentum Statc Equlbrum In Chap. 4 we studed the equlbrum of pontobjects (mass m) wth the applcaton of Newton s aws F 0 F x y, 0 Therefore, no lnear (translatonal) acceleraton, a0 For

More information

SELECTED SOLUTIONS, SECTION (Weak duality) Prove that the primal and dual values p and d defined by equations (4.3.2) and (4.3.3) satisfy p d.

SELECTED SOLUTIONS, SECTION (Weak duality) Prove that the primal and dual values p and d defined by equations (4.3.2) and (4.3.3) satisfy p d. SELECTED SOLUTIONS, SECTION 4.3 1. Weak dualty Prove that the prmal and dual values p and d defned by equatons 4.3. and 4.3.3 satsfy p d. We consder an optmzaton problem of the form The Lagrangan for ths

More information

p 1 c 2 + p 2 c 2 + p 3 c p m c 2

p 1 c 2 + p 2 c 2 + p 3 c p m c 2 Where to put a faclty? Gven locatons p 1,..., p m n R n of m houses, want to choose a locaton c n R n for the fre staton. Want c to be as close as possble to all the house. We know how to measure dstance

More information

Error Bars in both X and Y

Error Bars in both X and Y Error Bars n both X and Y Wrong ways to ft a lne : 1. y(x) a x +b (σ x 0). x(y) c y + d (σ y 0) 3. splt dfference between 1 and. Example: Prmordal He abundance: Extrapolate ft lne to [ O / H ] 0. [ He

More information

Gaussian Conditional Random Field Network for Semantic Segmentation - Supplementary Material

Gaussian Conditional Random Field Network for Semantic Segmentation - Supplementary Material Gaussan Condtonal Random Feld Networ for Semantc Segmentaton - Supplementary Materal Ravtea Vemulapall, Oncel Tuzel *, Mng-Yu Lu *, and Rama Chellappa Center for Automaton Research, UMIACS, Unversty of

More information

UNIVERSIDADE DE COIMBRA

UNIVERSIDADE DE COIMBRA UNIVERSIDADE DE COIMBRA DEPARTAMENTO DE ENGENHARIA ELECTROTÉCNICA E DE COMPUTADORES INSTITUTO DE SISTEMAS E ROBÓTICA 3030-290 COIMBRA, PORTUGAL ARTICLE: Pose Estmaton for Non-Central Cameras Usng Planes

More information

Adaptive Manifold Learning

Adaptive Manifold Learning Adaptve Manfold Learnng Jng Wang, Zhenyue Zhang Department of Mathematcs Zhejang Unversty, Yuquan Campus, Hangzhou, 327, P. R. Chna wroarng@sohu.com zyzhang@zju.edu.cn Hongyuan Zha Department of Computer

More information

Spin-rotation coupling of the angularly accelerated rigid body

Spin-rotation coupling of the angularly accelerated rigid body Spn-rotaton couplng of the angularly accelerated rgd body Loua Hassan Elzen Basher Khartoum, Sudan. Postal code:11123 E-mal: louaelzen@gmal.com November 1, 2017 All Rghts Reserved. Abstract Ths paper s

More information

Video Layer Extraction and Reconstruction

Video Layer Extraction and Reconstruction Vdeo Layer Extracton and Reconstructon Sylvan Pelleter 1, Françose Dbos 2 and Georges Koepfler 1 1 Unversty Pars Descartes, MAP5 (UMR CNRS 8145) 2 Unversty Pars Nord, LAGA (UMR CNRS 7539) MVA 2010-11 G.

More information

Vector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence.

Vector Norms. Chapter 7 Iterative Techniques in Matrix Algebra. Cauchy-Bunyakovsky-Schwarz Inequality for Sums. Distances. Convergence. Vector Norms Chapter 7 Iteratve Technques n Matrx Algebra Per-Olof Persson persson@berkeley.edu Department of Mathematcs Unversty of Calforna, Berkeley Math 128B Numercal Analyss Defnton A vector norm

More information

FUZZY GOAL PROGRAMMING VS ORDINARY FUZZY PROGRAMMING APPROACH FOR MULTI OBJECTIVE PROGRAMMING PROBLEM

FUZZY GOAL PROGRAMMING VS ORDINARY FUZZY PROGRAMMING APPROACH FOR MULTI OBJECTIVE PROGRAMMING PROBLEM Internatonal Conference on Ceramcs, Bkaner, Inda Internatonal Journal of Modern Physcs: Conference Seres Vol. 22 (2013) 757 761 World Scentfc Publshng Company DOI: 10.1142/S2010194513010982 FUZZY GOAL

More information

Performance of Different Algorithms on Clustering Molecular Dynamics Trajectories

Performance of Different Algorithms on Clustering Molecular Dynamics Trajectories Performance of Dfferent Algorthms on Clusterng Molecular Dynamcs Trajectores Chenchen Song Abstract Dfferent types of clusterng algorthms are appled to clusterng molecular dynamcs trajectores to get nsght

More information

APPENDIX F A DISPLACEMENT-BASED BEAM ELEMENT WITH SHEAR DEFORMATIONS. Never use a Cubic Function Approximation for a Non-Prismatic Beam

APPENDIX F A DISPLACEMENT-BASED BEAM ELEMENT WITH SHEAR DEFORMATIONS. Never use a Cubic Function Approximation for a Non-Prismatic Beam APPENDIX F A DISPACEMENT-BASED BEAM EEMENT WITH SHEAR DEFORMATIONS Never use a Cubc Functon Approxmaton for a Non-Prsmatc Beam F. INTRODUCTION { XE "Shearng Deformatons" }In ths appendx a unque development

More information

Tensor Analysis. For orthogonal curvilinear coordinates, ˆ ˆ (98) Expanding the derivative, we have, ˆ. h q. . h q h q

Tensor Analysis. For orthogonal curvilinear coordinates, ˆ ˆ (98) Expanding the derivative, we have, ˆ. h q. . h q h q For orthogonal curvlnear coordnates, eˆ grad a a= ( aˆ ˆ e). h q (98) Expandng the dervatve, we have, eˆ aˆ ˆ e a= ˆ ˆ a h e + q q 1 aˆ ˆ ˆ a e = ee ˆˆ ˆ + e. h q h q Now expandng eˆ / q (some of the detals

More information

MMA and GCMMA two methods for nonlinear optimization

MMA and GCMMA two methods for nonlinear optimization MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons

More information

Solutions to exam in SF1811 Optimization, Jan 14, 2015

Solutions to exam in SF1811 Optimization, Jan 14, 2015 Solutons to exam n SF8 Optmzaton, Jan 4, 25 3 3 O------O -4 \ / \ / The network: \/ where all lnks go from left to rght. /\ / \ / \ 6 O------O -5 2 4.(a) Let x = ( x 3, x 4, x 23, x 24 ) T, where the varable

More information

Army Ants Tunneling for Classical Simulations

Army Ants Tunneling for Classical Simulations Electronc Supplementary Materal (ESI) for Chemcal Scence. Ths journal s The Royal Socety of Chemstry 2014 electronc supplementary nformaton (ESI) for Chemcal Scence Army Ants Tunnelng for Classcal Smulatons

More information

Minimizing Algebraic Error in Geometric Estimation Problems

Minimizing Algebraic Error in Geometric Estimation Problems Mnmzng Algebrac n Geometrc Estmaton Problems Rchard I. Hartley G.E. Corporate Research and Development PO Box 8, Schenectady, NY 39 Emal : hartley@crd.ge.com Abstract Ths paper gves a wdely applcable technque

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Konstantn Tretyakov (kt@ut.ee) MTAT.03.227 Machne Learnng So far Supervsed machne learnng Lnear models Least squares regresson Fsher s dscrmnant, Perceptron, Logstc model Non-lnear

More information

FMA901F: Machine Learning Lecture 5: Support Vector Machines. Cristian Sminchisescu

FMA901F: Machine Learning Lecture 5: Support Vector Machines. Cristian Sminchisescu FMA901F: Machne Learnng Lecture 5: Support Vector Machnes Crstan Smnchsescu Back to Bnary Classfcaton Setup We are gven a fnte, possbly nosy, set of tranng data:,, 1,..,. Each nput s pared wth a bnary

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Konstantn Tretyakov (kt@ut.ee) MTAT.03.227 Machne Learnng So far So far Supervsed machne learnng Lnear models Non-lnear models Unsupervsed machne learnng Generc scaffoldng So far

More information

Modeling curves. Graphs: y = ax+b, y = sin(x) Implicit ax + by + c = 0, x 2 +y 2 =r 2 Parametric:

Modeling curves. Graphs: y = ax+b, y = sin(x) Implicit ax + by + c = 0, x 2 +y 2 =r 2 Parametric: Modelng curves Types of Curves Graphs: y = ax+b, y = sn(x) Implct ax + by + c = 0, x 2 +y 2 =r 2 Parametrc: x = ax + bxt x = cos t y = ay + byt y = snt Parametrc are the most common mplct are also used,

More information

Finite Element Modelling of truss/cable structures

Finite Element Modelling of truss/cable structures Pet Schreurs Endhoven Unversty of echnology Department of Mechancal Engneerng Materals echnology November 3, 214 Fnte Element Modellng of truss/cable structures 1 Fnte Element Analyss of prestressed structures

More information

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES BÂRZĂ, Slvu Faculty of Mathematcs-Informatcs Spru Haret Unversty barza_slvu@yahoo.com Abstract Ths paper wants to contnue

More information

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

CS 523: Computer Graphics, Spring Shape Modeling. PCA Applications + SVD. Andrew Nealen, Rutgers, /15/2011 1

CS 523: Computer Graphics, Spring Shape Modeling. PCA Applications + SVD. Andrew Nealen, Rutgers, /15/2011 1 CS 523: Computer Graphcs, Sprng 20 Shape Modelng PCA Applcatons + SVD Andrew Nealen, utgers, 20 2/5/20 emnder: PCA Fnd prncpal components of data ponts Orthogonal drectons that are domnant n the data (have

More information

Available online at ScienceDirect. Procedia Technology 14 (2014 ) Kailash Chaudhary*, Himanshu Chaudhary

Available online at   ScienceDirect. Procedia Technology 14 (2014 ) Kailash Chaudhary*, Himanshu Chaudhary Avalable onlne at www.scencedrect.com ScenceDrect Proceda Technology 4 (4 ) 35 4 nd Internatonal Conference on Innovatons n Automaton and Mechatroncs Engneerng, ICIAME 4 Optmum balancng of slder-crank

More information

PHYS 705: Classical Mechanics. Newtonian Mechanics

PHYS 705: Classical Mechanics. Newtonian Mechanics 1 PHYS 705: Classcal Mechancs Newtonan Mechancs Quck Revew of Newtonan Mechancs Basc Descrpton: -An dealzed pont partcle or a system of pont partcles n an nertal reference frame [Rgd bodes (ch. 5 later)]

More information

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

Rotation Invariant Shape Contexts based on Feature-space Fourier Transformation

Rotation Invariant Shape Contexts based on Feature-space Fourier Transformation Fourth Internatonal Conference on Image and Graphcs Rotaton Invarant Shape Contexts based on Feature-space Fourer Transformaton Su Yang 1, Yuanyuan Wang Dept of Computer Scence and Engneerng, Fudan Unversty,

More information

Inexact Newton Methods for Inverse Eigenvalue Problems

Inexact Newton Methods for Inverse Eigenvalue Problems Inexact Newton Methods for Inverse Egenvalue Problems Zheng-jan Ba Abstract In ths paper, we survey some of the latest development n usng nexact Newton-lke methods for solvng nverse egenvalue problems.

More information

Image Analysis. Active contour models (snakes)

Image Analysis. Active contour models (snakes) Image Analyss Actve contour models (snakes) Chrstophoros Nkou cnkou@cs.uo.gr Images taken from: Computer Vson course by Krsten Grauman, Unversty of Texas at Austn. Unversty of Ioannna - Department of Computer

More information

Lie Group Formulation of Articulated Rigid Body Dynamics

Lie Group Formulation of Articulated Rigid Body Dynamics Le Group Formulaton of Artculated Rgd Body Dynamcs Junggon Km 11/9/2012, Ver 1.01 Abstract It has been usual n most old-style text books for dynamcs to treat the formulas descrbng lnearor translatonal

More information

8/25/17. Data Modeling. Data Modeling. Data Modeling. Patrice Koehl Department of Biological Sciences National University of Singapore

8/25/17. Data Modeling. Data Modeling. Data Modeling. Patrice Koehl Department of Biological Sciences National University of Singapore 8/5/17 Data Modelng Patrce Koehl Department of Bologcal Scences atonal Unversty of Sngapore http://www.cs.ucdavs.edu/~koehl/teachng/bl59 koehl@cs.ucdavs.edu Data Modelng Ø Data Modelng: least squares Ø

More information

6.3.4 Modified Euler s method of integration

6.3.4 Modified Euler s method of integration 6.3.4 Modfed Euler s method of ntegraton Before dscussng the applcaton of Euler s method for solvng the swng equatons, let us frst revew the basc Euler s method of numercal ntegraton. Let the general from

More information

Introductory Optomechanical Engineering. 2) First order optics

Introductory Optomechanical Engineering. 2) First order optics Introductory Optomechancal Engneerng 2) Frst order optcs Moton of optcal elements affects the optcal performance? 1. by movng the mage 2. hgher order thngs (aberratons) The frst order effects are most

More information

Mean Field / Variational Approximations

Mean Field / Variational Approximations Mean Feld / Varatonal Appromatons resented by Jose Nuñez 0/24/05 Outlne Introducton Mean Feld Appromaton Structured Mean Feld Weghted Mean Feld Varatonal Methods Introducton roblem: We have dstrbuton but

More information

An Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors

An Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors An Algorthm to Solve the Inverse Knematcs Problem of a Robotc Manpulator Based on Rotaton Vectors Mohamad Z. Al-az*, Mazn Z. Othman**, and Baker B. Al-Bahr* *AL-Nahran Unversty, Computer Eng. Dep., Baghdad,

More information

(δr i ) 2. V i. r i 2,

(δr i ) 2. V i. r i 2, Cartesan coordnates r, = 1, 2,... D for Eucldean space. Dstance by Pythagoras: (δs 2 = (δr 2. Unt vectors ê, dsplacement r = r ê Felds are functons of poston, or of r or of {r }. Scalar felds Φ( r, Vector

More information

Lecture 10 Support Vector Machines. Oct

Lecture 10 Support Vector Machines. Oct Lecture 10 Support Vector Machnes Oct - 20-2008 Lnear Separators Whch of the lnear separators s optmal? Concept of Margn Recall that n Perceptron, we learned that the convergence rate of the Perceptron

More information

THE representation of high-dimensional signal sets with. Learning Smooth Pattern Transformation Manifolds. Elif Vural and Pascal Frossard

THE representation of high-dimensional signal sets with. Learning Smooth Pattern Transformation Manifolds. Elif Vural and Pascal Frossard 1 Learnng Smooth Pattern Transformaton Manfolds Elf Vural and Pascal Frossard that generate the nput mages,.e., the transformaton model, s known. However, we do not assume any pror algnment of the nput

More information

Mathematical Modeling to Support Gamma Radiation Angular Distribution Measurements

Mathematical Modeling to Support Gamma Radiation Angular Distribution Measurements Mathematcal Modelng to Support Gamma Radaton Angular Dstrbuton Measurements V. Baty, O. Stoyanov Insttute for Safety Problems of Nuclear Power Plants, Natonal Academy of Scences of Ukrane Ukrane D. Fedorchenko,

More information

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2 Physcs 607 Exam 1 Please be well-organzed, and show all sgnfcant steps clearly n all problems. You are graded on your wor, so please do not just wrte down answers wth no explanaton! Do all your wor on

More information

Invariant deformation parameters from GPS permanent networks using stochastic interpolation

Invariant deformation parameters from GPS permanent networks using stochastic interpolation Invarant deformaton parameters from GPS permanent networks usng stochastc nterpolaton Ludovco Bag, Poltecnco d Mlano, DIIAR Athanasos Dermans, Arstotle Unversty of Thessalonk Outlne Startng hypotheses

More information

Communication with AWGN Interference

Communication with AWGN Interference Communcaton wth AWG Interference m {m } {p(m } Modulator s {s } r=s+n Recever ˆm AWG n m s a dscrete random varable(rv whch takes m wth probablty p(m. Modulator maps each m nto a waveform sgnal s m=m

More information

La fonction à deux points et à trois points des quadrangulations et cartes. Éric Fusy (CNRS/LIX) Travaux avec Jérémie Bouttier et Emmanuel Guitter

La fonction à deux points et à trois points des quadrangulations et cartes. Éric Fusy (CNRS/LIX) Travaux avec Jérémie Bouttier et Emmanuel Guitter La foncton à deux ponts et à tros ponts des quadrangulatons et cartes Érc Fusy (CNRS/LIX) Travaux avec Jéréme Boutter et Emmanuel Gutter Sémnare Caln, LIPN, Ma 4 Maps Def. Planar map = connected graph

More information

Advanced Mechanical Elements

Advanced Mechanical Elements May 3, 08 Advanced Mechancal Elements (Lecture 7) Knematc analyss and moton control of underactuated mechansms wth elastc elements - Moton control of underactuated mechansms constraned by elastc elements

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION. Erdem Bala, Dept. of Electrical and Computer Engineering,

COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION. Erdem Bala, Dept. of Electrical and Computer Engineering, COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION Erdem Bala, Dept. of Electrcal and Computer Engneerng, Unversty of Delaware, 40 Evans Hall, Newar, DE, 976 A. Ens Cetn,

More information

Lecture Topics VMSC Prof. Dr.-Ing. habil. Hermann Lödding Prof. Dr.-Ing. Wolfgang Hintze. PD Dr.-Ing. habil.

Lecture Topics VMSC Prof. Dr.-Ing. habil. Hermann Lödding Prof. Dr.-Ing. Wolfgang Hintze. PD Dr.-Ing. habil. Lecture Topcs 1. Introducton 2. Sensor Gudes Robots / Machnes 3. Motvaton Model Calbraton 4. 3D Vdeo Metrc (Geometrcal Camera Model) 5. Grey Level Pcture Processng for Poston Measurement 6. Lght and Percepton

More information

Dynamic Programming. Lecture 13 (5/31/2017)

Dynamic Programming. Lecture 13 (5/31/2017) Dynamc Programmng Lecture 13 (5/31/2017) - A Forest Thnnng Example - Projected yeld (m3/ha) at age 20 as functon of acton taken at age 10 Age 10 Begnnng Volume Resdual Ten-year Volume volume thnned volume

More information

Numerical Integration of Geometric Flows for Space Curves

Numerical Integration of Geometric Flows for Space Curves Numercal Integraton of Geometrc Flows for Space Curves by Guolang Xu Report No. ICMSEC-13-01 January 2013 Research Report Insttute of Computatonal Mathematcs and Scentfc/Engneerng Computng Chnese Academy

More information

MEASUREMENT OF MOMENT OF INERTIA

MEASUREMENT OF MOMENT OF INERTIA 1. measurement MESUREMENT OF MOMENT OF INERTI The am of ths measurement s to determne the moment of nerta of the rotor of an electrc motor. 1. General relatons Rotatng moton and moment of nerta Let us

More information

On a direct solver for linear least squares problems

On a direct solver for linear least squares problems ISSN 2066-6594 Ann. Acad. Rom. Sc. Ser. Math. Appl. Vol. 8, No. 2/2016 On a drect solver for lnear least squares problems Constantn Popa Abstract The Null Space (NS) algorthm s a drect solver for lnear

More information

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force.

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force. Secton 1. Dynamcs (Newton s Laws of Moton) Two approaches: 1) Gven all the forces actng on a body, predct the subsequent (changes n) moton. 2) Gven the (changes n) moton of a body, nfer what forces act

More information

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules RESERVE HIS SPACE Electronc Quantum Monte Carlo Calculatons of Energes and Atomc Forces for Datomc and Polyatomc Molecules Myung Won Lee 1, Massmo Mella 2, and Andrew M. Rappe 1,* 1 he Maknen heoretcal

More information

Linear Classification, SVMs and Nearest Neighbors

Linear Classification, SVMs and Nearest Neighbors 1 CSE 473 Lecture 25 (Chapter 18) Lnear Classfcaton, SVMs and Nearest Neghbors CSE AI faculty + Chrs Bshop, Dan Klen, Stuart Russell, Andrew Moore Motvaton: Face Detecton How do we buld a classfer to dstngush

More information

Some modelling aspects for the Matlab implementation of MMA

Some modelling aspects for the Matlab implementation of MMA Some modellng aspects for the Matlab mplementaton of MMA Krster Svanberg krlle@math.kth.se Optmzaton and Systems Theory Department of Mathematcs KTH, SE 10044 Stockholm September 2004 1. Consdered optmzaton

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Instance-Based Learning (a.k.a. memory-based learning) Part I: Nearest Neighbor Classification

Instance-Based Learning (a.k.a. memory-based learning) Part I: Nearest Neighbor Classification Instance-Based earnng (a.k.a. memory-based learnng) Part I: Nearest Neghbor Classfcaton Note to other teachers and users of these sldes. Andrew would be delghted f you found ths source materal useful n

More information

ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING

ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING ESE 5 ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING Gven a geostatstcal regresson odel: k Y () s x () s () s x () s () s, s R wth () unknown () E[ ( s)], s R ()

More information

where λ = Z/f. where a 3 4 projection matrix represents a map from 3D to 2D. Part I: Single and Two View Geometry Internal camera parameters

where λ = Z/f. where a 3 4 projection matrix represents a map from 3D to 2D. Part I: Single and Two View Geometry Internal camera parameters Imagng Geometry Multple Vew Geometry Perspectve projecton Y Rchard Hartley and Andrew Zsserman X λ y = Y f Z O X p y Z X where λ = Z/f. mage plane CVPR June 1999 Ths can be wrtten as a lnear mappng between

More information

GEOMETRIC SELF-ASSEMBLY OF RIGID SHAPES: A SIMPLE VORONOI APPROACH

GEOMETRIC SELF-ASSEMBLY OF RIGID SHAPES: A SIMPLE VORONOI APPROACH GEOMETRIC SELF-ASSEMBLY OF RIGID SHAPES: A SIMPLE VORONOI APPROACH LISA J. LARSSON, RUSTUM CHOKSI, AND JEAN-CHRISTOPHE NAVE Abstract. Self-assembly of shapes from spheres to non-smooth and possbly non-convex

More information

Introduction to Simulation - Lecture 5. QR Factorization. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, and Karen Veroy

Introduction to Simulation - Lecture 5. QR Factorization. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, and Karen Veroy Introducton to Smulaton - Lecture 5 QR Factorzaton Jacob Whte hanks to Deepak Ramaswamy, Mchal Rewensk, and Karen Veroy Sngular Example LU Factorzaton Fals Strut Jont Load force he resultng nodal matrx

More information

Study Guide For Exam Two

Study Guide For Exam Two Study Gude For Exam Two Physcs 2210 Albretsen Updated: 08/02/2018 All Other Prevous Study Gudes Modules 01-06 Module 07 Work Work done by a constant force F over a dstance s : Work done by varyng force

More information

Spectral Clustering. Shannon Quinn

Spectral Clustering. Shannon Quinn Spectral Clusterng Shannon Qunn (wth thanks to Wllam Cohen of Carnege Mellon Unverst, and J. Leskovec, A. Raaraman, and J. Ullman of Stanford Unverst) Graph Parttonng Undrected graph B- parttonng task:

More information

PHYS 215C: Quantum Mechanics (Spring 2017) Problem Set 3 Solutions

PHYS 215C: Quantum Mechanics (Spring 2017) Problem Set 3 Solutions PHYS 5C: Quantum Mechancs Sprng 07 Problem Set 3 Solutons Prof. Matthew Fsher Solutons prepared by: Chatanya Murthy and James Sully June 4, 07 Please let me know f you encounter any typos n the solutons.

More information

18-660: Numerical Methods for Engineering Design and Optimization

18-660: Numerical Methods for Engineering Design and Optimization 8-66: Numercal Methods for Engneerng Desgn and Optmzaton n L Department of EE arnege Mellon Unversty Pttsburgh, PA 53 Slde Overve lassfcaton Support vector machne Regularzaton Slde lassfcaton Predct categorcal

More information

A Quantum Gauss-Bonnet Theorem

A Quantum Gauss-Bonnet Theorem A Quantum Gauss-Bonnet Theorem Tyler Fresen November 13, 2014 Curvature n the plane Let Γ be a smooth curve wth orentaton n R 2, parametrzed by arc length. The curvature k of Γ s ± Γ, where the sgn s postve

More information

Modeling of Dynamic Systems

Modeling of Dynamic Systems Modelng of Dynamc Systems Ref: Control System Engneerng Norman Nse : Chapters & 3 Chapter objectves : Revew the Laplace transform Learn how to fnd a mathematcal model, called a transfer functon Learn how

More information

UIC University of Illinois at Chicago

UIC University of Illinois at Chicago DSL Dynamc Smulaton Laboratory UIC Unversty o Illnos at Chcago FINITE ELEMENT/ MULTIBODY SYSTEM ALGORITHMS FOR RAILROAD VEHICLE SYSTEM DYNAMICS Ahmed A. Shabana Department o Mechancal and Industral Engneerng

More information

A property of the elementary symmetric functions

A property of the elementary symmetric functions Calcolo manuscrpt No. (wll be nserted by the edtor) A property of the elementary symmetrc functons A. Esnberg, G. Fedele Dp. Elettronca Informatca e Sstemstca, Unverstà degl Stud della Calabra, 87036,

More information

SIO 224. m(r) =(ρ(r),k s (r),µ(r))

SIO 224. m(r) =(ρ(r),k s (r),µ(r)) SIO 224 1. A bref look at resoluton analyss Here s some background for the Masters and Gubbns resoluton paper. Global Earth models are usually found teratvely by assumng a startng model and fndng small

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Lecture 3. Camera Models 2 & Camera Calibration. Professor Silvio Savarese Computational Vision and Geometry Lab. 13- Jan- 15.

Lecture 3. Camera Models 2 & Camera Calibration. Professor Silvio Savarese Computational Vision and Geometry Lab. 13- Jan- 15. Lecture Caera Models Caera Calbraton rofessor Slvo Savarese Coputatonal Vson and Geoetry Lab Slvo Savarese Lecture - - Jan- 5 Lecture Caera Models Caera Calbraton Recap of caera odels Caera calbraton proble

More information

Maximal Margin Classifier

Maximal Margin Classifier CS81B/Stat41B: Advanced Topcs n Learnng & Decson Makng Mamal Margn Classfer Lecturer: Mchael Jordan Scrbes: Jana van Greunen Corrected verson - /1/004 1 References/Recommended Readng 1.1 Webstes www.kernel-machnes.org

More information

The classical spin-rotation coupling

The classical spin-rotation coupling LOUAI H. ELZEIN 2018 All Rghts Reserved The classcal spn-rotaton couplng Loua Hassan Elzen Basher Khartoum, Sudan. Postal code:11123 louaelzen@gmal.com Abstract Ths paper s prepared to show that a rgd

More information

Note on EM-training of IBM-model 1

Note on EM-training of IBM-model 1 Note on EM-tranng of IBM-model INF58 Language Technologcal Applcatons, Fall The sldes on ths subject (nf58 6.pdf) ncludng the example seem nsuffcent to gve a good grasp of what s gong on. Hence here are

More information

Free vibration analysis of a hermetic capsule by pseudospectral method

Free vibration analysis of a hermetic capsule by pseudospectral method Journal of Mechancal Scence and echnology 6 (4) (0) 0~05 wwwsprngerlnkcom/content/78-494x DOI 0007/s06-0-06-y Free vbraton analyss of a hermetc capsule by pseudospectral method Jnhee ee * Department of

More information

Singular Value Decomposition: Theory and Applications

Singular Value Decomposition: Theory and Applications Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information