Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

Michigan State University - MTH - 1835

MTH1825sec.006Wed.Sept.8,2010Section1.5ApplicationsofLinearEquationsinOneVariable Translatingexpressions Example1)Ifnrepresentsanumber,writeanexpressionforeachofthefollowing: a. 3timesanumber b. thequotientofanumberand5 c. thesumofanumberand12 d. thedi

Michigan State University - MTH - 1835

MTH1825sec.006 Wed.Sept.8,2010Section1.5ApplicationsofLinearEquationsinOneVariable Translatingexpressions Example1)Ifnrepresentsanumber,writeanexpressionforeachofthefollowing: a. 3timesanumber b. thequotientofanumberand5 c. thesumofanumberand12 d. thed

Michigan State University - MTH - 1835

MTH1825sec.006Monday,Sept.27,2010Section2.4ApplicationsofLinearEquationsandModeling Example1:(Similarto#710insection2.4) Rachelisasalesrepresentativeandearnsabasesalaryof$2000permonthplusa12% commissiononhersalesforthemonth. a. Writealinearequationthat

Michigan State University - MTH - 1835

FS10MTH1825sec.006 Quiz4questions Greenversion 12 n 2 8 n 7 1. 2 y 2 + 6 y 56 2. 3n 3 x 192 nx 3. 64 x 3 125 y 3 4. 3 x 3 2 x 2 75 x + 50 5. 16 x 2 64 6. a. 16 x 2 + 64 b. Yellowversion 3c 2 12c 63 1. 7c 3 d 63cd 2. 12 w 2 11w 5 3. 7 x 3 3 x 2 28 x + 12 4

Michigan State University - MTH - 1835

UCLA - LIFE SCIEN - 4

!"#$%&'()* +#,%&'-#.(/&0&1,2!"#$%&'()$'*+,(-./(!"&.,+%*.+(.-(012'+239"+"(8/'+3-"/(%+(C'&2"/%'D%,@(EF@GD%,1/"(EF@G(4.#5/%,62(7(86"(0&9/':;<%$(4.=#'+%"3>(?+&@( )"/=%33%.+(/"A1%/"B(2.(/"#/.B1&"(./(B%3#$'586"(D(#$'3=%B('+B(&.+H1,'*.+D%,@(EF@EI('D%,1/"

Cornell - CSE - 527

function [sFinal,thresh] = canny(img, mLow, mHigh, sig) % Canny edge detector % % % function [sFinal, thresh] =canny(img, mLow, mHigh, sig) % Applies the canny edge detection algo to the given image % img : the given image (matrix) in color or B/W % mLow

Cornell - CSE - 527

Image FeaturesEdgesFormal Design of an Optimal Edge Detector Edge detection involves 3 steps: Noise smoothing Edge enhancement Edge localization J. Canny formalized these steps to design an optimal edge detector2/11/2004Octavia I. Camps21Canny E

Cornell - CS - 101

Cornell - CSE - 527

CSE527 Introduction to Computer Vision Dimitris Samaras Tue thu 5:20-6:40 CS 2120Computer Vision: A Modern Approach, Forsyth and Ponce, Prentice Hall 2002. (Optional) Introductory Techniques for 3D Computer Vision, Trucco and Verri, Prentice Hall 1998.

Cornell - CSE - 527

2/3/10Image Formation Light Reflectance Image Capture Camera Lens Sensor Projection models Camera System ParametersCameras - a brief history From the latin Camera Obscura - Dark Chamber The cameras of the 16th century were literally dark rooms with a

Cornell - CSE - 527

2/3/10Images as functionsf(x,y) y xWhat is a digital image? Digital images: Sample the 2D space on a regular grid Quantize each sample For samples being apart: f[i ,j] = Quantizecfw_ f(i , j ) Image: matrix of integer values62 10 10 2 0 255 166 79

Cornell - CSE - 527

A biref anlaogy wtih txet Waht mttares is waht hpapens on wrod baounrdies Mocpera iwht htsi sesm1Primal Sketch Early vision: invariants, moments, pattern recognition David Marr, late70s Inspiration from biological vision Image representation in ter

Cornell - CSE - 527

Lecture LayoutPerceptual and Sensory Augmented ComputingImage features Edges Junctions & Corners Blobs RidgesImage descriptors SIFT featuresGoals for a low-level image representation CompactReduce the number of processed image locations 000000011111

Cornell - CSE - 527

Feature Extraction Features: local meaningful detectable Points Edges Step edges Line edges Contours Closed contours are boundaries Regions Applying a filter at some point can be seen as taking a dot Insight product between the filters look like th

Cornell - CSE - 527

Feature Extraction Features: local meaningful detectable Points Edges Step edges Line edges Contours Deformable models Closed contours are boundaries RegionsPrevious Lecture: Gabor filters Gabor filterbanks: consider combinations of Spatially local

Cornell - CSE - 527

OverviewPage 1 of 1Next: Imaging Geometry Up: No Title Previous: No TitleOverviewThe main points covered in this lecture are: A perspective (central) projection camera is represented by a matrix.The most general perspective transformation transformat

Cornell - CSE - 527

function dG = dgauss(sig) x = floor(-3*sig):ceil(3*sig); G = exp(-0.5*x.^2/sig^2); G = G/sum(G); dG = -x.*G/sig^2;

Cornell - CSE - 527

+=Image Quilting for Texture Synthesis & TransferAlexei Efros (UC Berkeley) Bill Freeman (MERL)The Goal of Texture Synthesisinput imageSYNTHESISTrue (infinite) texturegenerated imageGiven a finite sample of some texture, the goal is to synthesize

Cornell - CSE - 527

Home work 2 : CSE 527 : Introduction to VisionANS (1) (A) The BRDF is constant for a Lambertian surface. The BRDF is defined as the ratio of radiance in the outgoing direction to the incident radiance BRDF = Radiance in Outgoing direction/ Incident Irrad

Cornell - CSE - 527

Cornell - CSE - 527

CHAPTER1An Introduction to ProbabilityAs the previous chapters have illustrated, it is often quite easy to come up with physical models that determine the eects that result from various causes we know how image intensity is determined, for example. The

Cornell - CSE - 527

function [ z ] = procrustesAnalysis( x ) %PROCRUSTESANALYSIS aligns a set of 2D points % [z] = procrustesAnalysis(x) % % Input: % x: M-by-2-by-N array where M is the number of points in a shape, N is % the number of data % Output: % z: M-by-2-by-N array w

Cornell - CSE - 527

PROJECTPROPOSALFORCSE527AspartofthefinalprojectforourcourseIntroductiontoComputerVision,Iwouldliketo developanAndroidbasedapplicationthatenablestheusertorunvariousVision algorithms. Onasimplenote,itallowsauserwithanAndroidphonetoexecutevariousvision algo

Cornell - CSE - 527

PROJECT PROPOSAL FOR CSE 527As part of the final project for our course Introduction to Computer Vision, I would be developing an Android based application. This application allows an Android user to run various Vision algorithms that we implemented duri

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer ArchitectureLec 1-3 - IntroductionLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s061/25,27 + 2/1/2010CSE502-S10, Lec 01-3 -

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 3-5 Performance + Instruction Pipelining ReviewLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s062/1,3,8/2010

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 6-7 Memory Hierarchy ReviewLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s06Review from last lecture Quanti

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 8-10 Instruction Level ParallelismLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s06Outline ILP Instruction

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 10+11 More Instruction Level Parallelism Via SpeculationLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s06Rev

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 12-13 Threading & Simultaneous MultithreadingLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s063/15-17/10CSE

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 13-15 Vector ComputersLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from Krste Asanovic of MIT and David Patterson of UCB, UC-Berkeley cs252-s06Outl

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer ArchitectureLec 15 MidTerm ReviewLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s0611/3/2009CSE502-F09, Lec 15 - MTRevu1Re

SUNY Stony Brook - CSE - 502

CSE502 Lecture 15 - Tue 3Nov09Review: MidTerm Thu 5Nov09 - Outline of Major TopicsComputing system: performance, speedup, performance/cost Origins and benefits of scalar instruction pipelines and caches Pipeline Hazards Structural need more HW to wait l

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 16-18 Symmetric MultiProcessingLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s06Outline MP Motivation SISD

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 19 Directory-Based Shared-Memory Multiprocessors & MP SynchronizationLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley

SUNY Stony Brook - CSE - 502

CSE502 Lecture 20 Mon 19Apr2010Review: MidTerm Mon 19Apr10 - Outline of Major TopicsCovers Text chapters 1-3, start of 4 plus appendices A,B,C,F. Read all pages at least once before the open-book, closed-friends midterm exam in class on Wednesday, 4/21.

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 21-22 Advanced Memory Hierarchy and Application TuningLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s06Outli

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 22-23 Disk StorageLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s06Case for Storage Shift in focus from com

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer Architecture Lec 22 Disk StorageLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s06Outline Magnetic Disks RAID Advanced Depend

Cornell - CS - 101

CS545Contents IVFrequency Domain Representations Laplace Transform Most important Laplace Transforms Transfer functions Block-Diagram Algebra ExamplesMatlab/Simulink Introduction How to get started The most relevant blocks and settings of Simulink S

Cornell - CS - 101

CS545-Contents XLagrange's Method of Deriving Equations of Motion for Rigid Body Systems Lagrange's Equation Generalized Coordinates Potential Energy Kinetic Energy Properties of the Dynamics EquationsReading Assignment for Next ClassSee http:/www-cl

Cornell - CS - 101

CS545-Contents XINewton-Euler Method of Deriving Equations of Motion Newton's Equation Euler's Equation The Newton-Euler Recursion Automatic Generation of Equations of MotionReading Assignment for Next ClassSee http:/www-clmc.usc.edu/~cs545Newton's

Cornell - CS - 101

CS545-Contents XIINonlinear ControlJoint space controlDecoupled controlPID control in joint space Compute torque control Inverse dynamics controlCentralized control Operational space controlReading Assignment for Next ClassSee http:/www-clmc.usc.

Cornell - CS - 101

CS545-Contents XIIITrajectory Planning Control Policies Desired Trajectories Optimization Methods Dynamical SystemsReading Assignment for Next ClassSee http:/www-clmc.usc.edu/~cs545Learning Policies is the Goal of Learning ControlPolicy:u ( t ) =

Cornell - CS - 101

CS545-Contents XIVInteraction Control Compliance Impedance Force control Hybrid control Impedance control Sensors and Actuators Reading Assignment for Next ClassSee http:/www-clmc.usc.edu/~cs545ExampleExampleProblems of Interaction Control Equa

Cornell - CSE - 527

Distinctive Image Features from Scale-Invariant KeypointsDavid G. Lowe Computer Science Department University of British Columbia Vancouver, B.C., Canada lowe@cs.ubc.ca January 5, 2004AbstractThis paper presents a method for extracting distinctive inva

Cornell - CSE - 527

IEEE COMPUTER GRAPHICS AND APPLICATIONS (c) 1996 IEEE Vol. 16, No. 2: MARCH 1996, pp. 22-30Video Mosaics for Virtual EnvironmentsRichard Szeliski, Microsoft Corporation By panning a camera over a scene and automatically compositing the video frames, thi

SUNY Stony Brook - CSE - 502

CSE 502 Graduate Computer ArchitectureLec 1-3 - IntroductionLarry WittieComputer Science, StonyBrook University http:/www.cs.sunysb.edu/~cse502 and ~lwSlides adapted from David Patterson, UC-Berkeley cs252-s061/25,27 + 2/1/2010CSE502-S10, Lec 01-3 -

Cornell - CS - 101

CS545Contents IIIBasic Linear Control Theory The plant The plant model Continuous vs. discrete systems The control policy Desired Trajectories Open Loop Control Feedback Control PID Control Negative Feedback Control Linear Systems BlockdiagramsReading

Cornell - CS - 101

CS545Contents VIControl Theory II Linear Stability Analysis Linearization of Nonlinear Systems Discretization See http:/www-clmc.usc.edu/~cs545Reading Assignment for Next ClassStability AnalysisGiven the control system x = f (x,u ) or x = Ax +BuHo

Cornell - CS - 101