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School: Penn State
Course: Intermed Progrmg
Hash Table How can we retrieve a value (by key) from an associative container in O(1) time? Yes we can, with the help of a Hash Table! Hash table typically implements an internal array for storing data and provides a function for index (i.e. hash code) ca
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 17: Mosaicing and Stabilization Robert Collins CSE486, Penn State Recall: Planar Projection Internal params Perspective projection u Pixel coords v y Homography x Rotation + Translation Point on plane Robert Colli
School: Penn State
Course: Computer Security
Chapter 12 An introduction to database programming Murachs ASP.NET 4/VB, C12 2011, Mike Murach & Associates, Inc. Slide 1 Objectives Knowledge Explain how a table in a relational database is organized. Explain how the tables in a relational database ar
School: Penn State
Course: Computer Security
Chapter 4 How to test and debug an ASP.NET application Murachs ASP.NET 4/VB, C4 2011, Mike Murach & Associates, Inc. Slide 1 Objectives Applied Test a file-system web site with the ASP.NET development server or IIS. Create and test a local IIS web site
School: Penn State
Course: Computer Security
Chapter 7 How to use the validation controls Murachs ASP.NET 4/VB, C7 2011, Mike Murach & Associates, Inc. Slide 1 Objectives Applied Given the data validation requirements for a web form, use any of the validation controls presented in this chapter to
School: Penn State
Course: Computer Security
Chapter 11 Hash-Based Indexes Overview What are Hash-Based Indexes good for? DBMSs also provide the hashing mechanism to accelerate equality search. Not suitable for range queries. Two Dynamic Hashing Techniques Extensible hashing Linear hashing 2 Intro
School: Penn State
Course: Intermed Progrmg
Hash Table How can we retrieve a value (by key) from an associative container in O(1) time? Yes we can, with the help of a Hash Table! Hash table typically implements an internal array for storing data and provides a function for index (i.e. hash code) ca
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Motivation: Points on Planar Surface Robert Collins CSE486, Penn State Lecture 16: y Planar Homographies x Robert Collins CSE486, Penn State Camera Coords World Coords U V W Robert Collins CSE486, Penn State Review : Forw
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Summary: Transformations Euclidean Lecture 14 Parameter Estimation similarity Readings T&V Sec 5.1 - 5.3 affine projective Robert Collins CSE486, Penn State Parameter Estimation We will t
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486 Robert Collins CSE486 Laplacian of Gaussian (LoG) Filter - useful for finding edges - also useful for finding blobs! Lecture 11: LoG and DoG Filters Robert Collins CSE486 approximation using Difference of Gaussian (DoG) Recall: First
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State RECALL: Parameter Estimation: Lecture 15 Robust Estimation : RANSAC Let's say we have found point matches between two images, and we think they are related by some parametric transformati
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Imaging Geometry W Object of Interest in World Coordinate System (U,V,W) Lecture 12: Camera Projection Robert Collins CSE486, Penn State Y Z is optic axis Image plane located f units ou
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 14 Parameter Estimation Readings T&V Sec 5.1 - 5.3 Robert Collins CSE486, Penn State Summary: Transformations Euclidean similarity affine projective Robert Collins CSE486, Penn State Parameter Estimation We will t
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 16: Planar Homographies Robert Collins CSE486, Penn State Motivation: Points on Planar Surface y x Robert Collins CSE486, Penn State Review : Forward Projection World Coords Camera Coords Film Coords U V W X Y Z x
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 15 Robust Estimation : RANSAC Robert Collins CSE486, Penn State RECALL: Parameter Estimation: Lets say we have found point matches between two images, and we think they are related by some parametric transformatio
School: Penn State
Course: FUND COMP ARCH
CSE 530 Final Project Presentation q <Survey on reducing power consumption for non volatile memories> <Tianqi Xu and Xiaobo Yu> CSE530 Final Project Presentation.1 <Tianqi Xu and Xiaobo Yu> Fall 2013 PSU Introduction q STT-RAM (spin-transfer torque RAM) q
School: Penn State
Course: FUND COMP ARCH
CSE 530 Final Project Presentation q <New Opportunities and Challenges for Non-volatile Memory Technologies> <Tianqi Xu and Xiaobo Yu> CSE530 Final Project Presentation.1 <YOURNAMES> Fall 2013 PSU The problem/opportunity (what is it, broadly?) q Provide g
School: Penn State
Course: Multicasting
Homework 1. CSE 598C Vision-based Tracking, Fall 2012. Due Wed Fri 7, 2012 in Angel. This homework will actually be pretty easy, I think, if you followed the example on Gaussian Point Observations that we went over in class. You can view that example as o
School: Penn State
Course: Topics In Computer Vision
CSE586 Assignment 3, due March 17 Thurs 1) Prove that the set of all 2x2 matrices of the form [cos(theta) -sin (theta); sin(theta) cos(theta)] form a group under matrix multiplication. 2) Prove that the set of all complex numbers of the form e^cfw_i theta
School: Penn State
Course: Topics In Computer Vision
CSE586/EE554 Computer Vision II EM incremental assignments Spring 2011 1. Given a d-dimensional mean vector v and dxd covariance matrix C (symmetric, pos def), generate N random sample points distributed according to a Gaussian with mean v and covariance
School: Penn State
Course: Topics In Computer Vision
Homework 1 (due Monday Jan 24 by end of the day) 1. Consider the x=1 y=0: 10 y=1: 10 y=2: 0 (unnormalized) 2D bivariate distribution f(x,y) x=2 x=3 x=4 10 10 10 20 20 0 10 0 0 For each of the following, give your answer to 1) a) What is the marginal distr
School: Penn State
Course: VLSI
HOMEWORK 4 CMPEN 411 Due: 2/12/2013 11:30pm Learning Objective Use the VLSI CAD tools to design and implement an 8-bit Program Counter (PC) circuit in bit-slice style and analyze it. Instruction Implement the program counter circuit shown below in schemat
School: Penn State
Course: Intermed Progrmg
Hash Table How can we retrieve a value (by key) from an associative container in O(1) time? Yes we can, with the help of a Hash Table! Hash table typically implements an internal array for storing data and provides a function for index (i.e. hash code) ca
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 17: Mosaicing and Stabilization Robert Collins CSE486, Penn State Recall: Planar Projection Internal params Perspective projection u Pixel coords v y Homography x Rotation + Translation Point on plane Robert Colli
School: Penn State
Course: Computer Security
Chapter 12 An introduction to database programming Murachs ASP.NET 4/VB, C12 2011, Mike Murach & Associates, Inc. Slide 1 Objectives Knowledge Explain how a table in a relational database is organized. Explain how the tables in a relational database ar
School: Penn State
Course: Computer Security
Chapter 4 How to test and debug an ASP.NET application Murachs ASP.NET 4/VB, C4 2011, Mike Murach & Associates, Inc. Slide 1 Objectives Applied Test a file-system web site with the ASP.NET development server or IIS. Create and test a local IIS web site
School: Penn State
Course: Computer Security
Chapter 7 How to use the validation controls Murachs ASP.NET 4/VB, C7 2011, Mike Murach & Associates, Inc. Slide 1 Objectives Applied Given the data validation requirements for a web form, use any of the validation controls presented in this chapter to
School: Penn State
Course: Computer Security
Chapter 11 Hash-Based Indexes Overview What are Hash-Based Indexes good for? DBMSs also provide the hashing mechanism to accelerate equality search. Not suitable for range queries. Two Dynamic Hashing Techniques Extensible hashing Linear hashing 2 Intro
School: Penn State
Course: Computer Security
Chapter 17 Concurrency Control Overview Lock Based Protocols Time-Stamp Based Protocols 2 Conflict Serializability (Review) Two schedules are conflict equivalent if: Involve the same actions of the same transactions Every pair of conflicting actions is
School: Penn State
Course: Computer Security
Chapter 13 External Sorting Why Sorting? A classic problem in computer science! Data requested in sorted order e.g., find students in increasing gpa order Sorting is first step in bulk loading B+ tree index. Sorting useful for eliminating duplicate copies
School: Penn State
Course: Computer Security
BackgroundReviewofHardwareand Software 1 Desktop/serverhardware CPU Memoryhierarchy:L1/L2Caches(SRAM),Main memory(DRAM). Interconnects(Buses) I/OPeripherals:Disk,Networkinterface,keyboard, terminal, 2 Hardware iL1 CPU L2 MemoryBus (e.g.DDRx) Main Memo
School: Penn State
Course: Computer Security
Chapter 12 Overview of Query Evaluation Architecture of a DBMS SQL Commands Plan Executor Optr Evaluator Transaction Manager Parser Optimizer Files & Access Methods Buffer Manager Lock Manager Query Evaluation Engine Recovery Manager Disk Space Manager DB
School: Penn State
Course: Computer Security
CSE 541 Database System I What is CSE541? Graduate-level course introducing database management systems, emphasize on: Fundamental concepts in database theory Internal system/implementation issues of database management systems. Database research experie
School: Penn State
Course: Computer Security
Using Encryption for Authentication in Large Networks of Computers Roger M. Needham Michael D. Schroeder Purpose Present protocols for decentralized authentication Authenticated interactive communication Authenticated one-way communication Signed comm
School: Penn State
Course: Computer Security
Using Encryption for Authentication in Large Networks of Computers Roger M. Needham Michael D. Schroeder Purpose Present protocols for decentralized authentication Authenticated interactive communication Authenticated one-way communication Signed comm
School: Penn State
Course: Computer Security
HYDRA Project HYDRA Use Of Semantic Technologies for Network Embedded System Middleware WIKT 28-29 November 2006 Contents 1. 2. 3. 4. HYDRA Project Project Vision Project Objectives Project Challenges 14 Nov 2005 HYDRA project Networked Embedded System Mi
School: Penn State
Course: Computer Security
Worm enabling exploits Cyber Security Lab Spring 12 Background reading Worm Anatomy and Model http:/portal.acm.org/citation.cfm?id=948196 Smashing the Stack for Fun and Profit http:/www.phrack.com/issues.html?issue=49&id=1 The Shellcoders Handbook At
School: Penn State
Course: Computer Security
Chapter 18 How to secure a web site Murachs ASP.NET 4/VB, C18 2011, Mike Murach & Associates, Inc. Slide 1 Objectives Applied If youre using IIS 5.1 or 6.0, request and install a digital secure certificate or a trial certificate. If youre using IIS 7,
School: Penn State
Course: Computer Security
SpatialDataManagement Chapter28 DatabasemanagementSystems,3ed,R.RamakrishnanandJ.Gehrke 1 TypesofSpatialData PointData Pointsinamultidimensionalspace E.g.,Rasterdatasuchassatelliteimagery,whereeach pixelstoresameasuredvalue E.g.,Featurevectorsextracted
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 5: Gradients and Edge Detection Reading: T&V Section 4.1 and 4.2 Robert Collins CSE486, Penn State What Are Edges? Simple answer: discontinuities in intensity. Robert Collins CSE486, Penn State Boundaries of objec
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486 Lecture 10: Pyramids and Scale Space Robert Collins CSE486 Recall Repeated convolution by a smaller Gaussian to simulate effects of a larger one. Cascaded Gaussians G*(G*f) = (G*G)*f [associativity] Robert Collins CSE486 Example: C
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 08: Introduction to Stereo Reading: T&V Section 7.1 Robert Collins CSE486, Penn State Stereo Vision Inferring depth from images taken at the same time by two or more cameras. Robert Collins CSE486, Penn State Basi
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 06: Harris Corner Detector Reading: T&V Section 4.3 Robert Collins CSE486, Penn State Motivation: Matchng Problem Vision tasks such as stereo and motion estimation require finding corresponding features across two
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 09: Stereo Algorithms Robert Collins CSE486, Penn State Recall: Simple Stereo System Z Y left y camera located at (0,0,0) z ( , x Image coords of point (X,Y,Z) Left Camera: Camps, PSU z ) Tx Right Camera: (X,Y,Z)
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 7: Correspondence Matching Reading: T&V Section 7.2 Robert Collins CSE486, Penn State Recall: Derivative of Gaussian Filter Ix=dI(x,y)/dx Gx I(x,y) convolve Gy Iy=dI(x,y)/dy convolve Robert Collins CSE486, Penn St
School: Penn State
Course: Computer Security
Congestion Control and Resource Allocation Congestion Packets contend for resources in a network If resources are exhausted, congestion occurs Resource allocation If resources are reserved in advance (admission control), congestion can be avoided Diff
School: Penn State
Course: FUND COMP ARCH
CSE 530 Final Project Presentation q <New Opportunities and Challenges for Non-volatile Memory Technologies> <Tianqi Xu and Xiaobo Yu> CSE530 Final Project Presentation.1 <YOURNAMES> Fall 2013 PSU Introduction q STT-RAM (spin-transfer torque RAM) q q STT-
School: Penn State
Course: FUND COMP ARCH
Computer Science and Engineering 530 Fundamentals of Computer Architecture Fall 2013 Tuesdays, Thursdays, 9:45-11:00am, 124 Walker (Preliminary) Course Outline (v1.0) L# Date Topic Reading 1 2 Aug 27 Aug 29 Introduction and class overview Technology trend
School: Penn State
Course: Computer Security
Chapter1 Introduction A note on the use of these ppt slides: Were making these slides freely available to all (faculty, students, readers). Theyre in PowerPoint form so you can add, modify, and delete slides (including this one) and slide content to suit
School: Penn State
Course: Computer Security
Chapter1 Introduction A note on the use of these ppt slides: Were making these slides freely available to all (faculty, students, readers). Theyre in PowerPoint form so you can add, modify, and delete slides (including this one) and slide content to suit
School: Penn State
Course: Computer Security
Chapter11:Modelsof Computation Invitation to Computer Science, Java Version, Third Edition Objectives In this chapter, you will learn about What is a model? A model of a computing agent A model of an algorithm Invitation to Computer Science, Java Version,
School: Penn State
Course: Intermed Progrmg
Hash Table How can we retrieve a value (by key) from an associative container in O(1) time? Yes we can, with the help of a Hash Table! Hash table typically implements an internal array for storing data and provides a function for index (i.e. hash code) ca
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Motivation: Points on Planar Surface Robert Collins CSE486, Penn State Lecture 16: y Planar Homographies x Robert Collins CSE486, Penn State Camera Coords World Coords U V W Robert Collins CSE486, Penn State Review : Forw
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Summary: Transformations Euclidean Lecture 14 Parameter Estimation similarity Readings T&V Sec 5.1 - 5.3 affine projective Robert Collins CSE486, Penn State Parameter Estimation We will t
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486 Robert Collins CSE486 Laplacian of Gaussian (LoG) Filter - useful for finding edges - also useful for finding blobs! Lecture 11: LoG and DoG Filters Robert Collins CSE486 approximation using Difference of Gaussian (DoG) Recall: First
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State RECALL: Parameter Estimation: Lecture 15 Robust Estimation : RANSAC Let's say we have found point matches between two images, and we think they are related by some parametric transformati
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Imaging Geometry W Object of Interest in World Coordinate System (U,V,W) Lecture 12: Camera Projection Robert Collins CSE486, Penn State Y Z is optic axis Image plane located f units ou
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Recall: Imaging Geometry W Object of Interest in World Coordinate System (U,V,W) Lecture 13: Camera Projection II Robert Collins CSE486, Penn State Y Z is optic axis Image plane located
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Recall: Simple Stereo System Z Y left y camera located at (0,0,0) Lecture 09: Stereo Algorithms (X,Y,Z) z ( , z ) y x Tx ( , ) right camera located at (Tx,0,0) x Image coords of point (X,
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Recall: Derivative of Gaussian Filter Ix=dI(x,y)/dx Gx I(x,y) Lecture 7: Correspondence Matching convolve Gy Reading: T&V Section 7.2 Iy=dI(x,y)/dy convolve Robert Collins CSE486, Penn St
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486 Robert Collins CSE486 Recall Cascaded Gaussians Lecture 10: Pyramids and Scale Space Robert Collins CSE486 Example: Cascaded Convolutions Robert Collins CSE486 G*(G*f) = (G*G)*f [associativity] Robert Collins CSE486 Aside: Binomial
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Visualizing Images Recall two ways of visualizing an image Intensity pattern 2d array of numbers Lecture 2: Intensity Surfaces and Gradients We see it at this level Robert Collins CSE486,
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Motivation: Matchng Problem Vision tasks such as stereo and motion estimation require finding corresponding features across two or more views. Lecture 06: Harris Corner Detector Reading:
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Stereo Vision Inferring depth from images taken at the same time by two or more cameras. Lecture 08: Introduction to Stereo Reading: T&V Section 7.1 Scene Point y Image Point p = (x,y,f)
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State Summary about Convolution Computing a linear operator in neighborhoods centered at each pixel. Can be thought of as sliding a kernel of fixed coefficients over the image, and doing a weig
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State I have put some Matlab image tutorials on Angel. Please take a look if you are unfamiliar with Matlab or the image toolbox. Lecture 3: Linear Operators Robert Collins CSE486, Penn State A
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Robert Collins CSE486, Penn State CSE/EE 486: Computer Vision I Fall 2007 Course Overview Textbook required Introductory Techniques for 3-D Computer Vision by E. Trucco and A. Verri, Prentice Hall, 1998. Robert Collins CS
School: Penn State
Binary Space Partition Trees Anthony Steed, Yiorgos Chrysanthou 1999-2003 1 Overview Previous list priority algorithms fail in a number of cases non of them is completely general BSP tree is a general solution, but with its own problems Tree size Tree
School: Penn State
Voronoi Diagram Subhas C. Nandy Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata 700108 Viewpoint 1: Locate the nearest dentist. Viewpoint 2: Find the service area of potential customers for each dentist. Voronoi Diagram F
School: Penn State
On the Agenda The Art Gallery Problem Polygon Triangulation Computational Geometry Chapter 3 Polygons and Triangulation 13. 23. Art Gallery Problem Given a simple polygon P, say that two points p and q can see each other if the open segment pq lies entire
School: Penn State
Introduction Smallest enclosing circle algorithm Randomized incremental construction Smallest enclosing circles and more Computational Geometry Lecture 6: Smallest enclosing circles and more Computational Geometry Lecture 6: Smallest enclosing circles and
School: Penn State
Voronoi Diagrams A city builds a set of post ofces, and now needs to determine which houses will be served by which ofce. It would be wasteful for a postman to go out of their way to make a delivery if another post ofce is closer. What is the right way to
School: Penn State
The painters algorithm It was mentioned earlier that sorting algorithms were useful in computer graphics. If we have a number of polygons to display we must make sure that those nearest to the viewpoint obscure those which are further away. Unit 6: BSP Tr
School: Penn State
Course: Topics In Computer Vision
EM Motivation want to do MLE of mixture of Gaussian parameters But this is hard, because of the summation in the mixture of Gaussian equation (cant take the log of a sum). If we knew which point contribute to which Gaussian component, the problem would
School: Penn State
Course: Multicasting
Robert Collins CSE598C, PSU Mean-shift, continued R.Collins, CSE, PSU CSE598C Fall 2012 Robert Collins CSE598C, PSU Background: Kernel Density Estimation Given a set of data samples xi; i=1.n Convolve with a kernel function H to generate a smooth function
School: Penn State
Course: Multicasting
Robert Collins CSE598C, PSU Introduction to Mean-Shift Tracking Robert Collins CSE598C, PSU Appearance-Based Tracking current frame + previous location likelihood over object location appearance model (e.g. image template, or Mode-Seeking (e.g. mean-shift
School: Penn State
Course: Multicasting
Robert Collins CSE598C Sampling Methods, Particle Filtering, and Markov-Chain Monte Carlo CSE598C Vision-Based Tracking Fall 2012, CSE Dept, Penn State Univ Robert Collins CSE598C References Robert Collins CSE598C Recall: Bayesian Filtering Rigorous gener
School: Penn State
Course: Multicasting
Robert Collins CSE598C Back to Lucas-Kanade Robert Collins CSE598C Step-by-Step Derivation The key to the derivation is Taylor series approximation: W [ I (W ([ x, y ]; P P) ~ [ I (W ([ x, y ]; P) I P ~ P We will derive this step-by-step. First, we need t
School: Penn State
Course: Multicasting
Robert Collins CSE598C Intro to Template Matching and the Lucas-Kanade Method Robert Collins CSE598C Appearance-Based Tracking current frame + previous location likelihood over object location appearance model (e.g. image template, or Mode-Seeking (e.g. m
School: Penn State
Course: Multicasting
Robert Collins CSE598C Particle Filter Failures References King and Forsyth, How Does CONDENSATION Behave with a Finite Number of Samples? ECCV 2000, 695-709. Karlin and Taylor, A First Course in Stochastic Processes, 2nd edition, Academic Press, 1975. Ro
School: Penn State
Course: Multicasting
6.559479; CO\M6 7 gélT-lolzavg~ (v MK ML 00 (5ZWLM Mara/var. F-v'lk rm: 672$ X; n. X4 ML okMa-"nan? Y1 o. 7v. AL A (Sana-7 fray 774.43?" km A7 90mm, vw Ya): PCVDPCYIIM)Plek>PCyzln>PClx,> u , 6" Mv _ onml .1 Cur/war 671%. M o/tb on (aha) 47th [My cu rrm'l'
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 14 Parameter Estimation Readings T&V Sec 5.1 - 5.3 Robert Collins CSE486, Penn State Summary: Transformations Euclidean similarity affine projective Robert Collins CSE486, Penn State Parameter Estimation We will t
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 16: Planar Homographies Robert Collins CSE486, Penn State Motivation: Points on Planar Surface y x Robert Collins CSE486, Penn State Review : Forward Projection World Coords Camera Coords Film Coords U V W X Y Z x
School: Penn State
Course: Computer VIsion I
Robert Collins CSE486, Penn State Lecture 15 Robust Estimation : RANSAC Robert Collins CSE486, Penn State RECALL: Parameter Estimation: Lets say we have found point matches between two images, and we think they are related by some parametric transformatio
School: Penn State
Course: FUND COMP ARCH
CSE 530 Final Project Presentation q <Survey on reducing power consumption for non volatile memories> <Tianqi Xu and Xiaobo Yu> CSE530 Final Project Presentation.1 <Tianqi Xu and Xiaobo Yu> Fall 2013 PSU Introduction q STT-RAM (spin-transfer torque RAM) q
School: Penn State
Course: FUND COMP ARCH
CSE 530 Final Project Presentation q <New Opportunities and Challenges for Non-volatile Memory Technologies> <Tianqi Xu and Xiaobo Yu> CSE530 Final Project Presentation.1 <YOURNAMES> Fall 2013 PSU The problem/opportunity (what is it, broadly?) q Provide g
School: Penn State
Course: Multicasting
Homework 1. CSE 598C Vision-based Tracking, Fall 2012. Due Wed Fri 7, 2012 in Angel. This homework will actually be pretty easy, I think, if you followed the example on Gaussian Point Observations that we went over in class. You can view that example as o
School: Penn State
Course: Topics In Computer Vision
CSE586 Assignment 3, due March 17 Thurs 1) Prove that the set of all 2x2 matrices of the form [cos(theta) -sin (theta); sin(theta) cos(theta)] form a group under matrix multiplication. 2) Prove that the set of all complex numbers of the form e^cfw_i theta
School: Penn State
Course: Topics In Computer Vision
CSE586/EE554 Computer Vision II EM incremental assignments Spring 2011 1. Given a d-dimensional mean vector v and dxd covariance matrix C (symmetric, pos def), generate N random sample points distributed according to a Gaussian with mean v and covariance
School: Penn State
Course: Topics In Computer Vision
Homework 1 (due Monday Jan 24 by end of the day) 1. Consider the x=1 y=0: 10 y=1: 10 y=2: 0 (unnormalized) 2D bivariate distribution f(x,y) x=2 x=3 x=4 10 10 10 20 20 0 10 0 0 For each of the following, give your answer to 1) a) What is the marginal distr
School: Penn State
Course: VLSI
HOMEWORK 4 CMPEN 411 Due: 2/12/2013 11:30pm Learning Objective Use the VLSI CAD tools to design and implement an 8-bit Program Counter (PC) circuit in bit-slice style and analyze it. Instruction Implement the program counter circuit shown below in schemat