Wavelets Fast Multiresolution Image Querying Jacobs et.al. SIGGRAPH95 Outline Overview / Background Wavelets 2D Image matching L1, L2 metrics Wavelet metric Evaluation Use in 3D Image matching 2D analogue of 3D shape matching Raster instead of XYZ What are we trying to match?

BySpectral bases for 3D shapes representation Spectral bases – reminder Last week we spoke of Fourier analysis 1D sine/cosine bases for signals: Spectral bases – reminder Last week we spoke of Fourier analysis 2D bases for 2D signals (images) How about 3D shapes?

ByDimension 2000’s Elementary Math Workshop Cissi Kale Workshop Objectives Discuss GPS changes for grades 3-5 Learn about the Five Stages of Teaching and Learning Math Define and model ‘high yield’ instructional engagement strategies that address key math concepts associated with GPS standards

ByThe Decision Process in Operations. Clearly define the problems and the factors that influence it Develop specific and measurable objectives Develop a model Evaluate each alternative solution Select the best alternative Implement the decision and set a timetable for completion.

ByLecture 5: Project Planning 2. Outline. Time/Cost Tradeoffs Linear and non-linear Adding Workforce Constraints Slides borrowed from Twente & Iowa See Pinedo CD. Time/Cost Trade-Offs. What if you could spend money to reduce the job duration More money shorter processing time

ByFeature-based Surface Decomposition for Correspondence and Morphing between Polyhedra Arthur D Gregory Andrei State, Ming C Lin, Dinesh Manocha, Mark A Livingston University of North Carolina at Chapel Hill http://www.cs.unc.edu/~geom/3Dmorphing {gregory,andrei,lin,dm,livingst}@cs.unc.edu

ByUniversity of Washington Computer Programming I Lecture 6: Conditionals © 2000 UW CSE Overview Concepts this lecture Conditional execution if statement Conditional expressions Relational and logical operators {Compound statements} Related Reading Read Sections 4.1-4.5, 4.7-4.9

ByUniversity of Washington Computer Programming I Lecture 6: Conditionals © 2000 UW CSE Overview Concepts this lecture Conditional execution if statement Conditional expressions Relational and logical operators {Compound statements} Related Reading Read Sections 4.1-4.5, 4.7-4.9

ByRegister Allocation 5 Outline What is register allocation Webs Interference Graphs Graph coloring Spilling Splitting More optimizations Storing values between def and use Program computes with values value definitions (where computed) value uses (where read to compute new values)

ByMATRICES A rectangular array of numbers enclosed in a square brackets (or parentheses) is called a matrix (the plural is matrices) The entries of a matrix are called elements . Example #1 Consider the following two matrices. Is there any difference between them?

ByIntegration . what it takes to put data together. Ir. Richard Vdovjak, MTD. richardv@win.tue.nl. The Grand Challenge. The Integration System should: Provide flexible, homogeneous and transparent access to several (possibly) distributed, autonomous, and heterogeneous sources.

ByReverse Furthest Neighbors in Spatial Databases. Bin Yao , Feifei Li, Piyush Kumar Florida State University . A Novel Query Type. Reverse Furthest Neighbors (RFN) Given a point q and a data set P, find the set of points in P that take q as their furthest neighbor Two versions :

ByFlood Little, Cache More: Effective Result-Reuse in P2P IR Systems Christian Zimmer , Srikanta Bedathur, Gerhard Weikum Max-Planck Institute for Informatics, Saarbrücken, Germany http://www.mpi-inf.mpg.de Outline of the Talk Motivation System Architecture Caching Framework

By龙星计划课程 : 信息检索 Overview of Text Retrieval. ChengXiang Zhai (翟成祥) Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign http://www-faculty.cs.uiuc.edu/~czhai, czhai@cs.uiuc.edu.

ByDecision Theory Professor Ahmadi Learning Objectives Structuring the decision problem and decision trees Types of decision making environments: Decision making under uncertainty when probabilities are not known Decision making under risk when probabilities are known

ByEMGT 501 HW Solutions Chapter 12 - SELF TEST 9 Chapter 12 - SELF TEST 18 12-9 a. b. c. d. e. f. 12-18 a. b. e. Chapter 14 Decision Analysis Problem Formulation Decision Making without Probabilities Decision Making with Probabilities Risk Analysis and Sensitivity Analysis

ByExploring Microsoft Excel Chapter 1 Introduction to Microsoft Excel: What is a Spreadsheet? By Robert T. Grauer Maryann Barber Objectives (1 of 2) Describe what a spreadsheet is and potential applications

ByInteractive Rendering using the Render Cache Bruce Walter, George Drettakis iMAGIS*-GRAVIR/IMAG-INRIA Steven Parker University of Utah *iMAGIS is a joint project of CNRS/INRIA/INPG and UJF Motivation Goal: Interactive rendering Ray tracing Path tracing Motivation High-quality renderers

ByFinancial Accounting: Tools for Business Decision Making, 3rd Ed. ELS Kimmel, Weygandt, Kieso Chapter 8 Chapter 8 Reporting and Analyzing Receivables After studying Chapter 8, you should be able to : Identify the different types of receivables.

BySurvey of Business Information Systems. “The value of a program is proportional to the weight of its output . “ Laws of Computer Programming, VI. Levels of Business Activity. Operational Tactical Strategic Supported by Information Systems Transaction Processing Systems

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