CS 551 Computer Graphics. Hidden Surface Elimination. Z-Buffering. Basic idea: Hidden Surface Removal
|
|
- Caitlin Booth
- 5 years ago
- Views:
Transcription
1 CS 55 Computer Grphis Hidden Surfe Removl Hidden Surfe Elimintion Ojet preision lgorithms: determine whih ojets re in front of others Uses the Pinter s lgorithm drw visile surfes from k (frthest) to front (nerest) Resize doesn t require relultion Works for stti senes Methods: k-fe ulling, depth-sort, BSP tree Imge preision lgorithms: determine whih ojet is visile t eh pixel Resize requires relultion Works for dynmi senes Methods: z-uffering, ry tring Z-Buffering Imge preision lgorithm: Determine whih ojet is visile t eh pixel Order of polygons not ritil Works for dynmi senes Bsi ide: Rsterize (sn-onvert) eh polygon Keep trk of z vlue t eh pixel - Interpolte z vlue of polygon verties during rsteriztion Reple pixel with new olor if z vlue is smller (i.e., if ojet is loser to eye)
2 Z-Buffering: lgorithm llote z-uffer; // llote depth uffer Sme size s viewport. for eh pixel (x,y) // For eh pixel in viewport. writepixel(x,y,kgrnd); // Initilize olor. writedepth(x,y,frplne); // Initilize depth (z) uffer. for eh polygon // Drw eh polygon (in ny order). for eh pixel (x,y) in polygon // Rsterize polygon. p z = polygon s z-vlue t (x,y); // Interpolte z-vlue t (x, y). if (p z < z-uffer(x,y)) // If new depth is loser: writepixel(x,y,olor); // Write new (polygon) olor. writedepth(x,y,p z ); // Write new depth. Note: This ssumes you ve negted the z vlues! Z-Buffering: Exmple = = = = 5 5 Colors R R R R R G G G G R Depths
3 Z-Buffering: Computing Z How do you ompute the z vlue t given pixel? Sme wy you do for olor Interpolte from verties Wht is the z vlue t vertex? Cnnot use stright z vlue prior to perspetive divide Cnnot use simple perspetive projetion: - ll points re ompressed onto the view plne Need to use one of the nonil z vlues B Hs the form: z = z z Use /z where z is fter the viewing trnsform ut efore the perspetive trnsform Z-Buffering: Computing Z /Z Demo Let x = [.0,.0,.0,.0,.0,.0,.0,.0]; Let z = [-0.0, -0.0, -0.0, -0.0, -50.0, -60.0, -70.0, ]; ner = -.0 fr = Projet x nd z using projetion equtions Grph x vs. z world ND x vs. z-projeted (/z) Z-Buffering: Computing Z x vs z n x = x z x vs. z n y = y z fn z = n f z h =
4 Z-Buffering : Summry dvntges: Esy to implement in hrdwre (nd softwre!) Fst with hrdwre support Fst depth uffer memory Hrdwre supported Proess polygons in ritrry order Hndles polygon interpenetrtion trivilly Disdvntges: Lots of memory for z-uffer: - Integer depth vlues - Sn-line lgorithm Prone to lising - Super-smpling Overhed in z-heking: requires fst memory Bk-Fe Culling Don t drw surfes fing wy from viewpoint: ssumes ojets re solid polyhedr - Usully omined with dditionl method(s) Compute polygon norml n: - ssume ounter-lokwise vertex order (when n pointing towrds you) - For tringle (,, ): n = ( ) ( ) Compute vetor from viewpoint to ny point p on polygon v: - For orthogrphi projetion: v = [0 0 ] T - For perspetive projetion: v = p - eye Fing wy (don t drw) if ngle etween n nd v is less thn 90 dot produt n v > 0 z n x (or y) Pinter s lgorithm Drw surfes from k (frthest wy) to front (losest): Sort surfes/polygons y their depth (z vlue) Drw ojets in order (frthest to losest) Closer ojets pint over the top of frther wy ojets Need speil proessing if polygons overlp in depth
5 Binry Spe Prtition (BSP) Tree Reursively sudivide spe to determine depth order: Think of sene s lusters of ojets Find plne tht seprtes two lusters Cluster on sme side of plne s viewpoint n osure, ut nnot e osured y, luster on other side of plne Reursively sudivide lusters y finding pproprite plnes Store sudivision (spe-prtioning) plnes in inry tree - Node: tringle nd spe prtioning plne - Left (right) hild: sutree of ojets on negtive (positive) side of plne Very effiient for stti senes New viewpoints lulted quikly BSP Trees: Bsi Ide t t In the plne (= 0) eye Behind the plne ( side) In front of the plne ( side) BSP Trees: Bsi Ide (ont.) ssume (for now) the polygons do not ross the plne Let f (p) e the impliit eqution of plne evluted t p t t In the plne eye if (f t (eye)< 0) then drw t drw t else drw t drw t Behind the plne Works for ny eye point!! 5
6 BSP lgorithm BSP Tree: prtitions spe into positive nd negtive sides Node: tringle nd spe prtioning plne Left (right) hild: sutree of ojets on negtive (positive) side of plne Use modified in-order trversl to drw the sene: Strt nd root Compute whih side of node s plne eye is on Reursively sn onvert polygons on opposite side s eye Sn onvert node s polygon Reursively sn onvert polygons on sme side s eye eye Root polygon BSP lgorithm (ont.) eye Root polygon drw(tree, eye) { if (tree.empty()) then return; if (tree.f plne (eye) < 0) then drw(tree.plus, eye); rsterize(tree.tringle); drw(tree.minus, eye) else drw(tree.minus, eye); rsterize(tree.tringle); drw(tree.plus, eye); } Resulting drwing order:,,, Building the Tree: Plne Eqution Impliit eqution of plne f plne (p) = ( ) ( ) (p ) More fmilir formultion: ssumes onsistent f plne (p) = x By Cz D = 0 vertex ordering where [BC] T represents the norml vetor n to the plne n = ( ) ( ) Solve for D y plugging in point D = x By Cz = n So: f plne (p) = (n p) (n ) = n (p ) 6
7 Building the Tree: Psuedo-Code uildtree() { tree = node(t ); // Crete root of tree. for (i = to N) do // dd eh tringle to tree. tree.dd(t i ); } dd(tringle t) { f = f plne (t.); // Compute whih side of plne f = f plne (t.); // eh vertex is on. f = f plne (t.); if (f 0 nd f 0 nd f 0) // If ll verties on negtive side if (minus.empty()) minus = node(t); // dd t to minus side. else minus.dd(t) else if (f 0 nd f 0 nd f 0) // If ll verties on positive side.... // dd t to plus side else... } // Wht do we do here? Cutting Tringles Plne If tringle intersets plne Split B Must mintin sme vertex ordering so they ll keep the sme norml! t Plne t t B t =(,, ) t = (, B, ) t = (, B, ) Cutting Tringles (ont.) ssume we ve isolted on one side of plne nd tht f plne () > 0, then: dd t nd t to negtive sutree: minus.dd(t ) minus.dd(t ) dd t to positive sutree: plus.dd(t ) t Plne t t B t =(,, ) t = (, B, ) t = (, B, ) 7
8 Cutting Tringles (ont.) How do we find nd B? : intersetion of line etween nd with the plne f plne Use prmetri form of line: p(t) = t( ) Plug p into the plne eqution for the tringle: f plne (p) = (n p) D = n ( t( )) D Solve for t nd plug k into p(t) to get ( n ) D t = n ( ) Repet for B B We will use sme formul in ry tring!! Cutting Tringles (ont.) Wht if is not isolted? if (f * f 0) // If nd on sme side: ->; ->; ->; // Shift verties lokwise. else if (f * f 0) // If nd on sme side: ->; ->; ->; // Shift verties ounter-lokwise. Note: ug in the ook here! plne plne plne ssumes onsistent, ounter-lokwise ordering of verties Cutting Tringles: Complete lgorithm if (f * f 0) // If nd on sme side: ->; ->; ->; // Shift verties lokwise. else if (f * f 0) // If nd on sme side: ->; ->; ->; // Shift verties ounter-lokwise. // Now is isolted on one side of the plne. ompute,b; // Compute intersetions points. t = (,,); // Crete su-tringles. t = (,B,); t = (,B,); if (f plne () 0) // dd su-tringles to tree. minus.dd(t ); minus.dd(t ); plus.dd(t ); else plus.dd(t ); plus.dd(t ); minus.dd(t ); 8
9 BSP Trees: Summry How do you pik the first tringle? Tril nd error Pik few permuttions t rndom nd try them out It s pre-proess: don t worry muh out uild speed O(N) trversl on verge Cn e worse sine it retes more tringles! Eye n e pled nywhere No prolems with resizing BSP Trees: pplitions Originlly developed for hidden surfe determintion However, rrely used for this purpose now How else might we use BSP tree? Visiility ulling Ry tring Dynmi Senes Collisions BSP Trees: Representtion Our originl representtion put polygons t internl nd lef nodes eye Root polygon We n lso store polygons t lef nodes only. The BSP tree then eomes sptil sudivision dt struture 9
10 BSP Trees: Sptil Sudivision Our originl representtion put polygons t internl nd lef nodes - B eye E Root dividing plne D C E D B C We n lso store polygons t lef nodes only. The BSP tree then eomes sptil sudivision dt struture BSP Trees: View Culling Compre the frustum to the split plne If it s on one side, you n ompletely ignore the other! - BSP Trees: Sptil Suidivion- dyn. sene Wht if the sene is hnging? Ie. hrter is moving out the environment? ssume the hrter is point point CNNOT interset plne so it s either in front or ehind dd the hrter to the BSP tree - Fst only requires omprisons to the root node until you reh lef Modified in-order trversl results in the sene with the hrter drwn ppropritely wrt. the sene. 0
11 BSP Tree: Clrifition We dont relly re out lning Why? We hve to hit every node nywys to render so it s O(N) on verge whether lned or not - t lest when we re using it for k to front rendering We DO re out polygon splits good BSP tree is one tht doesn t rete lots of splits! BSP Trees & ZBuffer Initil Quke engine used BSP trees for front to k rendering Lter modified to omine BSP with Zuffer BSP the stti prt of the world When rendering, keep /z vlues (only need to write the zuffer, no heking neessry) When render the hrter lst nd z-uffer it
CS-184: Computer Graphics. Today. Clipping. Hidden Surface Removal. Tuesday, October 7, Clipping to view volume Clipping arbitrary polygons
CS184: Computer Grphics Lecture #10: Clipping nd Hidden Surfces Prof. Jmes O Brien University of Cliforni, Berkeley V2008S101.0 1 Tody Clipping Clipping to view volume Clipping ritrry polygons Hidden Surfce
More informationCS-184: Computer Graphics. Today. Lecture #10: Clipping and Hidden Surfaces ClippingAndHidden.key - October 27, 2014.
1 CS184: Computer Grphics Lecture #10: Clipping nd Hidden Surfces!! Prof. Jmes O Brien University of Cliforni, Berkeley! V2013F101.0 Tody 2 Clipping Clipping to view volume Clipping ritrry polygons Hidden
More informationCS 241 Week 4 Tutorial Solutions
CS 4 Week 4 Tutoril Solutions Writing n Assemler, Prt & Regulr Lnguges Prt Winter 8 Assemling instrutions utomtilly. slt $d, $s, $t. Solution: $d, $s, nd $t ll fit in -it signed integers sine they re 5-it
More informationParadigm 5. Data Structure. Suffix trees. What is a suffix tree? Suffix tree. Simple applications. Simple applications. Algorithms
Prdigm. Dt Struture Known exmples: link tble, hep, Our leture: suffix tree Will involve mortize method tht will be stressed shortly in this ourse Suffix trees Wht is suffix tree? Simple pplitions History
More informationChapter 9. Greedy Technique. Copyright 2007 Pearson Addison-Wesley. All rights reserved.
Chpter 9 Greey Tehnique Copyright 2007 Person Aison-Wesley. All rights reserve. Greey Tehnique Construts solution to n optimiztion prolem piee y piee through sequene of hoies tht re: fesile lolly optiml
More informationCS311H: Discrete Mathematics. Graph Theory IV. A Non-planar Graph. Regions of a Planar Graph. Euler s Formula. Instructor: Işıl Dillig
CS311H: Discrete Mthemtics Grph Theory IV Instructor: Işıl Dillig Instructor: Işıl Dillig, CS311H: Discrete Mthemtics Grph Theory IV 1/25 A Non-plnr Grph Regions of Plnr Grph The plnr representtion of
More informationCOMP 423 lecture 11 Jan. 28, 2008
COMP 423 lecture 11 Jn. 28, 2008 Up to now, we hve looked t how some symols in n lphet occur more frequently thn others nd how we cn sve its y using code such tht the codewords for more frequently occuring
More informationLesson 4.4. Euler Circuits and Paths. Explore This
Lesson 4.4 Euler Ciruits nd Pths Now tht you re fmilir with some of the onepts of grphs nd the wy grphs onvey onnetions nd reltionships, it s time to egin exploring how they n e used to model mny different
More informationLast Time? Ray Casting II. Explicit vs. Implicit? Assignment 1: Ray Casting. Object-Oriented Design. Graphics Textbooks
Csting II Lst Time? Csting / Tring Orthogrphi Cmer epresenttion (t) = origin + t * diretion -Sphere Intersetion -lne Intersetion Impliit vs. Epliit epresenttions MIT EECS 6.837, Cutler nd Durnd 1 MIT EECS
More informationCMPUT101 Introduction to Computing - Summer 2002
CMPUT Introdution to Computing - Summer 22 %XLOGLQJ&RPSXWHU&LUFXLWV Chpter 4.4 3XUSRVH We hve looked t so fr how to uild logi gtes from trnsistors. Next we will look t how to uild iruits from logi gtes,
More informationCS553 Lecture Introduction to Data-flow Analysis 1
! Ide Introdution to Dt-flow nlysis!lst Time! Implementing Mrk nd Sweep GC!Tody! Control flow grphs! Liveness nlysis! Register llotion CS553 Leture Introdution to Dt-flow Anlysis 1 Dt-flow Anlysis! Dt-flow
More informationEXPONENTIAL & POWER GRAPHS
Eponentil & Power Grphs EXPONENTIAL & POWER GRAPHS www.mthletics.com.u Eponentil EXPONENTIAL & Power & Grphs POWER GRAPHS These re grphs which result from equtions tht re not liner or qudrtic. The eponentil
More informationDoubts about how to use azimuth values from a Coordinate Object. Juan Antonio Breña Moral
Douts out how to use zimuth vlues from Coordinte Ojet Jun Antonio Breñ Morl # Definition An Azimuth is the ngle from referene vetor in referene plne to seond vetor in the sme plne, pointing towrd, (ut
More information10.5 Graphing Quadratic Functions
0.5 Grphing Qudrtic Functions Now tht we cn solve qudrtic equtions, we wnt to lern how to grph the function ssocited with the qudrtic eqution. We cll this the qudrtic function. Grphs of Qudrtic Functions
More informationCS453 INTRODUCTION TO DATAFLOW ANALYSIS
CS453 INTRODUCTION TO DATAFLOW ANALYSIS CS453 Leture Register llotion using liveness nlysis 1 Introdution to Dt-flow nlysis Lst Time Register llotion for expression trees nd lol nd prm vrs Tody Register
More informationIntroduction to Algebra
INTRODUCTORY ALGEBRA Mini-Leture 1.1 Introdution to Alger Evlute lgeri expressions y sustitution. Trnslte phrses to lgeri expressions. 1. Evlute the expressions when =, =, nd = 6. ) d) 5 10. Trnslte eh
More informationProblem Final Exam Set 2 Solutions
CSE 5 5 Algoritms nd nd Progrms Prolem Finl Exm Set Solutions Jontn Turner Exm - //05 0/8/0. (5 points) Suppose you re implementing grp lgoritm tt uses ep s one of its primry dt strutures. Te lgoritm does
More information10.2 Graph Terminology and Special Types of Graphs
10.2 Grph Terminology n Speil Types of Grphs Definition 1. Two verties u n v in n unirete grph G re lle jent (or neighors) in G iff u n v re enpoints of n ege e of G. Suh n ege e is lle inient with the
More informationSection 10.4 Hyperbolas
66 Section 10.4 Hyperbols Objective : Definition of hyperbol & hyperbols centered t (0, 0). The third type of conic we will study is the hyperbol. It is defined in the sme mnner tht we defined the prbol
More informationPresentation Martin Randers
Presenttion Mrtin Rnders Outline Introduction Algorithms Implementtion nd experiments Memory consumption Summry Introduction Introduction Evolution of species cn e modelled in trees Trees consist of nodes
More informationV = set of vertices (vertex / node) E = set of edges (v, w) (v, w in V)
Definitions G = (V, E) V = set of verties (vertex / noe) E = set of eges (v, w) (v, w in V) (v, w) orere => irete grph (igrph) (v, w) non-orere => unirete grph igrph: w is jent to v if there is n ege from
More informationMITSUBISHI ELECTRIC RESEARCH LABORATORIES Cambridge, Massachusetts. Introduction to Matroids and Applications. Srikumar Ramalingam
Cmrige, Msshusetts Introution to Mtrois n Applitions Srikumr Rmlingm MERL mm//yy Liner Alger (,0,0) (0,,0) Liner inepenene in vetors: v, v2,..., For ll non-trivil we hve s v s v n s, s2,..., s n 2v2...
More informationCompression Outline :Algorithms in the Real World. Lempel-Ziv Algorithms. LZ77: Sliding Window Lempel-Ziv
Compression Outline 15-853:Algorithms in the Rel World Dt Compression III Introduction: Lossy vs. Lossless, Benchmrks, Informtion Theory: Entropy, etc. Proility Coding: Huffmn + Arithmetic Coding Applictions
More informationCalculus Differentiation
//007 Clulus Differentition Jeffrey Seguritn person in rowot miles from the nerest point on strit shoreline wishes to reh house 6 miles frther down the shore. The person n row t rte of mi/hr nd wlk t rte
More informationCOMP108 Algorithmic Foundations
Grph Theory Prudene Wong http://www.s.liv..uk/~pwong/tehing/omp108/201617 How to Mesure 4L? 3L 5L 3L ontiner & 5L ontiner (without mrk) infinite supply of wter You n pour wter from one ontiner to nother
More informationDistance Computation between Non-convex Polyhedra at Short Range Based on Discrete Voronoi Regions
Distne Computtion etween Non-onvex Polyhedr t Short Rnge Bsed on Disrete Voronoi Regions Ktsuki Kwhi nd Hiroms Suzuki Deprtment of Preision Mhinery Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku,
More informationOutline. Motivation Background ARCH. Experiment Additional usages for Input-Depth. Regular Expression Matching DPI over Compressed HTTP
ARCH This work ws supported y: The Europen Reserh Counil, The Isreli Centers of Reserh Exellene, The Neptune Consortium, nd Ntionl Siene Foundtion wrd CNS-119748 Outline Motivtion Bkground Regulr Expression
More informationLecture 13: Graphs I: Breadth First Search
Leture 13 Grphs I: BFS 6.006 Fll 2011 Leture 13: Grphs I: Bredth First Serh Leture Overview Applitions of Grph Serh Grph Representtions Bredth-First Serh Rell: Grph G = (V, E) V = set of verties (ritrry
More information3D convex hulls. Convex Hull in 3D. convex polyhedron. convex polyhedron. The problem: Given a set P of points in 3D, compute their convex hull
Convex Hull in The rolem: Given set P of oints in, omute their onvex hull onvex hulls Comuttionl Geometry [si 3250] Lur Tom Bowoin College onvex olyheron 1 2 3 olygon olyheron onvex olyheron 4 5 6 Polyheron
More informationConvex Hull Algorithms. Convex hull: basic facts
CG Leture D Conve Hull Algorithms Bsi fts Algorithms: Nïve, Gift wrpping, Grhm sn, Quik hull, Divide-nd-onquer Lower ound 3D Bsi fts Algorithms: Gift wrpping, Divide nd onquer, inrementl Conve hulls in
More informationCS-184: Computer Graphics. Today
CS-184: Computer Grphics Lecture #10: Clippin n Hien Surces Pro. Jmes O Brien University o Cliorni, Berkeley V2006-F-10-1.0 Toy Clippin Clippin to view volume Clippin ritrry polyons Hien Surce Removl Z-Buer
More informationUnit 5 Vocabulary. A function is a special relationship where each input has a single output.
MODULE 3 Terms Definition Picture/Exmple/Nottion 1 Function Nottion Function nottion is n efficient nd effective wy to write functions of ll types. This nottion llows you to identify the input vlue with
More informationGreedy Algorithm. Algorithm Fall Semester
Greey Algorithm Algorithm 0 Fll Semester Optimiztion prolems An optimiztion prolem is one in whih you wnt to fin, not just solution, ut the est solution A greey lgorithm sometimes works well for optimiztion
More informationCan Pythagoras Swim?
Overview Ativity ID: 8939 Mth Conepts Mterils Students will investigte reltionships etween sides of right tringles to understnd the Pythgoren theorem nd then use it to solve prolems. Students will simplify
More informationCS201 Discussion 10 DRAWTREE + TRIES
CS201 Discussion 10 DRAWTREE + TRIES DrwTree First instinct: recursion As very generic structure, we could tckle this problem s follows: drw(): Find the root drw(root) drw(root): Write the line for the
More informationITEC2620 Introduction to Data Structures
ITEC0 Introduction to Dt Structures Lecture 7 Queues, Priority Queues Queues I A queue is First-In, First-Out = FIFO uffer e.g. line-ups People enter from the ck of the line People re served (exit) from
More informationOrthogonal line segment intersection
Computtionl Geometry [csci 3250] Line segment intersection The prolem (wht) Computtionl Geometry [csci 3250] Orthogonl line segment intersection Applictions (why) Algorithms (how) A specil cse: Orthogonl
More informationIf you are at the university, either physically or via the VPN, you can download the chapters of this book as PDFs.
Lecture 5 Wlks, Trils, Pths nd Connectedness Reding: Some of the mteril in this lecture comes from Section 1.2 of Dieter Jungnickel (2008), Grphs, Networks nd Algorithms, 3rd edition, which is ville online
More informationFinal Exam Review F 06 M 236 Be sure to look over all of your tests, as well as over the activities you did in the activity book
inl xm Review 06 M 236 e sure to loo over ll of your tests, s well s over the tivities you did in the tivity oo 1 1. ind the mesures of the numered ngles nd justify your wor. Line j is prllel to line.
More informationMa/CS 6b Class 1: Graph Recap
M/CS 6 Clss 1: Grph Recp By Adm Sheffer Course Detils Adm Sheffer. Office hour: Tuesdys 4pm. dmsh@cltech.edu TA: Victor Kstkin. Office hour: Tuesdys 7pm. 1:00 Mondy, Wednesdy, nd Fridy. http://www.mth.cltech.edu/~2014-15/2term/m006/
More informationDistance vector protocol
istne vetor protool Irene Finohi finohi@i.unirom.it Routing Routing protool Gol: etermine goo pth (sequene of routers) thru network from soure to Grph strtion for routing lgorithms: grph noes re routers
More information9.1 apply the distance and midpoint formulas
9.1 pply the distnce nd midpoint formuls DISTANCE FORMULA MIDPOINT FORMULA To find the midpoint between two points x, y nd x y 1 1,, we Exmple 1: Find the distnce between the two points. Then, find the
More informationStained Glass Design. Teaching Goals:
Stined Glss Design Time required 45-90 minutes Teching Gols: 1. Students pply grphic methods to design vrious shpes on the plne.. Students pply geometric trnsformtions of grphs of functions in order to
More informationMinimal Memory Abstractions
Miniml Memory Astrtions (As implemented for BioWre Corp ) Nthn Sturtevnt University of Alert GAMES Group Ferury, 7 Tlk Overview Prt I: Building Astrtions Minimizing memory requirements Performnes mesures
More informationAnswer Key Lesson 6: Workshop: Angles and Lines
nswer Key esson 6: tudent Guide ngles nd ines Questions 1 3 (G p. 406) 1. 120 ; 360 2. hey re the sme. 3. 360 Here re four different ptterns tht re used to mke quilts. Work with your group. se your Power
More information6.3 Volumes. Just as area is always positive, so is volume and our attitudes towards finding it.
6.3 Volumes Just s re is lwys positive, so is volume nd our ttitudes towrds finding it. Let s review how to find the volume of regulr geometric prism, tht is, 3-dimensionl oject with two regulr fces seprted
More informationCompilers Spring 2013 PRACTICE Midterm Exam
Compilers Spring 2013 PRACTICE Midterm Exm This is full length prctice midterm exm. If you wnt to tke it t exm pce, give yourself 7 minutes to tke the entire test. Just like the rel exm, ech question hs
More informationCameras. Importance of camera models
pture imges mesuring devie Digitl mers mers fill in memor ith olor-smple informtion D hrge-oupled Devie insted of film film lso hs finite resolution grininess depends on speed IS 00 00 6400 sie 35mm IMAX
More informationWhat do all those bits mean now? Number Systems and Arithmetic. Introduction to Binary Numbers. Questions About Numbers
Wht do ll those bits men now? bits (...) Number Systems nd Arithmetic or Computers go to elementry school instruction R-formt I-formt... integer dt number text chrs... floting point signed unsigned single
More information9 Graph Cutting Procedures
9 Grph Cutting Procedures Lst clss we begn looking t how to embed rbitrry metrics into distributions of trees, nd proved the following theorem due to Brtl (1996): Theorem 9.1 (Brtl (1996)) Given metric
More informationSolving Problems by Searching. CS 486/686: Introduction to Artificial Intelligence Winter 2016
Solving Prolems y Serching CS 486/686: Introduction to Artificil Intelligence Winter 2016 1 Introduction Serch ws one of the first topics studied in AI - Newell nd Simon (1961) Generl Prolem Solver Centrl
More informationCOMBINATORIAL PATTERN MATCHING
COMBINATORIAL PATTERN MATCHING Genomic Repets Exmple of repets: ATGGTCTAGGTCCTAGTGGTC Motivtion to find them: Genomic rerrngements re often ssocited with repets Trce evolutionry secrets Mny tumors re chrcterized
More informationVisibility Algorithms
Visibility Determintion Visibility Algorithms AKA, hidden surfce elimintion Roger Crwfis CIS 78 This set of slides reference slides used t Ohio Stte for instruction by Prof. Mchirju nd Prof. Hn-Wei Shen.
More informationGraphing Conic Sections
Grphing Conic Sections Definition of Circle Set of ll points in plne tht re n equl distnce, clled the rdius, from fixed point in tht plne, clled the center. Grphing Circle (x h) 2 + (y k) 2 = r 2 where
More informationMTH 146 Conics Supplement
105- Review of Conics MTH 146 Conics Supplement In this section we review conics If ou ne more detils thn re present in the notes, r through section 105 of the ook Definition: A prol is the set of points
More information2 Computing all Intersections of a Set of Segments Line Segment Intersection
15-451/651: Design & Anlysis of Algorithms Novemer 14, 2016 Lecture #21 Sweep-Line nd Segment Intersection lst chnged: Novemer 8, 2017 1 Preliminries The sweep-line prdigm is very powerful lgorithmic design
More informationBefore We Begin. Introduction to Spatial Domain Filtering. Introduction to Digital Image Processing. Overview (1): Administrative Details (1):
Overview (): Before We Begin Administrtive detils Review some questions to consider Winter 2006 Imge Enhncement in the Sptil Domin: Bsics of Sptil Filtering, Smoothing Sptil Filters, Order Sttistics Filters
More informationUT1553B BCRT True Dual-port Memory Interface
UTMC APPICATION NOTE UT553B BCRT True Dul-port Memory Interfce INTRODUCTION The UTMC UT553B BCRT is monolithic CMOS integrted circuit tht provides comprehensive MI-STD- 553B Bus Controller nd Remote Terminl
More informationLecture 7: Building 3D Models (Part 1) Prof Emmanuel Agu. Computer Science Dept. Worcester Polytechnic Institute (WPI)
Computer Grphics (CS 4731) Lecture 7: Building 3D Models (Prt 1) Prof Emmnuel Agu Computer Science Dept. Worcester Polytechnic Institute (WPI) Stndrd d Unit itvectors Define y i j 1,0,0 0,1,0 k i k 0,0,1
More informationSlides for Data Mining by I. H. Witten and E. Frank
Slides for Dt Mining y I. H. Witten nd E. Frnk Simplicity first Simple lgorithms often work very well! There re mny kinds of simple structure, eg: One ttriute does ll the work All ttriutes contriute eqully
More informationP(r)dr = probability of generating a random number in the interval dr near r. For this probability idea to make sense we must have
Rndom Numers nd Monte Crlo Methods Rndom Numer Methods The integrtion methods discussed so fr ll re sed upon mking polynomil pproximtions to the integrnd. Another clss of numericl methods relies upon using
More informationQuestions About Numbers. Number Systems and Arithmetic. Introduction to Binary Numbers. Negative Numbers?
Questions About Numbers Number Systems nd Arithmetic or Computers go to elementry school How do you represent negtive numbers? frctions? relly lrge numbers? relly smll numbers? How do you do rithmetic?
More informationZZ - Advanced Math Review 2017
ZZ - Advnced Mth Review Mtrix Multipliction Given! nd! find the sum of the elements of the product BA First, rewrite the mtrices in the correct order to multiply The product is BA hs order x since B is
More informationMA1008. Calculus and Linear Algebra for Engineers. Course Notes for Section B. Stephen Wills. Department of Mathematics. University College Cork
MA1008 Clculus nd Liner Algebr for Engineers Course Notes for Section B Stephen Wills Deprtment of Mthemtics University College Cork s.wills@ucc.ie http://euclid.ucc.ie/pges/stff/wills/teching/m1008/ma1008.html
More information1 Quad-Edge Construction Operators
CS48: Computer Grphics Hndout # Geometric Modeling Originl Hndout #5 Stnford University Tuesdy, 8 December 99 Originl Lecture #5: 9 November 99 Topics: Mnipultions with Qud-Edge Dt Structures Scribe: Mike
More informationToday. CS 188: Artificial Intelligence Fall Recap: Search. Example: Pancake Problem. Example: Pancake Problem. General Tree Search.
CS 88: Artificil Intelligence Fll 00 Lecture : A* Serch 9//00 A* Serch rph Serch Tody Heuristic Design Dn Klein UC Berkeley Multiple slides from Sturt Russell or Andrew Moore Recp: Serch Exmple: Pncke
More informationWhat do all those bits mean now? Number Systems and Arithmetic. Introduction to Binary Numbers. Questions About Numbers
Wht do ll those bits men now? bits (...) Number Systems nd Arithmetic or Computers go to elementry school instruction R-formt I-formt... integer dt number text chrs... floting point signed unsigned single
More informationbinary trees, expression trees
COMP 250 Lecture 21 binry trees, expression trees Oct. 27, 2017 1 Binry tree: ech node hs t most two children. 2 Mximum number of nodes in binry tree? Height h (e.g. 3) 3 Mximum number of nodes in binry
More informationTiling Triangular Meshes
Tiling Tringulr Meshes Ming-Yee Iu EPFL I&C 1 Introdution Astrt When modelling lrge grphis senes, rtists re not epeted to model minute nd repetitive fetures suh s grss or snd with individul piees of geometry
More informationMa/CS 6b Class 1: Graph Recap
M/CS 6 Clss 1: Grph Recp By Adm Sheffer Course Detils Instructor: Adm Sheffer. TA: Cosmin Pohot. 1pm Mondys, Wednesdys, nd Fridys. http://mth.cltech.edu/~2015-16/2term/m006/ Min ook: Introduction to Grph
More informationCS 340, Fall 2016 Sep 29th Exam 1 Note: in all questions, the special symbol ɛ (epsilon) is used to indicate the empty string.
CS 340, Fll 2016 Sep 29th Exm 1 Nme: Note: in ll questions, the speil symol ɛ (epsilon) is used to indite the empty string. Question 1. [10 points] Speify regulr expression tht genertes the lnguge over
More informationGENG2140 Modelling and Computer Analysis for Engineers
GENG4 Moelling n Computer Anlysis or Engineers Letures 9 & : Gussin qurture Crete y Grn Romn Joles, PhD Shool o Mehnil Engineering, UWA GENG4 Content Deinition o Gussin qurture Computtion o weights n points
More informationHomework. Context Free Languages III. Languages. Plan for today. Context Free Languages. CFLs and Regular Languages. Homework #5 (due 10/22)
Homework Context Free Lnguges III Prse Trees nd Homework #5 (due 10/22) From textbook 6.4,b 6.5b 6.9b,c 6.13 6.22 Pln for tody Context Free Lnguges Next clss of lnguges in our quest! Lnguges Recll. Wht
More information5 ANGLES AND POLYGONS
5 GLES POLYGOS urling rige looks like onventionl rige when it is extene. However, it urls up to form n otgon to llow ots through. This Rolling rige is in Pington sin in Lonon, n urls up every Friy t miy.
More informationIn the last lecture, we discussed how valid tokens may be specified by regular expressions.
LECTURE 5 Scnning SYNTAX ANALYSIS We know from our previous lectures tht the process of verifying the syntx of the progrm is performed in two stges: Scnning: Identifying nd verifying tokens in progrm.
More informationToday. Search Problems. Uninformed Search Methods. Depth-First Search Breadth-First Search Uniform-Cost Search
Uninformed Serch [These slides were creted by Dn Klein nd Pieter Abbeel for CS188 Intro to AI t UC Berkeley. All CS188 mterils re vilble t http://i.berkeley.edu.] Tody Serch Problems Uninformed Serch Methods
More informationAgilent Mass Hunter Software
Agilent Mss Hunter Softwre Quick Strt Guide Use this guide to get strted with the Mss Hunter softwre. Wht is Mss Hunter Softwre? Mss Hunter is n integrl prt of Agilent TOF softwre (version A.02.00). Mss
More information3 4. Answers may vary. Sample: Reteaching Vertical s are.
Chpter 7 Answers Alterntive Activities 7-2 1 2. Check students work. 3. The imge hs length tht is 2 3 tht of the originl segment nd is prllel to the originl segment. 4. The segments pss through the endpoints
More informationFrom Dependencies to Evaluation Strategies
From Dependencies to Evlution Strtegies Possile strtegies: 1 let the user define the evlution order 2 utomtic strtegy sed on the dependencies: use locl dependencies to determine which ttriutes to compute
More informationLecture 10 Evolutionary Computation: Evolution strategies and genetic programming
Lecture 10 Evolutionry Computtion: Evolution strtegies nd genetic progrmming Evolution strtegies Genetic progrmming Summry Negnevitsky, Person Eduction, 2011 1 Evolution Strtegies Another pproch to simulting
More informationMATH 25 CLASS 5 NOTES, SEP
MATH 25 CLASS 5 NOTES, SEP 30 2011 Contents 1. A brief diversion: reltively prime numbers 1 2. Lest common multiples 3 3. Finding ll solutions to x + by = c 4 Quick links to definitions/theorems Euclid
More informationSolving Problems by Searching. CS 486/686: Introduction to Artificial Intelligence
Solving Prolems y Serching CS 486/686: Introduction to Artificil Intelligence 1 Introduction Serch ws one of the first topics studied in AI - Newell nd Simon (1961) Generl Prolem Solver Centrl component
More informationUNCORRECTED SAMPLE PAGES. Angle relationships and properties of 6geometrical figures 1. Online resources. What you will learn
Online resoures uto-mrked hpter pre-test Video demonstrtions of ll worked exmples Intertive widgets Intertive wlkthroughs Downlodle HOTsheets ess to ll HOTmths ustrlin urriulum ourses ess to the HOTmths
More informationGeometric Algorithms. Geometric Algorithms. Warning: Intuition May Mislead. Geometric Primitives
Geometri Algorithms Geometri Algorithms Convex hull Geometri primitives Closest pir of points Voronoi Applitions. Dt mining. VLSI design. Computer vision. Mthemtil models. Astronomil simultion. Geogrphi
More informationCSCI 446: Artificial Intelligence
CSCI 446: Artificil Intelligence Serch Instructor: Michele Vn Dyne [These slides were creted by Dn Klein nd Pieter Abbeel for CS188 Intro to AI t UC Berkeley. All CS188 mterils re vilble t http://i.berkeley.edu.]
More informationRepresentation of Numbers. Number Representation. Representation of Numbers. 32-bit Unsigned Integers 3/24/2014. Fixed point Integer Representation
Representtion of Numbers Number Representtion Computer represent ll numbers, other thn integers nd some frctions with imprecision. Numbers re stored in some pproximtion which cn be represented by fixed
More informationMath 142, Exam 1 Information.
Mth 14, Exm 1 Informtion. 9/14/10, LC 41, 9:30-10:45. Exm 1 will be bsed on: Sections 7.1-7.5. The corresponding ssigned homework problems (see http://www.mth.sc.edu/ boyln/sccourses/14f10/14.html) At
More informationIntegration. October 25, 2016
Integrtion October 5, 6 Introduction We hve lerned in previous chpter on how to do the differentition. It is conventionl in mthemtics tht we re supposed to lern bout the integrtion s well. As you my hve
More informationTries. Yufei Tao KAIST. April 9, Y. Tao, April 9, 2013 Tries
Tries Yufei To KAIST April 9, 2013 Y. To, April 9, 2013 Tries In this lecture, we will discuss the following exct mtching prolem on strings. Prolem Let S e set of strings, ech of which hs unique integer
More informationAnnouncements. CS 188: Artificial Intelligence Fall Recap: Search. Today. Example: Pancake Problem. Example: Pancake Problem
Announcements Project : erch It s live! Due 9/. trt erly nd sk questions. It s longer thn most! Need prtner? Come up fter clss or try Pizz ections: cn go to ny, ut hve priority in your own C 88: Artificil
More informationReducing a DFA to a Minimal DFA
Lexicl Anlysis - Prt 4 Reducing DFA to Miniml DFA Input: DFA IN Assume DFA IN never gets stuck (dd ded stte if necessry) Output: DFA MIN An equivlent DFA with the minimum numer of sttes. Hrry H. Porter,
More informationGrade 7/8 Math Circles Geometric Arithmetic October 31, 2012
Fculty of Mthemtics Wterloo, Ontrio N2L 3G1 Grde 7/8 Mth Circles Geometric Arithmetic Octoer 31, 2012 Centre for Eduction in Mthemtics nd Computing Ancient Greece hs given irth to some of the most importnt
More informationAssembly & Installation Instructions: 920 CPU Holder, 920-X
Assemly & Instlltion Instrutions: 920 CPU Holder, 920-X Prt Inluded, CPU Holder (ll models) A Exterior Housing B Interior Housing C Hrdwre Kit (ll models) D CPU Supporting Plte F Loking Kit (models 920-FL
More informationHyperbolas. Definition of Hyperbola
CHAT Pre-Clculus Hyperols The third type of conic is clled hyperol. For n ellipse, the sum of the distnces from the foci nd point on the ellipse is fixed numer. For hyperol, the difference of the distnces
More informationPythagoras theorem and trigonometry (2)
HPTR 10 Pythgors theorem nd trigonometry (2) 31 HPTR Liner equtions In hpter 19, Pythgors theorem nd trigonometry were used to find the lengths of sides nd the sizes of ngles in right-ngled tringles. These
More informationCSCI 104. Rafael Ferreira da Silva. Slides adapted from: Mark Redekopp and David Kempe
CSCI 0 fel Ferreir d Silv rfsilv@isi.edu Slides dpted from: Mrk edekopp nd Dvid Kempe LOG STUCTUED MEGE TEES Series Summtion eview Let n = + + + + k $ = #%& #. Wht is n? n = k+ - Wht is log () + log ()
More informationRight Angled Trigonometry. Objective: To know and be able to use trigonometric ratios in rightangled
C2 Right Angled Trigonometry Ojetive: To know nd e le to use trigonometri rtios in rightngled tringles opposite C Definition Trigonometry ws developed s method of mesuring ngles without ngulr units suh
More informationCOSC 6374 Parallel Computation. Communication Performance Modeling (II) Edgar Gabriel Fall Overview. Impact of communication costs on Speedup
COSC 6374 Prllel Computtion Communition Performne Modeling (II) Edgr Griel Fll 2015 Overview Impt of ommunition osts on Speedup Crtesin stenil ommunition All-to-ll ommunition Impt of olletive ommunition
More informationsuch that the S i cover S, or equivalently S
MATH 55 Triple Integrls Fll 16 1. Definition Given solid in spce, prtition of consists of finite set of solis = { 1,, n } such tht the i cover, or equivlently n i. Furthermore, for ech i, intersects i
More informationMATH 2530: WORKSHEET 7. x 2 y dz dy dx =
MATH 253: WORKSHT 7 () Wrm-up: () Review: polr coordintes, integrls involving polr coordintes, triple Riemnn sums, triple integrls, the pplictions of triple integrls (especilly to volume), nd cylindricl
More information