Computing Volumes of Solids Enclosed by Recursive Subdivision Surfaces

Size: px
Start display at page:

Download "Computing Volumes of Solids Enclosed by Recursive Subdivision Surfaces"

Transcription

1 EUROGRAPHI 97 / D. Fellner and L. zrmay-kalos (Guest Edtors) olume 6, (997), Number 3 omputng olumes of olds Enlosed by Reursve ubdvson urfaes Jörg Peters and Ahmad Nasr Abstrat The volume of a sold enlosed by a reursve subdvson surfae an be approxmated based on the losed-form representaton of regular parts of the subdvson surfae and a tght estmate of the loal onvex hull near extraordnary ponts. The approah presented s effent,.e. non-exponental, and robust n that t yelds rapdly ontratng error boundng boxes. An extenson to measurng hgher-order moments s skethed.. Introduton Due to oneptual smplty and dsplay by averagng, subdvson surfaes are a popular representaton for modelng, graphs and multresoluton (see e.g. 2, 5, 7, 7, 6, 8 and referenes theren). One of the noted drawbaks of subdvson surfaes for quanttatve applatons s ther lak of a fnte losed-form representaton. Already the bas queston, what s the volume enlosed by a subdvson surfae, s not treated n the lterature and does not seem to have a smple answer. Thus realst anmaton as opposed to artoons and geometr desgn usng ths onvenent representaton must rely on user ntuton rather than omputer gudane to generate objets that hold a gven volume or, n the ase of omputng the enter of mass, do not topple over. Ths gap n the subdvson arsenal s flled by the present paper. We develop an effent and robust algorthm for measurng volumes of subdvson surfaes and sketh how hgher-order moments an be treated n the same framework. For omparson, onsder frst the dret approah of splttng the volume enlosed by the subdvson surfae nto tetrahedra or sles as s the ommon approah when measurng polyhedra 0,, 9 ). The number of pees to be measured for exat alulaton s proportonal to the number of faets. ne eah polyhedron faet gves rse to a onstant number of new faets wth eah subdvson step, the resultng amount of work s exponental n the number of subdvson steps and hene unpratal for real tme applatons. A more sophstated approah s to ompute the volume of the upported by NF Natona Young Investgator grant R upported by an AUB grant ntal polyhedron and then trak the hange n volume due to the refnement, nterpretng the refnement as a sequene of uts appled to the sold enlosed by the polyhedron 3. However, ths trakng s not easly mplemented sne uts, n general, are nether smple nor planar and may add or remove materal dependng on loal onvexty. The approah s also not effent sne the amount of work s stll proportonal to the exponental nrease of the number of faets under subdvson. Yet there s an effent and robust approah to measurng the volume of most subdvson surfaes. Effent means that the amount of work s onstant at eah subdvson step and robust means that a two-sded error bound exsts and ontrats by a onstant multple less than, typally 2 3, wth eah refnement step. The two key observatons leadng to the result are () that peewse polynomal surfaes allow effent omputaton of the exat volume and hgher-order moments 6 and (2) that most popular subdvson shemes, e.g. 4, 2, are modfatons of box-splne subdvson rules, and hene generate a lmt surfae that s peewse polynomal exept at some solated ponts, alled extraordnary ponts. That s, whle subdvson surfaes lak a global losed-form representaton, nreasng submeshes of the subdvson polyhedron have suh a representaton. Ths sad, the overall strategy s lear namely to ompute the asymptot volume ontrbutons exatly for the regular regons of the subdvson surfae away from the extraordnary ponts, and to estmate and bound the volume ontrbutons of the neghborhood of eah extraordnary pont. For larty we lst our assumptons on the subdvson sheme. The Eurographs Assoaton 997. Publshed by Blakwell Publshers, 08 owley Road, Oxford OX4 JF, UK and 350 Man treet, Malden, MA 0248, UA.

2 Peters and Nasr / omputng ubdvson olumes Doo-abn Extraordnary Pont enter of regular submesh atmull-lark Fgure : Damonds mark extraordnary ponts on the subdvson polyhedra. The enters of regular submeshes n the less refned polyhedra on the left are marked by rles. A regular submesh of the Doo-abn sheme (top) onssts of four quadrlaterals surroundng a vertex. A regular submesh of the atmull-lark sheme (bottom) onssts of eght quadrlaterals surroundng a quadrlateral.. The subdvson sheme generates regular submeshes that refne to regular submeshes so that the number of extraordnary ponts remans onstant. 2. Regular submeshes have a polynomal parametrzaton. 3. The subdvson algorthm has the loal onvex hull property. That s, new mesh ponts are onvex ombnatons of old ones nearby, e.g. when the subdvson mask has only non-negatve entres. We proeed by showng, n eton 2, how the volume s omputed by ntegratng over the surfae, and ompute the ontrbuton of regular subsurfaes. eton 3 defnes the approxmaton aps over the remanng volume and ther ntegrals, and eton 4 gves a onrete example n terms of the Doo-abn subdvson. eton 5 outlnes the omputaton of mass propertes. 2. olume ontrbutons from regular submeshes At eah step, a subdvson algorthm reates a new mesh of ponts from an old mesh. A desrable property of any subdvson algorthm s that t generates nreasng submeshes all of whose ponts have the same valene and whose faets all have the same number of edges. A submesh of ponts and faets wth ths standard valene and number of edges s alled regular. Popular subdvson shemes derve ther appeal from the fat that the lmt surfae s expltly known for regular submeshes. For example, the lmt surfae of the Doo-abn subdvson sheme 4 appled to a regular nne-pont submesh s a bquadrat tensor-produt splne surfae. The lmt surfae of the atmull-lark subdvson sheme 2 appled to a regular sxteen-pont submesh s a bub tensor-produt splne surfae. And the lmt surfae of Loop s subdvson s a lnear ombnaton of shfts of a 3-dreton box splne. Wth respet to these subsurfaes we wll apply Gauss dvergene theorem. Gven a parametrzaton x u x y z u v of a surfae wth normal x u v y u v z u v N n n where n u v U x u x v and gven a map f : R 3 R 3, the dvergene theorem (see e.g ) states that f d f N d.e. that the ntegral of the dvergene f x f over the volume equals the ntegral of the normal omponent f N f N over the surfae of. By hange of varable d U n dudv.e. the area element n s the nverse of the normalzaton fator of the normal dreton and hene f d f n n d U f n dudv Now f both f and x are polynomal then the ntegrand s polynomal; and f U s a smple doman, say a trangle or square, then the ntegral an be determned effently, expltly and exatly by averagng the Bernsten-Bézer oeffents 3. If the parametrzaton x of onssts of pathes x and U s the unon of the path domans U then U f n dudv U f n du dv hoosng f 0 0 z we need only ompute one omponent of n, n 3 to determne the volume : d x y u v x y v u 0 0 z d 0 0 z n n 2 n 3 d U z n 3 dudv The Eurographs Assoaton 997

3 Peters and Nasr / omputng ubdvson olumes Fgure 2: Unon of surfae layers at a 5-valent extraordnary pont. The algorthm Intally the algorthm omputes the volume ontrbuton 0 for all, possbly zero, surfae pees orrespondng to regular submeshes: for eah path (f. Fgure 3) z u v n 3 u v s omputed and ntegrated. Eah subsequent subdvson adds exatly one, geometrally ever smaller, layer of surfae pees defned by regular submeshes around eah extraordnary pont as shown n Fgure 2 and Fgure 3. ne the ontrbutons to the volume by these regular layers are omputed exatly and for the lmt surfae, the ontrbuton of submeshes obtaned by subdvdng a regular submesh need not be reomputed! Thus the work at eah subdvson step s onstant, proportonal only to the number of extraordnary ponts; n the mth step t onssts of omputng the surfae ntegral m of the mth layer added at the extraordnary pont. Wth W m the surfae ntegral of the ap whose onstruton s explaned n eton 3, the approxmaton to the volume at the mth step s m m 0 eton 4 gves a onrete example. W m 3. olume ontrbutons of the neghborhood of an extraordnary pont Havng dealt wth the regular parts of the mesh n the prevous seton, the goal of ths seton s to ap off the holes remanng n the regular surfae around the extraordnary ponts and ompute the ntegrals of the appng surfaes. To dretly apply the dvergene theorem, eah ap should jon the regular surfae ontnuously and wthout gap or overlap. Extraordnary ponts are typally ether meshponts wth a valene dfferent from the regular meshponts, or entrods of Fgure 3: On top, dots, some labelled jk, ndate the nodes of a submesh that determnes the subdvson matrx at an extraordnary pont n the Doo-abn sheme. The grey quadrlaterals eah represent a bquadrat path. Below rles ndate the nodes of the refned submesh. Agan the bquadrat pathes are delneated. mesh faets that have a dfferent than the standard number of edges. For example, f. Fgure, the Doo-abn subdvson sheme has a standard valene of four for both vertes and faets,.e. every mesh pont s surrounded by four quadrlaterals. The error boundng box By assumpton, the volume s bounded by the onvex hull of the submesh n the neghborhood of the extraordnary pont. Fgure 3 shematally shows suh a submesh for the Doo- abn sheme. The estmate an be mproved by knot nserton,.e. onverson to Bernsten-Bézer form at the boundary between the regular surfae pees and the neghborhood of the extraordnary pont. Fgure 4 shows the ndes of the The Eurographs Assoaton 997

4 Peters and Nasr / omputng ubdvson olumes Bernsten-Bézer oeffents b 02, b 2, b 22, b 2, b 20 along the boundary for the Doo-abn subdvson. ne, dependng on the valene of the extraordnary pont, the exat onvex hull may be expensve to ompute, we settle for an effently omputable boundng box that enloses the mnmal and maxmal values of eah omponent of the ontrol ponts n the neghborhood of the extraordnary pont. To get an estmate of the ontraton of ths error box t s natural and standard to look at the subdvson matrx whh maps the submesh around the extraordnary pont to a refned submesh wth the same number of mesh ponts. For the Doo- abn sheme the matrx s of sze p p where p q 3 3 at a q-valent extraordnary pont, e.g. p 45 for the example n Fgure 3. For a smooth-surfang sheme the leadng egenvalue of ths matrx s and the two subdomnant, next largest egenvalues determne the frst-order behavor of the subdvson surfae and hene the asymptot shrnkage fator of the submesh surroundng the extraordnary pont 4. Denotng the seond-largest egenvalue by λ, the boundng box volume n three dmensons onverges asymptotally on the order of λ 3 wth eah refnement step. In pratal examples the asymptot rate s mmedately reahed (.f. Tables and 2). Fnally we defne the aps. To get a good estmate of the volume the ap pathes must le wthn the boundng box. We hoose them as a rng, orrespondng to the whte quadrlaterals n Fgure 4, of 0 -onneted pathes, of the same degree as the regular k subdvson surfae, and extendng the regular surfae parametrally k to over the neghborhood of the extraordnary pont. Ths avods gaps and determnes k layers of Bernsten-Bézer oeffents adjaent to the regular surfae. ewed as the result of knot nserton, the Bernsten-Bézer oeffents together wth the oeffent or rng of oeffents losest to the extraordnary pont form a onvex hull of the lmt surfae. The remanng nnermost rng(s) of oeffents may be determned by symmetry and knowledge of the lmtng surfae. For example, for the Doo-abn algorthm, the extenson determnes all but the Bernsten-Bézer oeffent wth ndex 00 (.f. Fgure 4). A natural hoe for ths oeffent s the entrod of the n-sded mesh ell whh s nterpolated by the lmt surfae. For the atmull-lark algorthm, the 2 extenson pns down all but one Bernsten-Bézer oeffent whh we may hoose to be the extraordnary pont of the subdvson polyhedron. 4. Example: The volume of Doo-abn subdvson surfaes The Doo-abn algorthm s a generalzaton of the subdvson sheme for bquadrat tensor produt B-splnes. For eah n-gon of the orgnal mesh, a new, smaller n-gon s reated and onneted wth ts neghbors as shown n Fgure 5. The masks for generatng a new n-gon from an old one are spefed n Fgure 6 for the regular ase n 4 (left), and the Fgure 4: Indes of the Bernsten-Bézer oeffents of a bquadrat extenson. Fgure 5: Mesh refnement by the Doo-abn algorthm. general ase (rght). In 4 Doo and abn suggest α j 3 2os 2π j n 4n 4 f j 0 0 else A regular Doo-abn submesh onssts of nne ponts j arranged as four quadrlateral faets surroundng a entral pont (.f. Fgure 3): Fgure 6: Masks for the Doo-abn algorthm. The Eurographs Assoaton 997

5 Peters and Nasr / omputng ubdvson olumes Fgure 7: olume error boxes of the frst three Doo-abn subdvsons of a ube-shaped mesh. subdvson error box volume e e e e e e e e-08 Table : olume of eah error box when subdvdng the unt ube. The volume measured n the eghth subdvson s The ponts defne a bquadrat path wth Bernsten-Bézer oeffents b jk where b b b b b b b b b The Eurographs Assoaton For ths path, z n 3 s a salar-valued bqunt polynomal. Denotng ts Bernsten-Bézer oeffents by p j ts ntegral s j 0 p j. To ap the neghborhood of the extraordnary pont for the gven subdvson step, the oeffents jk generate the Bernsten-Bézer oeffents of a rng of bquadrat pathes defned by extenson and nterpolaton of the entrod: b 02 b 2 b 22 b b 00 b 2 b n n 00 b b The subdomnant egenvalue of the subdvson matrx s λ 2 mplyng an asymptot reduton of the error box volume by 2 3. Indeed, the error bounds are onfrmed by Table and Table 2 wth a graphal nterpretaton shown n 00 Fgure 4 and Fgure 8. A tghter error bound ould be obtaned by observng that 00 b mples that the lmt surfae les n the onvex hull of the Bernsten-Bézer oeffents of the ap. 5. Extenson to hgher-order Moments The proedure outlned for the zeroth order moment, the volume, s easly extended to hgher-order moments (.f. 6 ). For example, to ompute the x-omponent of the enter of mass we an hoose f 0 0 xz. As a vsual ad a mass error-box whose sze s the sum of the ndvdual error boxes may be plaed at the enter of mass of the approxmate surfae. Referenes. BERNARDINI, F. Integraton of polynomals over n- dmensonal polyhedra. omputer Aded Desgn 23() (99), ATMULL, E., AND LARK, J. Reursvely generated B-splne surfaes on arbtrary topologal meshes. omputer Aded Des. 0 (978), DE BOOR,. B-form bass. In Geometr Modelng: Algorthms and New Trends, G. Farn, Ed. IAM, Phladelpha, 987, pp

6 Peters and Nasr / omputng ubdvson olumes Fgure 8: olume error boxes for a stenl shape. val- subdvson ene Table 2: olume of error boxes for extraordnary ponts of valene 3,4,5,6 when subdvdng the stenl shape. 4. DOO, D., AND ABIN, M. Behavour of reursve subdvson surfaes near extraordnary ponts. omputer Aded Des. 0, 6 (Nov 978), DYN, N., LEIN, D., AND GREGORY, J. A. A butterfly subdvson sheme for surfae nterpolaton wth tenson ontrol. AM Trans. on Graphs 9 (990), GONZALEZ-OHOA,., MAMMON,., AND PE- TER, J. omputng moments of peewse polynomal surfaes. xxx xx (99x). submtted. 7. HOPPE, H., DEROE, T., DUHAMP, T., HALTEAD, M., JIN, H., MDONALD, J., HWEITZER, J., AND TUETZLE, W. Peewse smooth surfae reonstruton. omputer Graphs Proeedngs of ggraph 94 (994), KOBBELT, L. Interpolatory subdvson on open quadrlateral nets wth arbtrary topology. vol. 5, Basl Blakwell Ltd. Eurographs 96 onferene ssue. 9. LEE, Y. T., AND REQUIHA, A. A. G. Algorthms for omputng the volume and other ntegral propertes of solds. II a famly of algorthms based on representaton onverson and ellular approxmaton. ommun. AM (UA) 25 (ept. 982), LIEN,., AND KAJIYA, J. A symbol method for alulatng the ntegral propertes of arbtrary nononvex polyhedra. IEEE omputer Graphs and Applatons 4(9) (Ot 984), LOOP,. mooth subdvson for surfaes based on trangles. Thess, Unv. of Utah (987). 2. NARI, A. Polyhedral subdvson methods for freeform surfaes. AM Transatons on Graphs 6 (987), NARI, A. An algorthm to ompute volume of solds defned by reursve subdvson surfaes. Teh. rep., Ameran Unversty of Berut, 996. TR REIF, U. A unfed approah to subdvson algorthms near extraordnary vertes. AGD 2 (995), RUDIN, W. Real and omplex analyss. eres n Hgher Mathemats. MGraw-Hll, HRODER, WELDEN, AND ZORIN. Interpolatng subdvson for meshes wth arbtrary topology. omputer Graphs Proeedngs of ggraph 96 (996), TOLLNITZ, E. J., DEROE, T. D., AND ALEIN, D. H. Wavelets for omputer graphs: theory and applatons. Morgan Kaufmann, an Franso, alf, 996. The Eurographs Assoaton 997

Interpolation of the Irregular Curve Network of Ship Hull Form Using Subdivision Surfaces

Interpolation of the Irregular Curve Network of Ship Hull Form Using Subdivision Surfaces 7 Interpolaton of the Irregular Curve Network of Shp Hull Form Usng Subdvson Surfaces Kyu-Yeul Lee, Doo-Yeoun Cho and Tae-Wan Km Seoul Natonal Unversty, kylee@snu.ac.kr,whendus@snu.ac.kr,taewan}@snu.ac.kr

More information

Reading. 14. Subdivision curves. Recommended:

Reading. 14. Subdivision curves. Recommended: eadng ecommended: Stollntz, Deose, and Salesn. Wavelets for Computer Graphcs: heory and Applcatons, 996, secton 6.-6., A.5. 4. Subdvson curves Note: there s an error n Stollntz, et al., secton A.5. Equaton

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

Multiscale Heterogeneous Modeling with Surfacelets

Multiscale Heterogeneous Modeling with Surfacelets 759 Multsale Heterogeneous Modelng wth Surfaelets Yan Wang 1 and Davd W. Rosen 2 1 Georga Insttute of Tehnology, yan.wang@me.gateh.edu 2 Georga Insttute of Tehnology, davd.rosen@me.gateh.edu ABSTRACT Computatonal

More information

Computer Graphics. - Spline and Subdivision Surfaces - Hendrik Lensch. Computer Graphics WS07/08 Spline & Subdivision Surfaces

Computer Graphics. - Spline and Subdivision Surfaces - Hendrik Lensch. Computer Graphics WS07/08 Spline & Subdivision Surfaces Computer Graphcs - Splne and Subdvson Surfaces - Hendrk Lensch Overvew Last Tme Image-Based Renderng Today Parametrc Curves Lagrange Interpolaton Hermte Splnes Bezer Splnes DeCasteljau Algorthm Parameterzaton

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

Session 4.2. Switching planning. Switching/Routing planning

Session 4.2. Switching planning. Switching/Routing planning ITU Semnar Warsaw Poland 6-0 Otober 2003 Sesson 4.2 Swthng/Routng plannng Network Plannng Strategy for evolvng Network Arhtetures Sesson 4.2- Swthng plannng Loaton problem : Optmal plaement of exhanges

More information

Connectivity in Fuzzy Soft graph and its Complement

Connectivity in Fuzzy Soft graph and its Complement IOSR Journal of Mathemats (IOSR-JM) e-issn: 2278-5728, p-issn: 2319-765X. Volume 1 Issue 5 Ver. IV (Sep. - Ot.2016), PP 95-99 www.osrjournals.org Connetvty n Fuzzy Soft graph and ts Complement Shashkala

More information

Introduction. Basic idea of subdivision. History of subdivision schemes. Subdivision Schemes in Interactive Surface Design

Introduction. Basic idea of subdivision. History of subdivision schemes. Subdivision Schemes in Interactive Surface Design Subdvson Schemes n Interactve Surface Desgn Introducton Hstory of subdvson. What s subdvson? Why subdvson? Hstory of subdvson schemes Stage I: Create smooth curves from arbtrary mesh de Rham, 947. Chan,

More information

A Five-Point Subdivision Scheme with Two Parameters and a Four-Point Shape-Preserving Scheme

A Five-Point Subdivision Scheme with Two Parameters and a Four-Point Shape-Preserving Scheme Mathematcal and Computatonal Applcatons Artcle A Fve-Pont Subdvson Scheme wth Two Parameters and a Four-Pont Shape-Preservng Scheme Jeqng Tan,2, Bo Wang, * and Jun Sh School of Mathematcs, Hefe Unversty

More information

Matrix-Matrix Multiplication Using Systolic Array Architecture in Bluespec

Matrix-Matrix Multiplication Using Systolic Array Architecture in Bluespec Matrx-Matrx Multplaton Usng Systol Array Arhteture n Bluespe Team SegFault Chatanya Peddawad (EEB096), Aman Goel (EEB087), heera B (EEB090) Ot. 25, 205 Theoretal Bakground. Matrx-Matrx Multplaton on Hardware

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Modeling, Manipulating, and Visualizing Continuous Volumetric Data: A Novel Spline-based Approach

Modeling, Manipulating, and Visualizing Continuous Volumetric Data: A Novel Spline-based Approach Modelng, Manpulatng, and Vsualzng Contnuous Volumetrc Data: A Novel Splne-based Approach Jng Hua Center for Vsual Computng, Department of Computer Scence SUNY at Stony Brook Talk Outlne Introducton and

More information

Parallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016)

Parallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016) Technsche Unverstät München WSe 6/7 Insttut für Informatk Prof. Dr. Thomas Huckle Dpl.-Math. Benjamn Uekermann Parallel Numercs Exercse : Prevous Exam Questons Precondtonng & Iteratve Solvers (From 6)

More information

Barycentric Coordinates. From: Mean Value Coordinates for Closed Triangular Meshes by Ju et al.

Barycentric Coordinates. From: Mean Value Coordinates for Closed Triangular Meshes by Ju et al. Barycentrc Coordnates From: Mean Value Coordnates for Closed Trangular Meshes by Ju et al. Motvaton Data nterpolaton from the vertces of a boundary polygon to ts nteror Boundary value problems Shadng Space

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

Bilateral Mesh Denoising

Bilateral Mesh Denoising Outlne Blateral Meh Denong S. Flehman, I. Dror,, D. Cohen-Or Tel Avv Unverty Preented by Derek Bradley Motvaton Prevou ork Blateral Meh Denong Image Proeng Bakground Blateral Image Flterng Tranformng from

More information

Progressive scan conversion based on edge-dependent interpolation using fuzzy logic

Progressive scan conversion based on edge-dependent interpolation using fuzzy logic Progressve san onverson based on edge-dependent nterpolaton usng fuzzy log P. Brox brox@mse.nm.es I. Baturone lum@mse.nm.es Insttuto de Mroeletróna de Sevlla, Centro Naonal de Mroeletróna Avda. Rena Meredes

More information

A Geometric Approach for Multi-Degree Spline

A Geometric Approach for Multi-Degree Spline L X, Huang ZJ, Lu Z. A geometrc approach for mult-degree splne. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(4): 84 850 July 202. DOI 0.007/s390-02-268-2 A Geometrc Approach for Mult-Degree Splne Xn L

More information

Wavefront Reconstructor

Wavefront Reconstructor A Dstrbuted Smplex B-Splne Based Wavefront Reconstructor Coen de Vsser and Mchel Verhaegen 14-12-201212 2012 Delft Unversty of Technology Contents Introducton Wavefront reconstructon usng Smplex B-Splnes

More information

Simplification of 3D Meshes

Simplification of 3D Meshes Smplfcaton of 3D Meshes Addy Ngan /4/00 Outlne Motvaton Taxonomy of smplfcaton methods Hoppe et al, Mesh optmzaton Hoppe, Progressve meshes Smplfcaton of 3D Meshes 1 Motvaton Hgh detaled meshes becomng

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Extending the Concept of Fuzzy Rule Interpolation with the Interpolation of Fuzziness

Extending the Concept of Fuzzy Rule Interpolation with the Interpolation of Fuzziness WCCI 01 IEEE World Congress on Computatonal Intellgene June, 10-15, 01 - Brsbane, ustrala FUZZ IEEE Extendng the Conept of Fuzzy Rule Interpolaton wth the Interpolaton of Fuzzness Szlveszter Kovás Department

More information

Very simple computational domains can be discretized using boundary-fitted structured meshes (also called grids)

Very simple computational domains can be discretized using boundary-fitted structured meshes (also called grids) Structured meshes Very smple computatonal domans can be dscretzed usng boundary-ftted structured meshes (also called grds) The grd lnes of a Cartesan mesh are parallel to one another Structured meshes

More information

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

Analysis of ray stability and caustic formation in a layered moving fluid medium

Analysis of ray stability and caustic formation in a layered moving fluid medium Analyss of ray stablty and aust formaton n a layered movng flud medum Davd R. Bergman * Morrstown NJ Abstrat Caust formaton ours wthn a ray skeleton as optal or aoust felds propagate n a medum wth varable

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

such that is accepted of states in , where Finite Automata Lecture 2-1: Regular Languages be an FA. A string is the transition function,

such that is accepted of states in , where Finite Automata Lecture 2-1: Regular Languages be an FA. A string is the transition function, * Lecture - Regular Languages S Lecture - Fnte Automata where A fnte automaton s a -tuple s a fnte set called the states s a fnte set called the alphabet s the transton functon s the ntal state s the set

More information

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress Analyss of 3D Cracks n an Arbtrary Geometry wth Weld Resdual Stress Greg Thorwald, Ph.D. Ted L. Anderson, Ph.D. Structural Relablty Technology, Boulder, CO Abstract Materals contanng flaws lke nclusons

More information

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.

More information

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method Internatonal Journal of Computatonal and Appled Mathematcs. ISSN 89-4966 Volume, Number (07), pp. 33-4 Research Inda Publcatons http://www.rpublcaton.com An Accurate Evaluaton of Integrals n Convex and

More information

Scan Conversion & Shading

Scan Conversion & Shading Scan Converson & Shadng Thomas Funkhouser Prnceton Unversty C0S 426, Fall 1999 3D Renderng Ppelne (for drect llumnaton) 3D Prmtves 3D Modelng Coordnates Modelng Transformaton 3D World Coordnates Lghtng

More information

Multiblock method for database generation in finite element programs

Multiblock method for database generation in finite element programs Proc. of the 9th WSEAS Int. Conf. on Mathematcal Methods and Computatonal Technques n Electrcal Engneerng, Arcachon, October 13-15, 2007 53 Multblock method for database generaton n fnte element programs

More information

Network Topologies: Analysis And Simulations

Network Topologies: Analysis And Simulations Networ Topologes: Analyss And Smulatons MARJAN STERJEV and LJUPCO KOCAREV Insttute for Nonlnear Scence Unversty of Calforna San Dego, 95 Glman Drve, La Jolla, CA 993-4 USA Abstract:-In ths paper we present

More information

Research on Neural Network Model Based on Subtraction Clustering and Its Applications

Research on Neural Network Model Based on Subtraction Clustering and Its Applications Avalable onlne at www.senedret.om Physs Proeda 5 (01 ) 164 1647 01 Internatonal Conferene on Sold State Deves and Materals Sene Researh on Neural Networ Model Based on Subtraton Clusterng and Its Applatons

More information

Lecture 4: Principal components

Lecture 4: Principal components /3/6 Lecture 4: Prncpal components 3..6 Multvarate lnear regresson MLR s optmal for the estmaton data...but poor for handlng collnear data Covarance matrx s not nvertble (large condton number) Robustness

More information

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water.

REFRACTION. a. To study the refraction of light from plane surfaces. b. To determine the index of refraction for Acrylic and Water. Purpose Theory REFRACTION a. To study the refracton of lght from plane surfaces. b. To determne the ndex of refracton for Acrylc and Water. When a ray of lght passes from one medum nto another one of dfferent

More information

Scan Conversion & Shading

Scan Conversion & Shading 1 3D Renderng Ppelne (for drect llumnaton) 2 Scan Converson & Shadng Adam Fnkelsten Prnceton Unversty C0S 426, Fall 2001 3DPrmtves 3D Modelng Coordnates Modelng Transformaton 3D World Coordnates Lghtng

More information

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume

More information

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references

More information

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES

More information

A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams

A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams A MPAA-Based Iteratve Clusterng Algorthm Augmented by Nearest Neghbors Searh for Tme-Seres Data Streams Jessa Ln 1, Mha Vlahos 1, Eamonn Keogh 1, Dmtros Gunopulos 1, Janwe Lu 2, Shouan Yu 2, and Jan Le

More information

Structure from Motion

Structure from Motion Structure from Moton Structure from Moton For now, statc scene and movng camera Equvalentl, rgdl movng scene and statc camera Lmtng case of stereo wth man cameras Lmtng case of multvew camera calbraton

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

Face Recognition University at Buffalo CSE666 Lecture Slides Resources:

Face Recognition University at Buffalo CSE666 Lecture Slides Resources: Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural

More information

Link Graph Analysis for Adult Images Classification

Link Graph Analysis for Adult Images Classification Lnk Graph Analyss for Adult Images Classfaton Evgeny Khartonov Insttute of Physs and Tehnology, Yandex LLC 90, 6 Lev Tolstoy st., khartonov@yandex-team.ru Anton Slesarev Insttute of Physs and Tehnology,

More information

ABHELSINKI UNIVERSITY OF TECHNOLOGY Networking Laboratory

ABHELSINKI UNIVERSITY OF TECHNOLOGY Networking Laboratory ABHELSINKI UNIVERSITY OF TECHNOLOGY Networkng Laboratory Load Balanng n Cellular Networks Usng Frst Poly Iteraton Johan an Leeuwaarden Samul Aalto & Jorma Vrtamo Networkng Laboratory Helsnk Unersty of

More information

Surface and Volume Discretization of Functionally Based Heterogeneous Objects

Surface and Volume Discretization of Functionally Based Heterogeneous Objects Surfae and Volume Dsretzaton of Funtonally Based Heterogeneous Objets Elena Kartasheva Insttute for Mathematal Modelng Russan Aademy of Sene Mosow, Russa ekart@mamod.ru Oleg Fryaznov Insttute for Mathematal

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

Pattern Classification: An Improvement Using Combination of VQ and PCA Based Techniques

Pattern Classification: An Improvement Using Combination of VQ and PCA Based Techniques Ameran Journal of Appled Senes (0): 445-455, 005 ISSN 546-939 005 Sene Publatons Pattern Classfaton: An Improvement Usng Combnaton of VQ and PCA Based Tehnques Alok Sharma, Kuldp K. Palwal and Godfrey

More information

USING GRAPHING SKILLS

USING GRAPHING SKILLS Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp

More information

Inverse-Polar Ray Projection for Recovering Projective Transformations

Inverse-Polar Ray Projection for Recovering Projective Transformations nverse-polar Ray Projecton for Recoverng Projectve Transformatons Yun Zhang The Center for Advanced Computer Studes Unversty of Lousana at Lafayette yxz646@lousana.edu Henry Chu The Center for Advanced

More information

Color Texture Classification using Modified Local Binary Patterns based on Intensity and Color Information

Color Texture Classification using Modified Local Binary Patterns based on Intensity and Color Information Color Texture Classfaton usng Modfed Loal Bnary Patterns based on Intensty and Color Informaton Shvashankar S. Department of Computer Sene Karnatak Unversty, Dharwad-580003 Karnataka,Inda shvashankars@kud.a.n

More information

Feature Reduction and Selection

Feature Reduction and Selection Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components

More information

1 Linear and Nonlinear Subdivision Schemes in Geometric Modeling

1 Linear and Nonlinear Subdivision Schemes in Geometric Modeling 1 Lnear and Nonlnear Subdvson Schemes n Geometrc Modelng Nra dyn School of Mathematcal Scences Tel Avv Unversty Tel Avv, Israel e-mal: nradyn@post.tau.ac.l Abstract Subdvson schemes are effcent computatonal

More information

Research Article Quasi-Bézier Curves with Shape Parameters

Research Article Quasi-Bézier Curves with Shape Parameters Hndaw Publshng Corporaton Appled Mathematcs Volume 3, Artcle ID 739, 9 pages http://dxdoorg/55/3/739 Research Artcle Quas-Bézer Curves wth Shape Parameters Jun Chen Faculty of Scence, Nngbo Unversty of

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

Some Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.

Some Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated. Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,

More information

Interval uncertain optimization of structures using Chebyshev meta-models

Interval uncertain optimization of structures using Chebyshev meta-models 0 th World Congress on Strutural and Multdsplnary Optmzaton May 9-24, 203, Orlando, Florda, USA Interval unertan optmzaton of strutures usng Chebyshev meta-models Jngla Wu, Zhen Luo, Nong Zhang (Tmes New

More information

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

Interpolatory Subdivision Curves with Local Shape Control

Interpolatory Subdivision Curves with Local Shape Control Interpolatory Subdvson Curves wth Local Shape Control Carolna Beccar Dept. of Mathematcs Unversty of Padova va G.Belzon 7, 35131 Padova, Italy beccar@math.unpd.t Gulo Cascola Dept. of Mathematcs Unversty

More information

Topology Design using LS-TaSC Version 2 and LS-DYNA

Topology Design using LS-TaSC Version 2 and LS-DYNA Topology Desgn usng LS-TaSC Verson 2 and LS-DYNA Wllem Roux Lvermore Software Technology Corporaton, Lvermore, CA, USA Abstract Ths paper gves an overvew of LS-TaSC verson 2, a topology optmzaton tool

More information

Recognizing Faces. Outline

Recognizing Faces. Outline Recognzng Faces Drk Colbry Outlne Introducton and Motvaton Defnng a feature vector Prncpal Component Analyss Lnear Dscrmnate Analyss !"" #$""% http://www.nfotech.oulu.f/annual/2004 + &'()*) '+)* 2 ! &

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Implementing Lattice Boltzmann Computation on Graphics Hardware

Implementing Lattice Boltzmann Computation on Graphics Hardware To appear n The Vsual omputer Implementng Latte oltzmann omputaton on Graphs Hardware We L, Xaomng We, and re Kaufman enter for Vsual omputng (V) and epartment of omputer Sene State Unversty of New York

More information

Microprocessors and Microsystems

Microprocessors and Microsystems Mroproessors and Mrosystems 36 (2012) 96 109 Contents lsts avalable at SeneDret Mroproessors and Mrosystems journal homepage: www.elsever.om/loate/mpro Hardware aelerator arhteture for smultaneous short-read

More information

Lecture #15 Lecture Notes

Lecture #15 Lecture Notes Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal

More information

Cluster ( Vehicle Example. Cluster analysis ( Terminology. Vehicle Clusters. Why cluster?

Cluster (  Vehicle Example. Cluster analysis (  Terminology. Vehicle Clusters. Why cluster? Why luster? referene funton R R Although R and R both somewhat orrelated wth the referene funton, they are unorrelated wth eah other Cluster (www.m-w.om) A number of smlar ndvduals that our together as

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

Minimize Congestion for Random-Walks in Networks via Local Adaptive Congestion Control

Minimize Congestion for Random-Walks in Networks via Local Adaptive Congestion Control Journal of Communatons Vol. 11, No. 6, June 2016 Mnmze Congeston for Random-Walks n Networks va Loal Adaptve Congeston Control Yang Lu, Y Shen, and Le Dng College of Informaton Sene and Tehnology, Nanjng

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

An Improved Isogeometric Analysis Using the Lagrange Multiplier Method

An Improved Isogeometric Analysis Using the Lagrange Multiplier Method An Improved Isogeometrc Analyss Usng the Lagrange Mltpler Method N. Valzadeh 1, S. Sh. Ghorash 2, S. Mohammad 3, S. Shojaee 1, H. Ghasemzadeh 2 1 Department of Cvl Engneerng, Unversty of Kerman, Kerman,

More information

Optimal Quadrilateral Finite Elements on Polygonal Domains

Optimal Quadrilateral Finite Elements on Polygonal Domains J Sc Comput (2017) 70:60 84 DOI 10.1007/s10915-016-0242-5 Optmal Quadrlateral Fnte Elements on Polygonal Domans Hengguang L 1 Qnghu Zhang 2 Receved: 30 January 2015 / Revsed: 21 January 2016 / Accepted:

More information

Collision Detection. Overview. Efficient Collision Detection. Collision Detection with Rays: Example. C = nm + (n choose 2)

Collision Detection. Overview. Efficient Collision Detection. Collision Detection with Rays: Example. C = nm + (n choose 2) Overvew Collson detecton wth Rays Collson detecton usng BSP trees Herarchcal Collson Detecton OBB tree, k-dop tree algorthms Multple object CD system Collson Detecton Fundamental to graphcs, VR applcatons

More information

A Semi-parametric Approach for Analyzing Longitudinal Measurements with Non-ignorable Missingness Using Regression Spline

A Semi-parametric Approach for Analyzing Longitudinal Measurements with Non-ignorable Missingness Using Regression Spline Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 93-9466 Vol., Issue (June 5), pp. 95 - Applatons and Appled Mathemats: An Internatonal Journal (AAM) A Sem-parametr Approah for Analyzng Longtudnal

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

A Unified Subdivision Scheme for Polygonal Modeling

A Unified Subdivision Scheme for Polygonal Modeling EUROGRAPHICS 2 / A. Chalmers and T.-M. Rhyne (Guest Editors) Volume 2 (2), Number 3 A Unified Subdivision Sheme for Polygonal Modeling Jérôme Maillot Jos Stam Alias Wavefront Alias Wavefront 2 King St.

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Sorting. Sorted Original. index. index

Sorting. Sorted Original. index. index 1 Unt 16 Sortng 2 Sortng Sortng requres us to move data around wthn an array Allows users to see and organze data more effcently Behnd the scenes t allows more effectve searchng of data There are MANY

More information

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane An Approach n Colorng Sem-Regular Tlngs on the Hyperbolc Plane Ma Louse Antonette N De Las Peñas, mlp@mathscmathadmueduph Glenn R Lago, glago@yahoocom Math Department, Ateneo de Manla Unversty, Loyola

More information

Steganalysis of DCT-Embedding Based Adaptive Steganography and YASS

Steganalysis of DCT-Embedding Based Adaptive Steganography and YASS Steganalyss of DCT-Embeddng Based Adaptve Steganography and YASS Qngzhong Lu Department of Computer Sene Sam Houston State Unversty Huntsvlle, TX 77341, U.S.A. lu@shsu.edu ABSTRACT Reently well-desgned

More information

Outline. Type of Machine Learning. Examples of Application. Unsupervised Learning

Outline. Type of Machine Learning. Examples of Application. Unsupervised Learning Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton

More information

International Journal of Pharma and Bio Sciences HYBRID CLUSTERING ALGORITHM USING POSSIBILISTIC ROUGH C-MEANS ABSTRACT

International Journal of Pharma and Bio Sciences HYBRID CLUSTERING ALGORITHM USING POSSIBILISTIC ROUGH C-MEANS ABSTRACT Int J Pharm Bo S 205 Ot; 6(4): (B) 799-80 Researh Artle Botehnology Internatonal Journal of Pharma and Bo Senes ISSN 0975-6299 HYBRID CLUSTERING ALGORITHM USING POSSIBILISTIC ROUGH C-MEANS *ANURADHA J,

More information

Shape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram

Shape Representation Robust to the Sketching Order Using Distance Map and Direction Histogram Shape Representaton Robust to the Sketchng Order Usng Dstance Map and Drecton Hstogram Department of Computer Scence Yonse Unversty Kwon Yun CONTENTS Revew Topc Proposed Method System Overvew Sketch Normalzaton

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on

More information

Complex Filtering and Integration via Sampling

Complex Filtering and Integration via Sampling Overvew Complex Flterng and Integraton va Samplng Sgnal processng Sample then flter (remove alases) then resample onunform samplng: jtterng and Posson dsk Statstcs Monte Carlo ntegraton and probablty theory

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

Recap: rigid motions

Recap: rigid motions Forward and Invere Knemat Chapter 3 Had Morad (orgnal lde by Steve from Harvard) Reap: rgd moton Rgd moton a ombnaton of rotaton and tranlaton Defned by a rotaton matrx (R) and a dplaement vetor (d) the

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

Loop Transformations, Dependences, and Parallelization

Loop Transformations, Dependences, and Parallelization Loop Transformatons, Dependences, and Parallelzaton Announcements Mdterm s Frday from 3-4:15 n ths room Today Semester long project Data dependence recap Parallelsm and storage tradeoff Scalar expanson

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information