Global Illumination and Radiosity

Size: px
Start display at page:

Download "Global Illumination and Radiosity"

Transcription

1 Global Illumnaton and Radosty CS535 Danel lg. Alaga Department of Computer Scence Purdue Unversty

2 Recall: Lghtng and Shadng Lght sources Pont lght Models an omndrectonal lght source (e.g., a bulb) Drectonal lght Models an omndrectonal lght source at nfnty Spot lght Models a pont tlght wth drecton Lght model Ambent lght Dffuse reflecton Specular reflecton

3 Recall: Lghtng and Shadng Dffuse reflecton Lambertan model

4 Recall: Lghtng and Shadng Specular reflecton Phong model

5 Global Illumnaton Consder drect llumnaton as well as ndrect llumnaton; e.g. Reflectons, refractons, shadows, etc. Dffuse nter reflecton wth global llumnaton only dffuse nter-reflecton drect llumnaton

6 Global Illumnaton Consder drect llumnaton as well as ndrect llumnaton; e.g. Reflectons, refractons, shadows, etc. Dffuse nter reflecton, specular nter reflecton, etc.

7 Radosty Radosty, nspred by deas from heat transfer, s an applcaton of a fnte element method to solvng the renderng equaton for scenes wth purely dffuse surfaces (renderng equaton)

8 Radosty Equaton: (more detals dtl on the board )

9 Radosty Rest of Sldes Courtesy: Dr. Maro Costa Sousa Dept. of of CS U. Of Calgary

10 Radosty Calculatng the overall lght propagaton wthn a scene, for short global llumnaton s a very dffcult problem. Wth a standard dray tracng algorthm, ths s a very tme consumng task, snce a huge number of rays have to be shot.

11

12 Radosty For ths reason, the radosty method was nvented. The man dea of the method s to store llumnaton values on the surfaces of the objects, as the lght s propagated startng at the lght sources.

13 Ray Tracng

14 Radosty

15 Dffuse Interreflecton (radosty method)

16 Dffuse Interreflecton Surface = "dffuse reflector" of lght energy, means: any lght energy whch strkes the surface wll be reflected n all drectons, dependent only on the angle between the surface's normal and the ncomng lght vector (Lambert's law).

17 Dffuse Interreflecton The reflected lght energy often s colored, to some small extent, by the color of the surface from whch t was reflected. Ths reflecton of lght energy n an envronment produces a phenomenon known as "color bleedng," where a brghtly colored surface's color wll "bleed" onto adjacent surfaces.

18 Dffuse Interreflecton The reflected lght energy often s colored, to some small extent, by the color of the surface from whch t was reflected. C l bl d Color bleedng, as both the red and blue walls "bleed" ther color onto the whte walls, celng and floor.

19 Radosty (Thermal Heat Transfer) The "radosty" method has ts bass n the feld of thermal heat transfer. Heat ttransfer theory descrbes radaton as the transfer of energy from a surface when that surface has been thermally excted.

20 Ths encompasses both surfaces whch are basc emtters of energy, as wth lght sources, and surfaces whch receve energy from other surfaces and thus have energy to transfer. Ths "thermal radaton" theory can be used to descrbe the transfer of many knds of energy between surfaces, ncludng lght energy.

21 Radosty (Computer Graphcs) Assumpton #1: surfaces are dffuse emtters and reflectors of energy, emttngand reflectng energy unformly over ther entre area. Assumpton #2: an equlbrum soluton can be reached; that all of the energy n an envronment s accounted for, through absorpton and reflecton. Also vewpont ndependent: the soluton wll be the same regardless of the vewpont of the mage.

22 The Radosty Equaton The "radosty equaton" descrbes the amount of energy whch hcan be emtted from a surface, as the sum of the energy nherent n the surface (a lght source, for example) and the energy whch strkes the surface, beng emtted from someother other surface. The energy whch leaves a surface (surface "j") and strkes anothers rface(surface "") satten attenuated ated by two ofactors: the "form factor" between surfaces "" and "j", whch accounts for the physcal relatonshp between the two surfaces the reflectvty of surface ", whch wll absorb a certan percentage of lght energy whch strkes the surface.

23 The Radosty Equaton B = E + ρ B F j j Radosty of surface Emssvty of surface Form Factor of surface j relatve to surface Radosty of surface j Reflectvty of surface wll absorb a certan percentage of lght energy whch strkes Surface Surface j accounts for the physcal relatonshp between the two surfaces

24 The Radosty Equaton B = E + ρ B F j j Energy reachng surface from other surfaces Surface j Surface

25 The Radosty Equaton B = E + ρ B F j j Energy reachng surface from other surfaces Form Factor of surface j relatve to surface Radosty of surface j accounts for the physcal Surface j relatonshp between the two surfaces Surface

26 The Radosty Equaton B = E + ρ B F j j Energy emtted by surface Surface j Surface

27 The Radosty Equaton B = E + ρ B F j j Energy reflected by surface Surface j Surface

28 The Radosty Equaton Energy reflected by surface B = E + ρ B F j j Form Factor of Reflectvty of surface surface j relatve to surface Energy reflected by Radosty of surface j surface = Reflectvty of surface Form * Factor Reflectvty Energy reachng accounts for surface wll absorb a from other surfaces Surface j the physcal certan relatonshp percentage of between the lght energy two Surface whch strkes surfaces

29 Radosty Classc radosty = fnte element e e method Assumptons Dffuse reflectance Usually polygonal surfaces Advantages Softshadows and ndrect lghtng Vew ndependent soluton Precompute for a set of lght sources Useful for walkthroughs

30 Classc Radosty Algorthm Mesh Surfaces nto Elements Compute Form Factors Between B t Elements Solve Lnear System for Radostes Reconstruct and Dsplay Soluton

31 Classc Radosty Algorthm Mesh Surfaces nto Elements Compute Form Factors Between B t Elements Solve Lnear System for Radostes Reconstruct and Dsplay Soluton

32 The Form Factor: the fracton of energy leavng one surface that reaches another surface It s a purely geometrc relatonshp, ndependent of vewpont or surface attrbutes Surface j Surface

33 Between dfferental areas, the form factor equals: dfferental l area of surface I, j FdA da j j = cos θ cos θ π r 2 j angle between Normal and r angle g between Normal j and r Surface j θ j da j θ r vector from da to da j da Surface

34 Between dfferental areas, the form factor equals: FdA da j j = cosθ cosθ π r 2 j F j The overall form factor between and j s found by ntegratng 1 cosθ cosθ = 2 A A A π r j j da da j Surface j θ j da j θ r da Surface

35 Next Step: Learn ways of computng form factors Recall the Radosty Equaton: B ρ = E + ρ B F j j The F j are the form factors Form factors ndependent of radostes (depend only on scene geometry)

36 Form Factors n (More) Detal F j cos cos = A 2 π r 1 θ θ A A j j da da j F j 1 cosθ cosθ j A π r = 2 A A j π V j da da j where V j s the vsblty (0 or 1)

37 We have two ntegrals to compute: F j = 1 cos θ cos θ j 2 A πr A A j V j da j da Area ntegral Area ntegral over surface over surface j Surface j θ j da j θ r da Surface

38 The Nusselt Analog Dfferentaton of the basc form factor equaton s dffcult even for smple surfaces! Nusselt developed a geometrc analog whch allows the smple and accurate calculaton of the form factor between a surface and a pont on a second surface.

39 The Nusselt Analog The "Nusselt analog" nvolves placng a hemsphercal projecton body, wth unt radus, at a pont on a surface. The second surface s sphercally projected onto the projecton body, then cylndrcally projected onto the base of the hemsphere. h The form factor s then the area projected on the The form factor s, then, the area projected on the base of the hemsphere dvded by the area of the base of the hemsphere.

40 Numercal Integraton: The Nusselt Analog Ths gves the form factor F daaj A j da

41 The Nusselt Analog q r 1. Project A j along ts normal: A cos q A j cos q j 2. Project result on sphere: A j cos q j / r 2 3. Project result on unt crcle: A j cos q j cos q /r 2 area A j 4. Dvde by unt crcle area: A j cos q j cos q / pr 2 q j 5. Integrate for all ponts on A j : F da A A j cos θ cos θ πr j 2 sphere projecton A j cos q j /r 2 = j V j da j unt crcle area p second projecton A j cos q j cos q /r 2

42 Method 1: Hemcube Approxmaton of Nusselt ss analog between a pont da and a polygon A j Polygonal Area (A j ) Infntesmal Area (da )

43 Hemcube For convenence, a cube 1 unt hgh wth a top face 2 x 2 s used. Sde faces are 2 wde by 1 hgh. D d l f h b Decde on a resoluton for the cube. Say 512 by 512 for the top.

44 The Hemcube In Acton

45 The Hemcube In Acton

46 The Hemcube In Acton Ths llustraton demonstrates the calculaton of form factors between a partcular surface on the wall of a room and several surfaces of objects n the room.

47 Compute the form factors from a pont on a surface to all other surfaces by: Projectng allother surfaces onto the hemcube Storng, at each dscrete area, the dentfyng ndex of the surface that s closest to the pont.

48 Dscrete areas wth the ndces of the surfaces whch are ultmately vsble to the pont. From there the form factors between the pont and the surfaces are calculated. For greater accuracy, a large surface would typcally be broken nto a set of small surfaces before any form factor calculaton s performed.

49 Hemcube Method 1. Scan convert all scene objects onto hemcube s 5 faces 2. Use Z buffer to determne vsblty term 3. Sum up the delta form factors of the hemcube cells covered by scanned objects 4. Gvesform factors from hemcube s base to all elements, e.e. F daaj for gven and all j

50 Hemcube Algorthms Advantages + Frst practcal method + Use exstng renderng systems; Hardware + Computes row of form factors n O(n) Dsadvantages Computes dfferental fnte form factor Alasng errors due to samplng Randomly rotate/shear tt/h hemcube Proxmty errors Vsblty errors Expensve to compute a sngle form factor

51 Hemcube Problem: Alasng

52 Method 2: Area Samplng 1. Subdvde de A j nto small peces da j A j 2. For all da j cast ray daj-daj to determne V j f vsble compute F dadaj cosθ cosθ j FdA da = VjdA j 2 πr sum up F daaj += F dadaj j da ray da j 3. We have now F daaj

53 Summary Several ways to fnd form factors Hemcube was orgnal method + Hardware acceleraton +GvesF daaj for all j n one pass Alasng Area samplng methods now preferred Slower than hemcube As accurate as desred snce adaptve

54 Next We have the form factors How do we fnd the radosty soluton for the scene? The "Full Matrx" Radosty Algorthm Gatherng & Shootng Progressve Radosty Meshng

55 Classc Radosty Algorthm Mesh Surfaces nto Elements Compute Form Factors Between B t Elements Solve Lnear System for Radostes Reconstruct and Dsplay Soluton

56 Recall The Radosty Equaton B = E + ρ B F j j Radosty of surface Emssvty of surface Form Factor of surface j relatve to surface Radosty of surface j Reflectvty of surface wll absorb a certan percentage of lght energy whch strkes Surface Surface j accounts for the physcal relatonshp between the two surfaces

57 Radosty Matrx E + = n j j j A B F A E A B ρ B j= j j j 1 n j j j F A F A = = + = n j j j B F E B 1 ρ n E B F F F 1 ρ ρ ρ j j j E B F B = =1 ρ = n n E E B B F F F F F F M M M O M M L L ρ ρ ρ ρ ρ ρ n n nn n n n n n E B F F F M M L M O M M ρ ρ ρ

58 Radosty Matrx The "full matrx" radosty soluton calculates the form factors between each par of surfaces n the envronment, then forms a seres of smultaneous lnear equatons. n n E E B B F F F F F F L L ρ ρ ρ ρ ρ ρ = n n nn n n n n n n E B F F F M M L M O M M ρ ρ ρ Ths matrx equaton s solved for the "B" values, whch can be used as the fnal ntensty (or color) value of each surface.

59 Radosty Matrx Ths method produces a complete soluton, at the substantal cost of frst calculatng form factors between each par of surfaces and then the soluton of the matrx equaton. Each of these steps can be qute expensve f the number of surfaces s large: complex envronments typcally have upwards of ten thousand surfaces, and envronments wth one mllon surfaces are not uncommon. Ths leads to substantal costs not only n computaton tme but n storage.

60 Next We have the form factors How do we fnd the radosty soluton for the scene? The "Full Matrx" Radosty Algorthm Gatherng & Shootng Progressve Radosty Meshng

61 Solve [F][B] = [E] Drect methods: O(n 3 ) Gaussan elmnaton Goral, Torrance, Greenberg, Battale, 1984 Iteratve t methods: O(n 2 ) Energy conservaton dagonally domnant teraton converges Gauss Sedel, Jacob: Gatherng Nshta, Nakamae, 1985 Cohen, Greenberg, 1985 Southwell: Shootng Cohen, Chen, Wallace, Greenberg, 1988

62 Gatherng Ina sense, the lght leavng patch s determned by gatherng n the lght from the rest of the envronment B B = due E to + ρ B j n j= 1 B j = ρ B F j j F j B = E + n ( ρ ) Fj j= 1 B j

63 Gatherng Gatherng lght through a hem cube allows one patch radosty to be updated. B = E + n ( ρ ) Fj j= 1 B j

64 Gatherng

65 Successve Approxmaton

66 Shootng Shootng lght through a sngle hem cube allows the whole envronment's radosty values to be updated smultaneously. For all j B j = B j + B ( ρ E ) j j whereh F = j F j A A j

67 Shootng

68 Progressve Radosty

69 Next We have the form factors How do we fnd the radosty soluton for the scene? The "Full Matrx" Radosty Algorthm Gatherng & Shootng Progressve Radosty Meshng

70 Accuracy

71 Artfacts

72 Increasng Resoluton

73 Adaptve Meshng

74 Classc Radosty Algorthm Mesh Surfaces nto Elements Compute Form Factors Between B t Elements Solve Lnear System for Radostes Reconstruct and Dsplay Soluton

75 Some Radosty Results

76 The Cornell Box Ths s the orgnal Cornell box, as smulated by Cndy M. Goral, Kenneth E. Torrance, and Donald P. Greenberg for the 1984 paper Modelng the nteracton of Lght Between Dffuse Surfaces, Computer Graphcs (SIGGRAPH '84 Proceedngs), Vol. 18, No. 3, July 1984, pp Because form factors were computed analytcally, no occludng objects were ncluded nsde the box.

77 The Cornell Box Ths smulaton of the Cornell box was done by Mchael F. Cohen and Donald P. Greenberg for the 1985 paper The Hem Cube, A Radosty Soluton for Complex Envronments, Vol. 19, No. 3, July 1985, pp The hem cube allowed form factors to be calculated usng scan converson algorthms (whch were avalable n hardware), and made t possble to calculate shadows from occludng objects.

78

79

80

81

82 Dscontnuty Meshng Dan Lschnsk, Flppo Tamper and Donald P. Greenberg created ths mage for the 1992 paper Dscontnuty Meshng for Accurate Radosty. It depcts a scene that represents a pathologcal case for tradtonal radosty mages, many small shadow castng detals. Notce, n partcular, the shadows cast by the wndows, and the slats n the char.

83

84 Opera Lghtng Ths scene from La Boheme demonstrates the use of focused lghtng and angular projecton of predstorted mages for the background. It was rendered by Jule O'B. Dorsey, Francos X. Sllon, and Donald P. Greenberg for the 1991 paper Desgn and Smulaton of Opera Lghtng and Projecton Effects.

85

86 Radosty Factory These two mages were rendered by Mchael F. Cohen, Shenchang Erc Chen, John R. Wallace and Donald P. Greenberg for the 1988 paper A Progressve Refnement Approach to Fast Radosty Image Generaton. The factory model contans 30,000 patches, and was the most complex radosty soluton computed at that tme. The radosty soluton took approxmately 5 hours for 2,000 shots, and the mage generaton requred 190 hours; each on a VAX8700.

87

88 Museum Most of the llumnaton that comes nto ths smulated museum arrves va the baffles on the celng. As the progressve radosty soluton executed, users could wtness each of the bffl baffles b beng llumnated from above, and then reflectng some of ths lght to the bottom of an adjacent baffle. A porton of ths reflected lght was eventually bounced down nto the room. The mage appeared on the proceedngs cover of SIGGRAPH 1988.

89

90

91

92

93

94

95

Global Illumination and Radiosity

Global Illumination and Radiosity Global Illumnaton and Radosty CS535 Danel G. Alaga Department of Computer Scence Purdue Unversty Recall: Lghtng and Shadng Lght sources Pont lght Models an omndrectonal lght source (e.g., a bulb) Drectonal

More information

Global Illumination and Radiosity

Global Illumination and Radiosity Global Illumnaton and Radosty CS535 Danel G. Alaga Department of Computer Scence Purdue Unversty Recall: Lghtng and Shadng Lght sources Pont lght Models an omndrectonal lght source (e.g., a bulb) Drectonal

More information

Global Illumination: Radiosity

Global Illumination: Radiosity Last Tme? Global Illumnaton: Radosty Planar Shadows Shadow Maps An early applcaton of radatve heat transfer n stables. Projectve Texture Shadows (Texture Mappng) Shadow Volumes (Stencl Buffer) Schedule

More information

Computer Graphics. Jeng-Sheng Yeh 葉正聖 Ming Chuan University (modified from Bing-Yu Chen s slides)

Computer Graphics. Jeng-Sheng Yeh 葉正聖 Ming Chuan University (modified from Bing-Yu Chen s slides) Computer Graphcs Jeng-Sheng Yeh 葉正聖 Mng Chuan Unversty (modfed from Bng-Yu Chen s sldes) llumnaton and Shadng llumnaton Models Shadng Models for Polygons Surface Detal Shadows Transparency Global llumnaton

More information

Global Illumination and Radiosity

Global Illumination and Radiosity Global Illumination and Radiosity CS434 Daniel G. Aliaga Department of Computer Science Purdue University Recall: Lighting and Shading Light sources Point light Models an omnidirectional light source (e.g.,

More information

Introduction to Radiosity

Introduction to Radiosity EECS 487: Interactve Computer Graphcs EECS 487: Interactve Computer Graphcs Renderng a Scene Introducton to Radosty John. Hughes and ndres van Dam rown Unversty The scene conssts of a geometrc arrangement

More information

Form-factors Josef Pelikán CGG MFF UK Praha.

Form-factors Josef Pelikán CGG MFF UK Praha. Form-factors 1996-2016 Josef Pelkán CGG MFF UK Praha pepca@cgg.mff.cun.cz http://cgg.mff.cun.cz/~pepca/ FormFactor 2016 Josef Pelkán, http://cgg.mff.cun.cz/~pepca 1 / 23 Form-factor F It ndcates the proporton

More information

Real-time. Shading of Folded Surfaces

Real-time. Shading of Folded Surfaces Rhensche Fredrch-Wlhelms-Unverstät Bonn Insttute of Computer Scence II Computer Graphcs Real-tme Shadng of Folded Surfaces B. Ganster, R. Klen, M. Sattler, R. Sarlette Motvaton http://www www.vrtualtryon.de

More information

Color in OpenGL Polygonal Shading Light Source in OpenGL Material Properties Normal Vectors Phong model

Color in OpenGL Polygonal Shading Light Source in OpenGL Material Properties Normal Vectors Phong model Color n OpenGL Polygonal Shadng Lght Source n OpenGL Materal Propertes Normal Vectors Phong model 2 We know how to rasterze - Gven a 3D trangle and a 3D vewpont, we know whch pxels represent the trangle

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

Global Illumination. Computer Graphics COMP 770 (236) Spring Instructor: Brandon Lloyd 3/26/07 1

Global Illumination. Computer Graphics COMP 770 (236) Spring Instructor: Brandon Lloyd 3/26/07 1 Global Illumnaton Computer Graphcs COMP 770 (236) Sprng 2007 Instructor: Brandon Lloyd 3/26/07 1 From last tme Robustness ssues Code structure Optmzatons Acceleraton structures Dstrbuton ray tracng ant-alasng

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

Discussion. History and Outline. Smoothness of Indirect Lighting. Irradiance Caching. Irradiance Calculation. Advanced Computer Graphics (Fall 2009)

Discussion. History and Outline. Smoothness of Indirect Lighting. Irradiance Caching. Irradiance Calculation. Advanced Computer Graphics (Fall 2009) Advanced Computer Graphcs (Fall 2009 CS 29, Renderng Lecture 6: Recent Advances n Monte Carlo Offlne Renderng Rav Ramamoorth http://nst.eecs.berkeley.edu/~cs29-13/fa09 Dscusson Problems dfferent over years.

More information

Discussion. History and Outline. Smoothness of Indirect Lighting. Irradiance Calculation. Irradiance Caching. Advanced Computer Graphics (Fall 2009)

Discussion. History and Outline. Smoothness of Indirect Lighting. Irradiance Calculation. Irradiance Caching. Advanced Computer Graphics (Fall 2009) Advanced Computer Graphcs (Fall 2009 CS 283, Lecture 13: Recent Advances n Monte Carlo Offlne Renderng Rav Ramamoorth http://nst.eecs.berkeley.edu/~cs283/fa10 Dscusson Problems dfferent over years. Intally,

More information

Monte Carlo 1: Integration

Monte Carlo 1: Integration Monte Carlo : Integraton Prevous lecture: Analytcal llumnaton formula Ths lecture: Monte Carlo Integraton Revew random varables and probablty Samplng from dstrbutons Samplng from shapes Numercal calculaton

More information

Monte Carlo Rendering

Monte Carlo Rendering Monte Carlo Renderng Last Tme? Modern Graphcs Hardware Cg Programmng Language Gouraud Shadng vs. Phong Normal Interpolaton Bump, Dsplacement, & Envronment Mappng Cg Examples G P R T F P D Today Does Ray

More information

Monte Carlo 1: Integration

Monte Carlo 1: Integration Monte Carlo : Integraton Prevous lecture: Analytcal llumnaton formula Ths lecture: Monte Carlo Integraton Revew random varables and probablty Samplng from dstrbutons Samplng from shapes Numercal calculaton

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

Surface Mapping One. CS7GV3 Real-time Rendering

Surface Mapping One. CS7GV3 Real-time Rendering Surface Mappng One CS7GV3 Real-tme Renderng Textures Add complexty to scenes wthout addtonal geometry Textures store ths nformaton, can be any dmenson Many dfferent types: Dffuse most common Ambent, specular,

More information

Some Tutorial about the Project. Computer Graphics

Some Tutorial about the Project. Computer Graphics Some Tutoral about the Project Lecture 6 Rastersaton, Antalasng, Texture Mappng, I have already covered all the topcs needed to fnsh the 1 st practcal Today, I wll brefly explan how to start workng on

More information

Diffuse and specular interreflections with classical, deterministic ray tracing

Diffuse and specular interreflections with classical, deterministic ray tracing Dffuse and specular nterreflectons wth classcal, determnstc ray tracng Gergely Vass gergely_vass@sggraph.org Dept. of Control Engneerng and Informaton Technology Techncal Unversty of Budapest Budapest,

More information

Comparison of calculation methods and models in software for computer graphics and radiative heat exchange

Comparison of calculation methods and models in software for computer graphics and radiative heat exchange Comparson of calculaton methods and models n software for computer graphcs and radatve heat exchange Insttute of Electrcal and Electroncs Engneerng Poznan Unversty of Technology ul. Potrowo 3A, 60-950

More information

Physics 132 4/24/17. April 24, 2017 Physics 132 Prof. E. F. Redish. Outline

Physics 132 4/24/17. April 24, 2017 Physics 132 Prof. E. F. Redish. Outline Aprl 24, 2017 Physcs 132 Prof. E. F. Redsh Theme Musc: Justn Tmberlake Mrrors Cartoon: Gary Larson The Far Sde 1 Outlne Images produced by a curved mrror Image equatons for a curved mrror Lght n dense

More information

Consistent Illumination within Optical See-Through Augmented Environments

Consistent Illumination within Optical See-Through Augmented Environments Consstent Illumnaton wthn Optcal See-Through Augmented Envronments Olver Bmber, Anselm Grundhöfer, Gordon Wetzsten and Sebastan Knödel Bauhaus Unversty Bauhausstraße 11, 99423 Wemar, Germany, {olver.bmber,

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

An exhaustive error-bounding algorithm for hierarchical radiosity

An exhaustive error-bounding algorithm for hierarchical radiosity An exhaustve error-boundng algorthm for herarchcal radosty Ncolas Holzschuch, Franços X. Sllon To cte ths verson: Ncolas Holzschuch, Franços X. Sllon. An exhaustve error-boundng algorthm for herarchcal

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

Fast, Arbitrary BRDF Shading for Low-Frequency Lighting Using Spherical Harmonics

Fast, Arbitrary BRDF Shading for Low-Frequency Lighting Using Spherical Harmonics Thrteenth Eurographcs Workshop on Renderng (2002) P. Debevec and S. Gbson (Edtors) Fast, Arbtrary BRDF Shadng for Low-Frequency Lghtng Usng Sphercal Harmoncs Jan Kautz 1, Peter-Pke Sloan 2 and John Snyder

More information

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples

More information

Lighting. Dr. Scott Schaefer

Lighting. Dr. Scott Schaefer Lghtng Dr. Scott Schaefer 1 Lghtng/Illumnaton Color s a functon of how lght reflects from surfaces to the eye Global llumnaton accounts for lght from all sources as t s transmtted throughout the envronment

More information

Monte Carlo Integration

Monte Carlo Integration Introducton Monte Carlo Integraton Dgtal Image Synthess Yung-Yu Chuang 11/9/005 The ntegral equatons generally don t have analytc solutons, so we must turn to numercal methods. L ( o p,ωo) = L e ( p,ωo)

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

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

Plane Sampling for Light Paths from the Environment Map

Plane Sampling for Light Paths from the Environment Map jgt 2009/5/27 16:42 page 1 #1 Vol. [VOL], No. [ISS]: 1 6 Plane Samplng for Lght Paths from the Envronment Map Holger Dammertz and Johannes Hanka Ulm Unversty Abstract. We present a method to start lght

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

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

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

Fast and accurate view factor generation

Fast and accurate view factor generation FICUP An Internatonal Conference on Urban Physcs B. Beckers, T. Pco, S. Jmenez (Eds.) Quto Galápagos, Ecuador, 6 30 September 016 Fast and accurate vew factor generaton Benot Beckers 1 and Perre Beckers

More information

Computer graphics III Light reflection, BRDF. Jaroslav Křivánek, MFF UK

Computer graphics III Light reflection, BRDF. Jaroslav Křivánek, MFF UK Computer graphcs III Lght reflecton, BRDF Jaroslav Křvánek, MFF UK Jaroslav.Krvanek@mff.cun.cz Basc radometrc quanttes Image: Wojcech Jarosz CG III (NPGR010) - J. Křvánek 2015 Interacton of lght wth a

More information

Surface Integrators. Digital Image Synthesis Yung-Yu Chuang 12/20/2007

Surface Integrators. Digital Image Synthesis Yung-Yu Chuang 12/20/2007 Surface Integrators Dgtal Image Synthess Yung-Yu Chuang 12/20/2007 wth sldes by Peter Shrley, Pat Hanrahan, Henrk Jensen, Maro Costa Sousa and Torsten Moller Drect lghtng va Monte Carlo ntegraton dffuse

More information

Kiran Joy, International Journal of Advanced Engineering Technology E-ISSN

Kiran Joy, International Journal of Advanced Engineering Technology E-ISSN Kran oy, nternatonal ournal of Advanced Engneerng Technology E-SS 0976-3945 nt Adv Engg Tech/Vol. V/ssue /Aprl-une,04/9-95 Research Paper DETERMATO O RADATVE VEW ACTOR WTOUT COSDERG TE SADOWG EECT Kran

More information

Motivation. Motivation. Monte Carlo. Example: Soft Shadows. Outline. Monte Carlo Algorithms. Advanced Computer Graphics (Fall 2009)

Motivation. Motivation. Monte Carlo. Example: Soft Shadows. Outline. Monte Carlo Algorithms. Advanced Computer Graphics (Fall 2009) Advanced Comuter Grahcs Fall 29 CS 294, Renderng Lecture 4: Monte Carlo Integraton Rav Ramamoorth htt://nst.eecs.berkeley.edu/~cs294-3/a9 Motvaton Renderng = ntegraton Relectance equaton: Integrate over

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

Electrical analysis of light-weight, triangular weave reflector antennas

Electrical analysis of light-weight, triangular weave reflector antennas Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna

More information

Interactive Virtual Relighting of Real Scenes

Interactive Virtual Relighting of Real Scenes Frst submtted: October 1998 (#846). Edtor/revewers please consult accompanyng document wth detaled responses to revewer comments. Interactve Vrtual Relghtng of Real Scenes Célne Loscos, George Drettaks,

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

Short Papers. Toward Accurate Recovery of Shape from Shading Under Diffuse Lighting 1 INTRODUCTION 2 PROBLEM FORMULATION

Short Papers. Toward Accurate Recovery of Shape from Shading Under Diffuse Lighting 1 INTRODUCTION 2 PROBLEM FORMULATION 1020 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 19, NO. 9, SEPTEMBER 1997 Short Papers Toward Accurate Recovery of Shape from Shadng Under Dffuse Lghtng A. James Stewart and Mchael

More information

Robust Soft Shadow Mapping with Depth Peeling

Robust Soft Shadow Mapping with Depth Peeling 1 Robust Soft Shadow Mappng wth Depth Peelng Lous Bavol, Steven P. Callahan, Cláudo T. Slva UUSCI-2006-028 Scentfc Computng and Imagng Insttute Unversty of Utah Salt Lake Cty, UT 84112 USA August 11, 2006

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

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

Computer Sciences Department

Computer Sciences Department Computer Scences Department Populaton Monte Carlo Path Tracng Yu-Ch La Charles Dyer Techncal Report #1614 September 2007 Populaton Monte Carlo Path Tracng Yu-Ch La Unversty of Wsconsn at Madson Graphcs-Vson

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

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

Robust Soft Shadow Mapping with Backprojection and Depth Peeling

Robust Soft Shadow Mapping with Backprojection and Depth Peeling paper 2008/3/20 15:47 page 19 #1 Vol. 13, No. 1: 19 29 Robust Soft Shadow Mappng wth Backprojecton and Depth Peelng Lous Bavol, Steven P. Callahan, and Claudo T. Slva Scentfc Computng and Imagng Insttute,

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

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

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

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

Computer models of motion: Iterative calculations

Computer models of motion: Iterative calculations Computer models o moton: Iteratve calculatons OBJECTIVES In ths actvty you wll learn how to: Create 3D box objects Update the poston o an object teratvely (repeatedly) to anmate ts moton Update the momentum

More information

PBRT core. Announcements. pbrt. pbrt plug-ins

PBRT core. Announcements. pbrt. pbrt plug-ins Announcements PBRT core Dgtal Image Synthess Yung-Yu Chuang 9/27/2007 Please subscrbe the malng lst. Wndows complaton Debuggng n Wndows Doxygen (onlne, download or doxygen by yourself) HW#1 wll be assgned

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

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

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

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

A Range Image Refinement Technique for Multi-view 3D Model Reconstruction

A Range Image Refinement Technique for Multi-view 3D Model Reconstruction A Range Image Refnement Technque for Mult-vew 3D Model Reconstructon Soon-Yong Park and Mural Subbarao Electrcal and Computer Engneerng State Unversty of New York at Stony Brook, USA E-mal: parksy@ece.sunysb.edu

More information

Dependence of the Color Rendering Index on the Luminance of Light Sources and Munsell Samples

Dependence of the Color Rendering Index on the Luminance of Light Sources and Munsell Samples Australan Journal of Basc and Appled Scences, 4(10): 4609-4613, 2010 ISSN 1991-8178 Dependence of the Color Renderng Index on the Lumnance of Lght Sources and Munsell Samples 1 A. EL-Bally (Physcs Department),

More information

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION?

S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? Célne GALLET ENSICA 1 place Emle Bloun 31056 TOULOUSE CEDEX e-mal :cgallet@ensca.fr Jean Luc LACOME DYNALIS Immeuble AEROPOLE - Bat 1 5, Avenue Albert

More information

AVO Modeling of Monochromatic Spherical Waves: Comparison to Band-Limited Waves

AVO Modeling of Monochromatic Spherical Waves: Comparison to Band-Limited Waves AVO Modelng of Monochromatc Sphercal Waves: Comparson to Band-Lmted Waves Charles Ursenbach* Unversty of Calgary, Calgary, AB, Canada ursenbach@crewes.org and Arnm Haase Unversty of Calgary, Calgary, AB,

More information

DIFFRACTION SHADING MODELS FOR IRIDESCENT SURFACES

DIFFRACTION SHADING MODELS FOR IRIDESCENT SURFACES DIFFRACTION SHADING MODELS FOR IRIDESCENT SURFACES Emmanuel Agu Department of Computer Scence Worcester Polytechnc Insttute, Worcester, MA 01609, USA emmanuel@cs.wp.edu Francs S.Hll Jr Department of Electrcal

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

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005 Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed

More information

2.2 Photometric Image Formation

2.2 Photometric Image Formation 2.2 Photometrc Image Formaton mage plane n source sensor plane optcs!1 Illumnaton Computer son ory s ten deeloped wth assumpton a pont source at nfnty. But een sun has a fnte extent (about 0.5 deg sual

More information

Radiosity. Early Radiosity. Page 1

Radiosity. Early Radiosity. Page 1 Page 1 Radiosity Classic radiosity = finite element method Assumptions Diffuse reflectance Usually polygonal surfaces Advantages Soft shadows and indirect lighting View independent solution Precompute

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

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

AP PHYSICS B 2008 SCORING GUIDELINES

AP PHYSICS B 2008 SCORING GUIDELINES AP PHYSICS B 2008 SCORING GUIDELINES General Notes About 2008 AP Physcs Scorng Gudelnes 1. The solutons contan the most common method of solvng the free-response questons and the allocaton of ponts for

More information

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process

More information

Topic 13: Radiometry. The Basic Light Transport Path

Topic 13: Radiometry. The Basic Light Transport Path Topc 3: Raometry The bg pcture Measurng lght comng from a lght source Measurng lght fallng onto a patch: Irraance Measurng lght leavng a patch: Raance The Lght Transport Cycle The BrecAonal Reflectance

More information

Object Recognition Based on Photometric Alignment Using Random Sample Consensus

Object Recognition Based on Photometric Alignment Using Random Sample Consensus Vol. 44 No. SIG 9(CVIM 7) July 2003 3 attached shadow photometrc algnment RANSAC RANdom SAmple Consensus Yale Face Database B RANSAC Object Recognton Based on Photometrc Algnment Usng Random Sample Consensus

More information

Interactive Rendering of Translucent Objects

Interactive Rendering of Translucent Objects Interactve Renderng of Translucent Objects Hendrk Lensch Mchael Goesele Phlppe Bekaert Jan Kautz Marcus Magnor Jochen Lang Hans-Peter Sedel 2003 Presented By: Mark Rubelmann Outlne Motvaton Background

More information

The Shortest Path of Touring Lines given in the Plane

The Shortest Path of Touring Lines given in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

Fast Computation of Shortest Path for Visiting Segments in the Plane

Fast Computation of Shortest Path for Visiting Segments in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang

More information

Simulation and Animation of Fire

Simulation and Animation of Fire Smulaton and Anmaton of Fre Overvew Presentaton n Semnar on Motvaton Methods for smulaton of fre Physcally-based Methods for 3D-Games and Medcal Applcatons Dens Stenemann partcle-based flud-based flame-based

More information

PHYS 219 Spring semester Lecture 20: Reflection of Electromagnetic Radiation: Mirrors and Images Formed by Mirrors

PHYS 219 Spring semester Lecture 20: Reflection of Electromagnetic Radiation: Mirrors and Images Formed by Mirrors PHYS 219 Sprng semester 2014 Lecture 20: eflecton of Electromagnetc adaton: Mrrors and Images Formed by Mrrors on efenberger Brck Nanotechnology Center Purdue Unversty Lecture 20 1 evew: Snapshot of an

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL)

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL) Crcut Analyss I (ENG 405) Chapter Method of Analyss Nodal(KCL) and Mesh(KVL) Nodal Analyss If nstead of focusng on the oltages of the crcut elements, one looks at the oltages at the nodes of the crcut,

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

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

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

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell

Module 6: FEM for Plates and Shells Lecture 6: Finite Element Analysis of Shell Module 6: FEM for Plates and Shells Lecture 6: Fnte Element Analyss of Shell 3 6.6. Introducton A shell s a curved surface, whch by vrtue of ther shape can wthstand both membrane and bendng forces. A shell

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

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like: Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A

More information

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal

More information

Motivation. EE 457 Unit 4. Throughput vs. Latency. Performance Depends on View Point?! Computer System Performance. An individual user wants to:

Motivation. EE 457 Unit 4. Throughput vs. Latency. Performance Depends on View Point?! Computer System Performance. An individual user wants to: 4.1 4.2 Motvaton EE 457 Unt 4 Computer System Performance An ndvdual user wants to: Mnmze sngle program executon tme A datacenter owner wants to: Maxmze number of Mnmze ( ) http://e-tellgentnternetmarketng.com/webste/frustrated-computer-user-2/

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

A Facet Generation Procedure. for solving 0/1 integer programs

A Facet Generation Procedure. for solving 0/1 integer programs A Facet Generaton Procedure for solvng 0/ nteger programs by Gyana R. Parja IBM Corporaton, Poughkeepse, NY 260 Radu Gaddov Emery Worldwde Arlnes, Vandala, Oho 45377 and Wlbert E. Wlhelm Teas A&M Unversty,

More information