TOOTH ALIGNMENT OF THE DENTAL CAST USING 3D THIN PLATE SPLINE

Size: px
Start display at page:

Download "TOOTH ALIGNMENT OF THE DENTAL CAST USING 3D THIN PLATE SPLINE"

Transcription

1 TOOTH ALIGMET OF THE DETAL CAST USIG 3D THI LATE SLIE Chanjira Sinhanaohin, Wisaru Bholsihi, Wichi Tharanon aional Science and Technolog Developmen Agenc (STDA 111 Thailand Science ark, hahon-yohin d, Klong 1, Klong Luang, ahumhani, 11, Thailand ABSTACT This aricle has described he process for ooh alignmen using 3D hin plae spline. Firs sep is o simulae he individual ooh and is landmarks and fi i ino he denal cas b geomeric ransformaion of individual ooh. ex sep is o align he simulaed eeh ino proper arch form. Afer ha, record pairs he landmarks of simulaed eeh before and afer ooh alignmen. The final sep is o creae he new denal cas (warping denal cas b appling 3D hin plae spline echnique hrough pairs of landmarks on simulaed eeh as inpus. The resuls of he ooh alignmen and he comparison wih he denal cas before ooh alignmen have shown ha he eeh aligned along he curve while he gum was slighl deformed o show he smoohness of ransformaion on eeh and gum. KEY WODS Visualisaion, Simulaion, Thin plae spline, Teeh Alignmen 1. Inroducion The orhodonics analses have relied upon denal cass made from plaser of aris wih silicone mold for a long ime. The saes-of-he-ar echnologies have allowed orhodonic diagnosic, analses and reamens planning in 3D images of denal cass hrough commercial sofware on personal compuers. OrhoCAD of Caden Inc. [1], 3Dxer of Dimemnex Digial Lab [] and e-model from Geodigm Corp. [3] and Applicaion Visualiaion Ssem Express of Advanced Visual Ssem Inc.[4] are a few examples of sofware for 3D orhodonic planning and analses. However, exising commercial sofware for 3D orhodonic analses has a limiaion of displaing onl crown secion of he denal cas wihou aking anaom informaion of denal roos ino accoun. [5] Even hough here are some researches for he complee 3D model of human jaw including denal crowns and roos [6], i requires rigid regisraion and hen maching on boh denal crowns and roos wih he 3D Freeform Deformaion (FFD denal models which is a imeconsuming process. Macchi [7] has developed he echnique for 3D superimposiion of anaomic eeh and cas model. However, he sofware menioned in Macchi s paper is no eas o use and also required he significan user skill. Furhermore, we have found he similar problem when we ried o perform image regisraion beween CT images and model image due o he sheer amoun of 3D daa which complicae he difficul o segmen he 3D CT model.. Mehod.1 egisraion beween Denal Cas and Simulaed Teeh This secion, firs he simulaed eeh were creaed using 3D Sudio Max [8]. The enamel-crowns emplae shown in Fig. 1(A was scanned and segmened o be a emplae of 3D crowns. The denal roos were designed and aached o he crowns as shown in Fig. 1(B. The resul of he simulaed 3D eeh model can be shown as in fig. 1 (C, which shows he side and op views respecivel. Also he eeh landmarks of each ooh have been locaed on he surface of he simulaed eeh as can be seen in fig 1(C as well. ex sep is o locae he occlusal landmarks on he op of each ooh or a some seleced eeh and appl cubic spline echnique [9] o fi he landmark as he race line posiions in 3D. To locae he occusal landmarks, user needs o click on he 3D objec o locae he landmarks. ormall he sofware recognies he landmarks locaed b user on screen as screen coordinaes. Therefore, i is needed o call gluunprojec funcion from opengl librar o conver screen coordinaes ino o 3D model coordinaes. To appl he D-o-3D coordinae conversion on mouse coordinaes, several variables has o pass ino he funcions such as funcion for viewpor origin and exen, modelview Marix, projecion marix and he windows

2 screen coordinae which can be obained using some opengl funcions. [1] he denal cas b using he funcion provided in our sofware. Once ever ooh has been moved o mach he cas, hen he user can saved i and open i laer on wihou regisraion ever ime running. Translaion expression can be seen in (1. (A (B x1 + x T T +.. (1 x + x + + The roaion abou x-axis, -axis and -axis can be seen in (A, (B and (C according o Euler angles. [1] (C Fig. 1: Simulaed Teeh wih roos using 3D Sudio Max. A The enamel-crown emplae used in he denal lab. B 3D Crowns creaed from he emplae. C Side and op view of Simulaed Teeh wih roos. Once locaing occlusal landmarks, he cubic spline algorihm will be execued o generae race line and he landmarks. The cubic spline consiss of secions of polnomial curves conneced a hese landmarks. The polnomials of a given spline all have he same degree in X, Y and Z and fi he conrol poins as 3D line. The spline is obained b calculaing he second derivaives of he inerpolaing funcion a he abulaed poins based on he formulaion given in umerical ecipes in C. [11] The resul of fiing he 3D line wih cubic-spline inerpolaion can be seen as in Fig.. X Y Z X,, Y Z Y, Z X 1 cos( θ sin( θ cos( θ sin( θ cos( θ x sin( θ (A x sin( θ cos( x θx 1 sin( θ cos( θ sin( θ cos( θ 1 (B (C The scaling echnique can be expressed as (3 where S is he scaling value. S * x1 S * 1 S * 1 S S (3 S * x S * S * The regisraion ool ha has been developed for his projec le he user be able o move, roae and scale he eeh emplae o mach he denal cas as shown in Fig 3. Fig. 3(A shows he resul before he regisraion while Fig. 3(B shows he resul for moving, roaing and scaling individual eeh on he eeh emplae. Fig 3(C shows he final resul of image regisraion of eeh emplae on denal cas while program in Fig 3(D shows he cas wih 6 % ranslucen o enable user o see he roos inside he cas along wih he mechanic for moving he individual ooh. Fig. : Cubic spline fiing line on he occlusal landmarks The occlusal line was applied o calculae he saring posiion of he 3D simulaed eeh. Then, each ooh was ranslaed, roaed and scaled in o he righ posiion o fi (A (B (C 56

3 Afer simulaing he final posiions for eeh emplae, he nex sep is o compare he landmarks of he iniial simulaed eeh wih corresponding landmarks on he aligned eeh emplae as shown in Fig. 6. Fig. 6(A shows he resul of paring landmarks wih he denal cas while Fig 6(B shows he resuls of paring wihou denal cas. (D Fig 3: Simulaed eeh regisering on he denal cas.. Simulaed Teeh Alignmen (A (B Fig. 6: The resuls of paring landmarks..3 Denal Cas Warping based on 3D Thin lae Spline (A (B Fig. 4: Iniialiing he eeh alignmen. In he experimen of ooh alignmen wihou causing denal roos proruding ou of he gum in he denal cas, he firs sep is o seup he iniial posiion for simulaing eeh alignmen. esearchers have defined he occlusal landmarks in red for each ooh in he denal cas in o draw he race line and hen superimpose he eeh emplae on he denal cas as shown in Fig. 4(A o se he iniial eeh posiions before performing eeh alignmen as shown in Fig. 4(B Afer he iniialiaion, he nex sep is o se he final posiions for he eeh alignmen. This can be done b appling a eeh emplae wih geomeric ransformaion ino he proper alignmen as shown in Fig. 5(A. Comparison of he proper simulaed eeh alignmen and he denal cas can be seen as in Fig. 5(B. To generae he new denal cas, i becomes necessar o appl algorihms o creae he new model from he eeh movemen daa. One of he algorihms for his ask is Thin lae Spline (TS. Thin plae spline (TS [13] is he echnique for esimaing he random daa from paring ses of daa o consruc spline map from he affine facor for linear disorion and weighing facor for nonlinear disorion for image regisraion. The firs sep of TS is o solve (4 o calculae boh affine facor A and weighing facor W. K ˆ T ˆ W V O(4,4 A O(4,3 T O(r,w is ero marix of 4x4 and 4x3 respecivel. ˆ is a marix ˆ ha has rows and columns swiched (ransposed ˆ marix. ˆ is an iniial landmark posiion se marix before moving wih he addiional value 1 in ever row defined in (5 while K is a marix U (r defined as a funcion of disance beween each landmark r for image disorion b 3D Thin lae Spline process as defined in (6 and (7 respecivel, and V is an final landmark posiion se marix afer moving he landmarks defined in (8 wih is he number of sar-sop landmark pairs. (4 1 x ˆ [ Ones(1,, ] (5 1 x 1 (A (B Fig 5: Simulaing he final posiions for eeh emplae. 57

4 1 K U ( r( ( 1 U U 1( 1 ( 1 ( 1 1 ( 1 (6 e. g.: r (7 1, ( x x1 + ( 1 + ( 1 x V (8 x Fig. 7 shows he resul from 3D Thin lae Spline applied on he skeleon model wih 1 pairs of landmarks as demonsraed in [14]. Fig. 7(A showed he skeleal before appling 3D TS while Fig. 7(B shows he final resul afer 3D hin plae spline. (A (B Fig. 7.TS Applicaion in 3D on he Skeleal Model 3. esuls The experimen resuls show he denal cas before ooh alignmen as Fig. 8(A while Fig 8(B shows he final resuls afer performing ooh alignmen using 3D Thin lae Spline echnique. (A (B Fig. 8 Comparing he Teeh Alignmen esuls The resuls showing in Fig. 9 are he superimposiion of he final eeh alignmen wih he iniial denal cas in differen angles. The resul has shown ha here is no denal roo proruding ou of he old and he new denal cas as well. Fig. 9 The superimposiion of he resuls eeh alignmen However, here are some disorions on he individual eeh afer performing ooh alignmen due o he effec of 3D Thin lae Spline. The individual eeh are no supposed o be disored shapes since he are inelasic compared wih he gum secion. Furher developmen is o perform segmenaion on individual eeh as shown he case of markerconrolled mesh segmenaion [15] or auomaic segmenaion b using range images o deec boh denal arch and ooh inersices before segmenaion [16]. ex sep afer segmenaion is o applied proper 3D cubic spline inerpolaion algorihms on he model mesh o fill he missing pars of individual eeh. These algorihms would allow users o perform eeh alignmen wih minimied shape disorion on each ooh b separaing eeh ou of he model before performing 3D Thin plae Spline. 4. Conclusion We have proposed an approach o align eeh on he denal cas for orhodonic planning b appling 3D hin plae spline echnique. This mehod has used a se of eeh wih crowns and roos as a emplae o mach wih eeh on he denal cas o se he iniial posiions for landmarks on denal emplae. Afer performing eeh alignmen o se he final posiions for landmarks on denal plae, we perform 3D hin plae spline o creae he new model of denal cas. Our approach depends onl on he changing posiions of landmarks on he emplae wihou reling upon CT scan daa which are expensive imaging. The applicaion of image regisraion is o mach he denal emplae wih denal cas wih he bes fi wih available upper pars of eeh. The applicaion of 3D hin plae spline is o creae he new denal model afer orhodonic reamen o appl orhodoniss o predic he final resuls of he reamen in advance. 58

5 Acknowledgemen Thanks o Dr. Kanoknar Chinakanon and Orhodoniss eam a ADTEC for heir advices in he projec. Also hanks o all ADTEC & ECTEC s members for heir friendship and encouragemen. eferences [1] M. Meer, Comparison of peer assessmen raing (A index scores of plaser and compuer-based digial models, American Journal of Orhodonics and Denofacial Orhopedics, 18(4, 5, df [] C. Li. e. al, Orhodonic Simulaion and Diagnosis: An Enhanced Tool for Deniss, roc. 7 h IEEE Conf. on IEEE Engineering in Medicine and Biolog, Shanghai, China, 5, ieeexplore.ieee.org/iel5/1755/339/ pdf [3] B. heude e. al., An Evaluaion of he Use of Digial Sud Models in Orhodonic Diagnosis and Treamen lanning, Angle Orhodonis, 75(3, 5, [4]. Moohashi e. al., A 3D compuer-aided design ssem applied o diagnosis and reamen planning in orhodonics and orhognahic surger, European Journal of Orhodonics, 1(3, June 1999., Available online a ejo.oxfordjournals.org/cgi/reprin/1/3/63.pdf [5] A.A. Goshasb, A Ssem for Digial econsrucion of Gpsum Denal Cass, IEEE Transacions on Medical Imaging, 16(5, Ocober ieeexplore.ieee.org/iel3/4/1397/64757.pdf [6] H. Hassan e. al., A Complee Volumeric 3D Model of he Human Jaw, roc. of Compuer Assised adiolog and Surger (CAS, Berlin, German, June 5, ub_df/5/cmi_hossam5.pdf [7] A. Macchi e. al, Three-dimensional digial modeling and seup, American Journal of Orhodonics and Denofacial Orhopedics, 19(5, Ma 6, [8].Ensio e. al., The virual craniofacial paien: 3D jaw modeling and animaion. roc.11h Medicine Mees Virual eali, ew or Beach, California, USA, Januar 3, hp://graphics.usc.edu/cgi/pdf/papers/eciso_jawani m_mmv_fial.pdf [9] J. V. Hajnal, e. al, A regisraion and inerpolaion procedure for subvoxel maching of seriall acquired M images, Journal of Compuer Assised Tomograph, 19(, Februar 1995, [1] Mason Woo e. al., OpenGL rogramming Guide: The Official Guide o Learning OpenGL, Version 1.1, Second Ediion, (ew York, Addison-Wesle ublishing, Januar [11] W.H. ress. e. al., Cubic Spline Inerpolaion, umerical ecipes in C: The Ar of Scienific Compuing, (Cambridge, UK: Cambridge Universi ress, hp:// [1] E. Lengel, Mahemaics for 3D Game rogramming and Compuer Graphics, Second Ediion, (Boson, MA, USA: Charles iver Media, 3. [13] F. L. Booksein, rincipal Warps: Thin-lae Splines and he Decomposiion of Deformaions, IEEE Transacion on. aern Analsis and Machine Inelligence, 11(6, June 1989, Available online a www-cse.ucsd.edu/classes/sp3/cse5/booksein.pdf [14] C. Sinnhanaohin e. al, Image Warping based on 3D Thin lae Spline, roc. 4 h Inernaional Conf. on Informaion Technolog in Asia, Kuching, Malasia, 5, [15] Z. Chi e. al., Marker-conrolled ercepion-based Mesh Segmenaion, roc. 3 rd Iner Conf. on Image and Graphics, Hong Kong, China, Available online a graphics.pku.edu.cn/papers/download/mms.pdf [16] T. Kondo e. al., Tooh Segmenaion of Denal Sud Models Using ange Images, IEEE Transacion on Medical Imaging, 3(3, March 4, ieeexplore.ieee.org/iel5/4/8414/ pdf 59

4.1 3D GEOMETRIC TRANSFORMATIONS

4.1 3D GEOMETRIC TRANSFORMATIONS MODULE IV MCA - 3 COMPUTER GRAPHICS ADMN 29- Dep. of Compuer Science And Applicaions, SJCET, Palai 94 4. 3D GEOMETRIC TRANSFORMATIONS Mehods for geomeric ransformaions and objec modeling in hree dimensions

More information

CENG 477 Introduction to Computer Graphics. Modeling Transformations

CENG 477 Introduction to Computer Graphics. Modeling Transformations CENG 477 Inroducion o Compuer Graphics Modeling Transformaions Modeling Transformaions Model coordinaes o World coordinaes: Model coordinaes: All shapes wih heir local coordinaes and sies. world World

More information

CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL

CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL Klečka Jan Docoral Degree Programme (1), FEEC BUT E-mail: xkleck01@sud.feec.vubr.cz Supervised by: Horák Karel E-mail: horak@feec.vubr.cz

More information

Spline Curves. Color Interpolation. Normal Interpolation. Last Time? Today. glshademodel (GL_SMOOTH); Adjacency Data Structures. Mesh Simplification

Spline Curves. Color Interpolation. Normal Interpolation. Last Time? Today. glshademodel (GL_SMOOTH); Adjacency Data Structures. Mesh Simplification Las Time? Adjacency Daa Srucures Spline Curves Geomeric & opologic informaion Dynamic allocaion Efficiency of access Mesh Simplificaion edge collapse/verex spli geomorphs progressive ransmission view-dependen

More information

EECS 487: Interactive Computer Graphics

EECS 487: Interactive Computer Graphics EECS 487: Ineracive Compuer Graphics Lecure 7: B-splines curves Raional Bézier and NURBS Cubic Splines A represenaion of cubic spline consiss of: four conrol poins (why four?) hese are compleely user specified

More information

Geometry Transformation

Geometry Transformation Geomer Transformaion Januar 26 Prof. Gar Wang Dep. of Mechanical and Manufacuring Engineering Universi of Manioba Wh geomer ransformaion? Beer undersanding of he design Communicaion wih cusomers Generaing

More information

STEREO PLANE MATCHING TECHNIQUE

STEREO PLANE MATCHING TECHNIQUE STEREO PLANE MATCHING TECHNIQUE Commission III KEY WORDS: Sereo Maching, Surface Modeling, Projecive Transformaion, Homography ABSTRACT: This paper presens a new ype of sereo maching algorihm called Sereo

More information

Point Cloud Representation of 3D Shape for Laser- Plasma Scanning 3D Display

Point Cloud Representation of 3D Shape for Laser- Plasma Scanning 3D Display Poin Cloud Represenaion of 3D Shape for Laser- Plasma Scanning 3D Displa Hiroo Ishikawa and Hideo Saio Keio Universi E-mail {hiroo, saio}@ozawa.ics.keio.ac.jp Absrac- In his paper, a mehod of represening

More information

M y. Image Warping. Targil 7 : Image Warping. Image Warping. 2D Geometric Transformations. image filtering: change range of image g(x) = T(f(x))

M y. Image Warping. Targil 7 : Image Warping. Image Warping. 2D Geometric Transformations. image filtering: change range of image g(x) = T(f(x)) Hebrew Universi Image Processing - 6 Image Warping Hebrew Universi Image Processing - 6 argil 7 : Image Warping D Geomeric ransormaions hp://www.jere-marin.com Man slides rom Seve Seiz and Aleei Eros Image

More information

Research Article Auto Coloring with Enhanced Character Registration

Research Article Auto Coloring with Enhanced Character Registration Compuer Games Technology Volume 2008, Aricle ID 35398, 7 pages doi:0.55/2008/35398 Research Aricle Auo Coloring wih Enhanced Characer Regisraion Jie Qiu, Hock Soon Seah, Feng Tian, Quan Chen, Zhongke Wu,

More information

MATH Differential Equations September 15, 2008 Project 1, Fall 2008 Due: September 24, 2008

MATH Differential Equations September 15, 2008 Project 1, Fall 2008 Due: September 24, 2008 MATH 5 - Differenial Equaions Sepember 15, 8 Projec 1, Fall 8 Due: Sepember 4, 8 Lab 1.3 - Logisics Populaion Models wih Harvesing For his projec we consider lab 1.3 of Differenial Equaions pages 146 o

More information

Curves & Surfaces. Last Time? Today. Readings for Today (pick one) Limitations of Polygonal Meshes. Today. Adjacency Data Structures

Curves & Surfaces. Last Time? Today. Readings for Today (pick one) Limitations of Polygonal Meshes. Today. Adjacency Data Structures Las Time? Adjacency Daa Srucures Geomeric & opologic informaion Dynamic allocaion Efficiency of access Curves & Surfaces Mesh Simplificaion edge collapse/verex spli geomorphs progressive ransmission view-dependen

More information

A Matching Algorithm for Content-Based Image Retrieval

A Matching Algorithm for Content-Based Image Retrieval A Maching Algorihm for Conen-Based Image Rerieval Sue J. Cho Deparmen of Compuer Science Seoul Naional Universiy Seoul, Korea Absrac Conen-based image rerieval sysem rerieves an image from a daabase using

More information

NEWTON S SECOND LAW OF MOTION

NEWTON S SECOND LAW OF MOTION Course and Secion Dae Names NEWTON S SECOND LAW OF MOTION The acceleraion of an objec is defined as he rae of change of elociy. If he elociy changes by an amoun in a ime, hen he aerage acceleraion during

More information

Image warping Li Zhang CS559

Image warping Li Zhang CS559 Wha is an image Image arping Li Zhang S559 We can hink of an image as a funcion, f: R 2 R: f(, ) gives he inensi a posiion (, ) defined over a recangle, ih a finie range: f: [a,b][c,d] [,] f Slides solen

More information

Image warping/morphing

Image warping/morphing Image arping/morphing Image arping Digial Visual Effecs Yung-Yu Chuang ih slides b Richard Szeliski, Seve Seiz, Tom Funkhouser and leei Efros Image formaion Sampling and quanizaion B Wha is an image We

More information

Implementing Ray Casting in Tetrahedral Meshes with Programmable Graphics Hardware (Technical Report)

Implementing Ray Casting in Tetrahedral Meshes with Programmable Graphics Hardware (Technical Report) Implemening Ray Casing in Terahedral Meshes wih Programmable Graphics Hardware (Technical Repor) Marin Kraus, Thomas Erl March 28, 2002 1 Inroducion Alhough cell-projecion, e.g., [3, 2], and resampling,

More information

Schedule. Curves & Surfaces. Questions? Last Time: Today. Limitations of Polygonal Meshes. Acceleration Data Structures.

Schedule. Curves & Surfaces. Questions? Last Time: Today. Limitations of Polygonal Meshes. Acceleration Data Structures. Schedule Curves & Surfaces Sunday Ocober 5 h, * 3-5 PM *, Room TBA: Review Session for Quiz 1 Exra Office Hours on Monday (NE43 Graphics Lab) Tuesday Ocober 7 h : Quiz 1: In class 1 hand-wrien 8.5x11 shee

More information

A METHOD OF MODELING DEFORMATION OF AN OBJECT EMPLOYING SURROUNDING VIDEO CAMERAS

A METHOD OF MODELING DEFORMATION OF AN OBJECT EMPLOYING SURROUNDING VIDEO CAMERAS A METHOD OF MODELING DEFORMATION OF AN OBJECT EMLOYING SURROUNDING IDEO CAMERAS Joo Kooi TAN, Seiji ISHIKAWA Deparmen of Mechanical and Conrol Engineering Kushu Insiue of Technolog, Japan ehelan@is.cnl.kuech.ac.jp,

More information

Image segmentation. Motivation. Objective. Definitions. A classification of segmentation techniques. Assumptions for thresholding

Image segmentation. Motivation. Objective. Definitions. A classification of segmentation techniques. Assumptions for thresholding Moivaion Image segmenaion Which pixels belong o he same objec in an image/video sequence? (spaial segmenaion) Which frames belong o he same video sho? (emporal segmenaion) Which frames belong o he same

More information

Upper Body Tracking for Human-Machine Interaction with a Moving Camera

Upper Body Tracking for Human-Machine Interaction with a Moving Camera The 2009 IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems Ocober -5, 2009 S. Louis, USA Upper Body Tracking for Human-Machine Ineracion wih a Moving Camera Yi-Ru Chen, Cheng-Ming Huang, and

More information

A new algorithm for small object tracking based on super-resolution technique

A new algorithm for small object tracking based on super-resolution technique A new algorihm for small objec racking based on super-resoluion echnique Yabunayya Habibi, Dwi Rana Sulisyaningrum, and Budi Seiyono Ciaion: AIP Conference Proceedings 1867, 020024 (2017); doi: 10.1063/1.4994427

More information

FIELD PROGRAMMABLE GATE ARRAY (FPGA) AS A NEW APPROACH TO IMPLEMENT THE CHAOTIC GENERATORS

FIELD PROGRAMMABLE GATE ARRAY (FPGA) AS A NEW APPROACH TO IMPLEMENT THE CHAOTIC GENERATORS FIELD PROGRAMMABLE GATE ARRAY (FPGA) AS A NEW APPROACH TO IMPLEMENT THE CHAOTIC GENERATORS Mohammed A. Aseeri and M. I. Sobhy Deparmen of Elecronics, The Universiy of Ken a Canerbury Canerbury, Ken, CT2

More information

Today. Curves & Surfaces. Can We Disguise the Facets? Limitations of Polygonal Meshes. Better, but not always good enough

Today. Curves & Surfaces. Can We Disguise the Facets? Limitations of Polygonal Meshes. Better, but not always good enough Today Curves & Surfaces Moivaion Limiaions of Polygonal Models Some Modeling Tools & Definiions Curves Surfaces / Paches Subdivision Surfaces Limiaions of Polygonal Meshes Can We Disguise he Faces? Planar

More information

EXPERIMENTAL RESULTS GOT WITH THE OMNIDIRECTIONAL VISION SENSOR: SYCLOP

EXPERIMENTAL RESULTS GOT WITH THE OMNIDIRECTIONAL VISION SENSOR: SYCLOP EXERIENTAL RESULTS GOT WITH THE ONIDIRECTIONAL ISION SENSOR: SYCLO Eric BRASSART, Lauren DELAHOCHE, Cyril CAUCHOIS, Cyril DROCOURT, Claude EGARD, El usapha OUADDIB CREA (Cenre de Roboique d Elecroechnique

More information

MORPHOLOGICAL SEGMENTATION OF IMAGE SEQUENCES

MORPHOLOGICAL SEGMENTATION OF IMAGE SEQUENCES MORPHOLOGICAL SEGMENTATION OF IMAGE SEQUENCES B. MARCOTEGUI and F. MEYER Ecole des Mines de Paris, Cenre de Morphologie Mahémaique, 35, rue Sain-Honoré, F 77305 Fonainebleau Cedex, France Absrac. In image

More information

Last Time: Curves & Surfaces. Today. Questions? Limitations of Polygonal Meshes. Can We Disguise the Facets?

Last Time: Curves & Surfaces. Today. Questions? Limitations of Polygonal Meshes. Can We Disguise the Facets? Las Time: Curves & Surfaces Expeced value and variance Mone-Carlo in graphics Imporance sampling Sraified sampling Pah Tracing Irradiance Cache Phoon Mapping Quesions? Today Moivaion Limiaions of Polygonal

More information

MOTION DETECTORS GRAPH MATCHING LAB PRE-LAB QUESTIONS

MOTION DETECTORS GRAPH MATCHING LAB PRE-LAB QUESTIONS NME: TE: LOK: MOTION ETETORS GRPH MTHING L PRE-L QUESTIONS 1. Read he insrucions, and answer he following quesions. Make sure you resae he quesion so I don hae o read he quesion o undersand he answer..

More information

Shortest Path Algorithms. Lecture I: Shortest Path Algorithms. Example. Graphs and Matrices. Setting: Dr Kieran T. Herley.

Shortest Path Algorithms. Lecture I: Shortest Path Algorithms. Example. Graphs and Matrices. Setting: Dr Kieran T. Herley. Shores Pah Algorihms Background Seing: Lecure I: Shores Pah Algorihms Dr Kieran T. Herle Deparmen of Compuer Science Universi College Cork Ocober 201 direced graph, real edge weighs Le he lengh of a pah

More information

Motion Estimation of a Moving Range Sensor by Image Sequences and Distorted Range Data

Motion Estimation of a Moving Range Sensor by Image Sequences and Distorted Range Data Moion Esimaion of a Moving Range Sensor by Image Sequences and Disored Range Daa Asuhiko Banno, Kazuhide Hasegawa and Kasushi Ikeuchi Insiue of Indusrial Science Universiy of Tokyo 4-6-1 Komaba, Meguro-ku,

More information

Proceeding of the 6 th International Symposium on Artificial Intelligence and Robotics & Automation in Space: i-sairas 2001, Canadian Space Agency,

Proceeding of the 6 th International Symposium on Artificial Intelligence and Robotics & Automation in Space: i-sairas 2001, Canadian Space Agency, Proceeding of he 6 h Inernaional Symposium on Arificial Inelligence and Roboics & Auomaion in Space: i-sairas 00, Canadian Space Agency, S-Huber, Quebec, Canada, June 8-, 00. Muli-resoluion Mapping Using

More information

Motion Level-of-Detail: A Simplification Method on Crowd Scene

Motion Level-of-Detail: A Simplification Method on Crowd Scene Moion Level-of-Deail: A Simplificaion Mehod on Crowd Scene Absrac Junghyun Ahn VR lab, EECS, KAIST ChocChoggi@vr.kais.ac.kr hp://vr.kais.ac.kr/~zhaoyue Recen echnological improvemen in characer animaion

More information

AUTOMATIC 3D FACE REGISTRATION WITHOUT INITIALIZATION

AUTOMATIC 3D FACE REGISTRATION WITHOUT INITIALIZATION Chaper 3 AUTOMATIC 3D FACE REGISTRATION WITHOUT INITIALIZATION A. Koschan, V. R. Ayyagari, F. Boughorbel, and M. A. Abidi Imaging, Roboics, and Inelligen Sysems Laboraory, The Universiy of Tennessee, 334

More information

Virtual Recovery of Excavated Archaeological Finds

Virtual Recovery of Excavated Archaeological Finds Virual Recovery of Excavaed Archaeological Finds Jiang Yu ZHENG, Zhong Li ZHANG*, Norihiro ABE Kyushu Insiue of Technology, Iizuka, Fukuoka 820, Japan *Museum of he Terra-Coa Warrlors and Horses, Lin Tong,

More information

Learning in Games via Opponent Strategy Estimation and Policy Search

Learning in Games via Opponent Strategy Estimation and Policy Search Learning in Games via Opponen Sraegy Esimaion and Policy Search Yavar Naddaf Deparmen of Compuer Science Universiy of Briish Columbia Vancouver, BC yavar@naddaf.name Nando de Freias (Supervisor) Deparmen

More information

AML710 CAD LECTURE 11 SPACE CURVES. Space Curves Intrinsic properties Synthetic curves

AML710 CAD LECTURE 11 SPACE CURVES. Space Curves Intrinsic properties Synthetic curves AML7 CAD LECTURE Space Curves Inrinsic properies Synheic curves A curve which may pass hrough any region of hreedimensional space, as conrased o a plane curve which mus lie on a single plane. Space curves

More information

Image Content Representation

Image Content Representation Image Conen Represenaion Represenaion for curves and shapes regions relaionships beween regions E.G.M. Perakis Image Represenaion & Recogniion 1 Reliable Represenaion Uniqueness: mus uniquely specify an

More information

3-D Object Modeling and Recognition for Telerobotic Manipulation

3-D Object Modeling and Recognition for Telerobotic Manipulation Research Showcase @ CMU Roboics Insiue School of Compuer Science 1995 3-D Objec Modeling and Recogniion for Teleroboic Manipulaion Andrew Johnson Parick Leger Regis Hoffman Marial Heber James Osborn Follow

More information

DETC2004/CIE VOLUME-BASED CUT-AND-PASTE EDITING FOR EARLY DESIGN PHASES

DETC2004/CIE VOLUME-BASED CUT-AND-PASTE EDITING FOR EARLY DESIGN PHASES Proceedings of DETC 04 ASME 004 Design Engineering Technical Conferences and Compuers and Informaion in Engineering Conference Sepember 8-Ocober, 004, Sal Lake Ciy, Uah USA DETC004/CIE-57676 VOLUME-BASED

More information

Low-Cost WLAN based. Dr. Christian Hoene. Computer Science Department, University of Tübingen, Germany

Low-Cost WLAN based. Dr. Christian Hoene. Computer Science Department, University of Tübingen, Germany Low-Cos WLAN based Time-of-fligh fligh Trilaeraion Precision Indoor Personnel Locaion and Tracking for Emergency Responders Third Annual Technology Workshop, Augus 5, 2008 Worceser Polyechnic Insiue, Worceser,

More information

Gauss-Jordan Algorithm

Gauss-Jordan Algorithm Gauss-Jordan Algorihm The Gauss-Jordan algorihm is a sep by sep procedure for solving a sysem of linear equaions which may conain any number of variables and any number of equaions. The algorihm is carried

More information

Sam knows that his MP3 player has 40% of its battery life left and that the battery charges by an additional 12 percentage points every 15 minutes.

Sam knows that his MP3 player has 40% of its battery life left and that the battery charges by an additional 12 percentage points every 15 minutes. 8.F Baery Charging Task Sam wans o ake his MP3 player and his video game player on a car rip. An hour before hey plan o leave, he realized ha he forgo o charge he baeries las nigh. A ha poin, he plugged

More information

Real Time Integral-Based Structural Health Monitoring

Real Time Integral-Based Structural Health Monitoring Real Time Inegral-Based Srucural Healh Monioring The nd Inernaional Conference on Sensing Technology ICST 7 J. G. Chase, I. Singh-Leve, C. E. Hann, X. Chen Deparmen of Mechanical Engineering, Universiy

More information

Parallel and Distributed Systems for Constructive Neural Network Learning*

Parallel and Distributed Systems for Constructive Neural Network Learning* Parallel and Disribued Sysems for Consrucive Neural Nework Learning* J. Flecher Z. Obradovi School of Elecrical Engineering and Compuer Science Washingon Sae Universiy Pullman WA 99164-2752 Absrac A consrucive

More information

Evaluation and Improvement of Region-based Motion Segmentation

Evaluation and Improvement of Region-based Motion Segmentation Evaluaion and Improvemen of Region-based Moion Segmenaion Mark Ross Universiy Koblenz-Landau, Insiue of Compuaional Visualisics, Universiässraße 1, 56070 Koblenz, Germany Email: ross@uni-koblenz.de Absrac

More information

Reinforcement Learning by Policy Improvement. Making Use of Experiences of The Other Tasks. Hajime Kimura and Shigenobu Kobayashi

Reinforcement Learning by Policy Improvement. Making Use of Experiences of The Other Tasks. Hajime Kimura and Shigenobu Kobayashi Reinforcemen Learning by Policy Improvemen Making Use of Experiences of The Oher Tasks Hajime Kimura and Shigenobu Kobayashi Tokyo Insiue of Technology, JAPAN genfe.dis.iech.ac.jp, kobayasidis.iech.ac.jp

More information

Projection & Interaction

Projection & Interaction Projecion & Ineracion Algebra of projecion Canonical viewing volume rackball inerface ransform Hierarchies Preview of Assignmen #2 Lecure 8 Comp 236 Spring 25 Projecions Our lives are grealy simplified

More information

Integro-differential splines and quadratic formulae

Integro-differential splines and quadratic formulae Inegro-differenial splines and quadraic formulae I.G. BUROVA, O. V. RODNIKOVA S. Peersburg Sae Universiy 7/9 Universiesaya nab., S.Persburg, 9934 Russia i.g.burova@spbu.ru, burovaig@mail.ru Absrac: This

More information

An Improved Square-Root Nyquist Shaping Filter

An Improved Square-Root Nyquist Shaping Filter An Improved Square-Roo Nyquis Shaping Filer fred harris San Diego Sae Universiy fred.harris@sdsu.edu Sridhar Seshagiri San Diego Sae Universiy Seshigar.@engineering.sdsu.edu Chris Dick Xilinx Corp. chris.dick@xilinx.com

More information

Improving Occupancy Grid FastSLAM by Integrating Navigation Sensors

Improving Occupancy Grid FastSLAM by Integrating Navigation Sensors Improving Occupancy Grid FasSLAM by Inegraing Navigaion Sensors Chrisopher Weyers Sensors Direcorae Air Force Research Laboraory Wrigh-Paerson AFB, OH 45433 Gilber Peerson Deparmen of Elecrical and Compuer

More information

Image Registration in Medical Imaging

Image Registration in Medical Imaging 2/2/202 Image Regisraion in Medical Imaging BI260 VALERIE CARDENAS NICOLSON, PH.D ACKNOWLEDGEMENTS: COLIN STUDHOLME, PH.D. Medical Imaging Analysis Developing mahemaical algorihms o erac and relae informaion

More information

Image Based Computer-Aided Manufacturing Technology

Image Based Computer-Aided Manufacturing Technology Sensors & Transducers 03 by IFSA hp://www.sensorsporal.com Image Based Compuer-Aided Manufacuring Technology Zhanqi HU Xiaoqin ZHANG Jinze LI Wei LI College of Mechanical Engineering Yanshan Universiy

More information

Hierarchical Information Fusion for Human Upper Limb Motion Capture

Hierarchical Information Fusion for Human Upper Limb Motion Capture 1h Inernaional Conference on Informaion Fusion Seale, WA, USA, July 6-9, 009 Hierarchical Informaion Fusion for Human Upper Limb Moion Capure Zhiqiang Zhang 1,, Zhipei Huang 1 and Jiankang Wu 1, 1 Graduae

More information

Real-Time Avatar Animation Steered by Live Body Motion

Real-Time Avatar Animation Steered by Live Body Motion Real-Time Avaar Animaion Seered by Live Body Moion Oliver Schreer, Ralf Tanger, Peer Eiser, Peer Kauff, Bernhard Kaspar, and Roman Engler 3 Fraunhofer Insiue for Telecommunicaions/Heinrich-Herz-Insiu,

More information

A time-space consistency solution for hardware-in-the-loop simulation system

A time-space consistency solution for hardware-in-the-loop simulation system Inernaional Conference on Advanced Elecronic Science and Technology (AEST 206) A ime-space consisency soluion for hardware-in-he-loop simulaion sysem Zexin Jiang a Elecric Power Research Insiue of Guangdong

More information

Voltair Version 2.5 Release Notes (January, 2018)

Voltair Version 2.5 Release Notes (January, 2018) Volair Version 2.5 Release Noes (January, 2018) Inroducion 25-Seven s new Firmware Updae 2.5 for he Volair processor is par of our coninuing effors o improve Volair wih new feaures and capabiliies. For

More information

In Proceedings of CVPR '96. Structure and Motion of Curved 3D Objects from. using these methods [12].

In Proceedings of CVPR '96. Structure and Motion of Curved 3D Objects from. using these methods [12]. In Proceedings of CVPR '96 Srucure and Moion of Curved 3D Objecs from Monocular Silhouees B Vijayakumar David J Kriegman Dep of Elecrical Engineering Yale Universiy New Haven, CT 652-8267 Jean Ponce Compuer

More information

4. Minimax and planning problems

4. Minimax and planning problems CS/ECE/ISyE 524 Inroducion o Opimizaion Spring 2017 18 4. Minima and planning problems ˆ Opimizing piecewise linear funcions ˆ Minima problems ˆ Eample: Chebyshev cener ˆ Muli-period planning problems

More information

User Adjustable Process Scheduling Mechanism for a Multiprocessor Embedded System

User Adjustable Process Scheduling Mechanism for a Multiprocessor Embedded System Proceedings of he 6h WSEAS Inernaional Conference on Applied Compuer Science, Tenerife, Canary Islands, Spain, December 16-18, 2006 346 User Adjusable Process Scheduling Mechanism for a Muliprocessor Embedded

More information

Robot localization under perceptual aliasing conditions based on laser reflectivity using particle filter

Robot localization under perceptual aliasing conditions based on laser reflectivity using particle filter Robo localizaion under percepual aliasing condiions based on laser refleciviy using paricle filer DongXiang Zhang, Ryo Kurazume, Yumi Iwashia, Tsuomu Hasegawa Absrac Global localizaion, which deermines

More information

LAMP: 3D Layered, Adaptive-resolution and Multiperspective Panorama - a New Scene Representation

LAMP: 3D Layered, Adaptive-resolution and Multiperspective Panorama - a New Scene Representation Submission o Special Issue of CVIU on Model-based and Image-based 3D Scene Represenaion for Ineracive Visualizaion LAMP: 3D Layered, Adapive-resoluion and Muliperspecive Panorama - a New Scene Represenaion

More information

Moving Object Detection Using MRF Model and Entropy based Adaptive Thresholding

Moving Object Detection Using MRF Model and Entropy based Adaptive Thresholding Moving Objec Deecion Using MRF Model and Enropy based Adapive Thresholding Badri Narayan Subudhi, Pradipa Kumar Nanda and Ashish Ghosh Machine Inelligence Uni, Indian Saisical Insiue, Kolkaa, 700108, India,

More information

Less Pessimistic Worst-Case Delay Analysis for Packet-Switched Networks

Less Pessimistic Worst-Case Delay Analysis for Packet-Switched Networks Less Pessimisic Wors-Case Delay Analysis for Packe-Swiched Neworks Maias Wecksén Cenre for Research on Embedded Sysems P O Box 823 SE-31 18 Halmsad maias.wecksen@hh.se Magnus Jonsson Cenre for Research

More information

A Fast Stereo-Based Multi-Person Tracking using an Approximated Likelihood Map for Overlapping Silhouette Templates

A Fast Stereo-Based Multi-Person Tracking using an Approximated Likelihood Map for Overlapping Silhouette Templates A Fas Sereo-Based Muli-Person Tracking using an Approximaed Likelihood Map for Overlapping Silhouee Templaes Junji Saake Jun Miura Deparmen of Compuer Science and Engineering Toyohashi Universiy of Technology

More information

Interactive Graphical Systems HT2005

Interactive Graphical Systems HT2005 Ineracive Graphical Ssems HT25 Lesson 2 : Graphics Primer Sefan Seipel Sefan Seipel, Deparmen of Informaion Technolog, Uppsala Universi Ke issues of his lecure Represenaions of 3D models Repeiion of basic

More information

Real-Time Non-Rigid Multi-Frame Depth Video Super-Resolution

Real-Time Non-Rigid Multi-Frame Depth Video Super-Resolution Real-Time Non-Rigid Muli-Frame Deph Video Super-Resoluion Kassem Al Ismaeil 1, Djamila Aouada 1, Thomas Solignac 2, Bruno Mirbach 2, Björn Oersen 1 1 Inerdisciplinary Cenre for Securiy, Reliabiliy, and

More information

Visual Perception as Bayesian Inference. David J Fleet. University of Toronto

Visual Perception as Bayesian Inference. David J Fleet. University of Toronto Visual Percepion as Bayesian Inference David J Flee Universiy of Torono Basic rules of probabiliy sum rule (for muually exclusive a ): produc rule (condiioning): independence (def n ): Bayes rule: marginalizaion:

More information

COSC 3213: Computer Networks I Chapter 6 Handout # 7

COSC 3213: Computer Networks I Chapter 6 Handout # 7 COSC 3213: Compuer Neworks I Chaper 6 Handou # 7 Insrucor: Dr. Marvin Mandelbaum Deparmen of Compuer Science York Universiy F05 Secion A Medium Access Conrol (MAC) Topics: 1. Muliple Access Communicaions:

More information

Dynamic Route Planning and Obstacle Avoidance Model for Unmanned Aerial Vehicles

Dynamic Route Planning and Obstacle Avoidance Model for Unmanned Aerial Vehicles Volume 116 No. 24 2017, 315-329 ISSN: 1311-8080 (prined version); ISSN: 1314-3395 (on-line version) url: hp://www.ijpam.eu ijpam.eu Dynamic Roue Planning and Obsacle Avoidance Model for Unmanned Aerial

More information

PART 1 REFERENCE INFORMATION CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONITOR

PART 1 REFERENCE INFORMATION CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONITOR . ~ PART 1 c 0 \,).,,.,, REFERENCE NFORMATON CONTROL DATA 6400 SYSTEMS CENTRAL PROCESSOR MONTOR n CONTROL DATA 6400 Compuer Sysems, sysem funcions are normally handled by he Monior locaed in a Peripheral

More information

IROS 2015 Workshop on On-line decision-making in multi-robot coordination (DEMUR 15)

IROS 2015 Workshop on On-line decision-making in multi-robot coordination (DEMUR 15) IROS 2015 Workshop on On-line decision-making in muli-robo coordinaion () OPTIMIZATION-BASED COOPERATIVE MULTI-ROBOT TARGET TRACKING WITH REASONING ABOUT OCCLUSIONS KAROL HAUSMAN a,, GREGORY KAHN b, SACHIN

More information

Petri Nets for Object-Oriented Modeling

Petri Nets for Object-Oriented Modeling Peri Nes for Objec-Oriened Modeling Sefan Wi Absrac Ensuring he correcness of concurren rograms is difficul since common aroaches for rogram design do no rovide aroriae mehods This aer gives a brief inroducion

More information

Learning Topological Image Transforms Using Cellular Simultaneous Recurrent Networks

Learning Topological Image Transforms Using Cellular Simultaneous Recurrent Networks Proceedings of Inernaional Join Conference on Neural Neworks Dallas Texas USA Augus 4-9 013 Learning Topological Image Transforms Using Cellular Simulaneous Recurren Neworks J. Keih Anderson Deparmen of

More information

Assignment 2. Due Monday Feb. 12, 10:00pm.

Assignment 2. Due Monday Feb. 12, 10:00pm. Faculy of rs and Science Universiy of Torono CSC 358 - Inroducion o Compuer Neworks, Winer 218, LEC11 ssignmen 2 Due Monday Feb. 12, 1:pm. 1 Quesion 1 (2 Poins): Go-ack n RQ In his quesion, we review how

More information

A Face Detection Method Based on Skin Color Model

A Face Detection Method Based on Skin Color Model A Face Deecion Mehod Based on Skin Color Model Dazhi Zhang Boying Wu Jiebao Sun Qinglei Liao Deparmen of Mahemaics Harbin Insiue of Technology Harbin China 150000 Zhang_dz@163.com mahwby@hi.edu.cn sunjiebao@om.com

More information

Handling uncertainty in semantic information retrieval process

Handling uncertainty in semantic information retrieval process Handling uncerainy in semanic informaion rerieval process Chkiwa Mounira 1, Jedidi Anis 1 and Faiez Gargouri 1 1 Mulimedia, InfoRmaion sysems and Advanced Compuing Laboraory Sfax Universiy, Tunisia m.chkiwa@gmail.com,

More information

A Bayesian Approach to Video Object Segmentation via Merging 3D Watershed Volumes

A Bayesian Approach to Video Object Segmentation via Merging 3D Watershed Volumes A Bayesian Approach o Video Objec Segmenaion via Merging 3D Waershed Volumes Yu-Pao Tsai 1,3, Chih-Chuan Lai 1,2, Yi-Ping Hung 1,2, and Zen-Chung Shih 3 1 Insiue of Informaion Science, Academia Sinica,

More information

In fmri a Dual Echo Time EPI Pulse Sequence Can Induce Sources of Error in Dynamic Magnetic Field Maps

In fmri a Dual Echo Time EPI Pulse Sequence Can Induce Sources of Error in Dynamic Magnetic Field Maps In fmri a Dual Echo Time EPI Pulse Sequence Can Induce Sources of Error in Dynamic Magneic Field Maps A. D. Hahn 1, A. S. Nencka 1 and D. B. Rowe 2,1 1 Medical College of Wisconsin, Milwaukee, WI, Unied

More information

Video Content Description Using Fuzzy Spatio-Temporal Relations

Video Content Description Using Fuzzy Spatio-Temporal Relations Proceedings of he 4s Hawaii Inernaional Conference on Sysem Sciences - 008 Video Conen Descripion Using Fuzzy Spaio-Temporal Relaions rchana M. Rajurkar *, R.C. Joshi and Sananu Chaudhary 3 Dep of Compuer

More information

MOBILE COMPUTING. Wi-Fi 9/20/15. CSE 40814/60814 Fall Wi-Fi:

MOBILE COMPUTING. Wi-Fi 9/20/15. CSE 40814/60814 Fall Wi-Fi: MOBILE COMPUTING CSE 40814/60814 Fall 2015 Wi-Fi Wi-Fi: name is NOT an abbreviaion play on Hi-Fi (high fideliy) Wireless Local Area Nework (WLAN) echnology WLAN and Wi-Fi ofen used synonymous Typically

More information

MOBILE COMPUTING 3/18/18. Wi-Fi IEEE. CSE 40814/60814 Spring 2018

MOBILE COMPUTING 3/18/18. Wi-Fi IEEE. CSE 40814/60814 Spring 2018 MOBILE COMPUTING CSE 40814/60814 Spring 2018 Wi-Fi Wi-Fi: name is NOT an abbreviaion play on Hi-Fi (high fideliy) Wireless Local Area Nework (WLAN) echnology WLAN and Wi-Fi ofen used synonymous Typically

More information

Quantitative macro models feature an infinite number of periods A more realistic (?) view of time

Quantitative macro models feature an infinite number of periods A more realistic (?) view of time INFINIE-HORIZON CONSUMPION-SAVINGS MODEL SEPEMBER, Inroducion BASICS Quaniaive macro models feaure an infinie number of periods A more realisic (?) view of ime Infinie number of periods A meaphor for many

More information

Systems & Biomedical Engineering Department. Transformation

Systems & Biomedical Engineering Department. Transformation Sem & Biomedical Engineering Deparmen SBE 36B: Compuer Sem III Compuer Graphic Tranformaion Dr. Aman Eldeib Spring 28 Tranformaion Tranformaion i a fundamenal corner one of compuer graphic and i a cenral

More information

Algorithm for image reconstruction in multi-slice helical CT

Algorithm for image reconstruction in multi-slice helical CT Algorihm for image reconsrucion in muli-slice helical CT Kasuyuki Taguchi a) and Hiroshi Aradae Medical Engineering Laboraory, Toshiba Corporaion, 1385 Shimoishigami, Oawara, Tochigi 324-855, Japan Received

More information

Streamline Pathline Eulerian Lagrangian

Streamline Pathline Eulerian Lagrangian Sreamline Pahline Eulerian Lagrangian Sagnaion Poin Flow V V V = + = + = + o V xi y j a V V xi y j o Pahline and Sreakline Insananeous Sreamlines Pahlines Sreaklines Maerial Derivaive Acceleraion

More information

Optimal Crane Scheduling

Optimal Crane Scheduling Opimal Crane Scheduling Samid Hoda, John Hooker Laife Genc Kaya, Ben Peerson Carnegie Mellon Universiy Iiro Harjunkoski ABB Corporae Research EWO - 13 November 2007 1/16 Problem Track-mouned cranes move

More information

Wheelchair-user Detection Combined with Parts-based Tracking

Wheelchair-user Detection Combined with Parts-based Tracking Wheelchair-user Deecion Combined wih Pars-based Tracking Ukyo Tanikawa 1, Yasuomo Kawanishi 1, Daisuke Deguchi 2,IchiroIde 1, Hiroshi Murase 1 and Ryo Kawai 3 1 Graduae School of Informaion Science, Nagoya

More information

CS 428: Fall Introduction to. Geometric Transformations (continued) Andrew Nealen, Rutgers, /20/2010 1

CS 428: Fall Introduction to. Geometric Transformations (continued) Andrew Nealen, Rutgers, /20/2010 1 CS 428: Fall 2 Inroducion o Compuer Graphic Geomeric Tranformaion (coninued) Andrew Nealen, Ruger, 2 9/2/2 Tranlaion Tranlaion are affine ranformaion The linear par i he ideni mari The 44 mari for he ranlaion

More information

Rendering Pipeline/ OpenGL

Rendering Pipeline/ OpenGL Grading Programming Assignmens: 4% Chaper 2 2D Game: Inro o (6%) ou now 3D Transformaions modeling/animaion (11%) Basics of Compuer Graphics: Rendering pipeline (11%) Ray racing (12%) compuer graphics

More information

Robust Multi-view Face Detection Using Error Correcting Output Codes

Robust Multi-view Face Detection Using Error Correcting Output Codes Robus Muli-view Face Deecion Using Error Correcing Oupu Codes Hongming Zhang,2, Wen GaoP P, Xilin Chen 2, Shiguang Shan 2, and Debin Zhao Deparmen of Compuer Science and Engineering, Harbin Insiue of Technolog

More information

Rao-Blackwellized Particle Filtering for Probing-Based 6-DOF Localization in Robotic Assembly

Rao-Blackwellized Particle Filtering for Probing-Based 6-DOF Localization in Robotic Assembly MITSUBISHI ELECTRIC RESEARCH LABORATORIES hp://www.merl.com Rao-Blackwellized Paricle Filering for Probing-Based 6-DOF Localizaion in Roboic Assembly Yuichi Taguchi, Tim Marks, Haruhisa Okuda TR1-8 June

More information

X-Splines : A Spline Model Designed for the End-User

X-Splines : A Spline Model Designed for the End-User X-Splines : A Spline Model Designed for he End-User Carole Blanc Chrisophe Schlic LaBRI 1 cours de la libéraion, 40 alence (France) [blancjschlic]@labri.u-bordeaux.fr Absrac his paper presens a new model

More information

Analysis of Various Types of Bugs in the Object Oriented Java Script Language Coding

Analysis of Various Types of Bugs in the Object Oriented Java Script Language Coding Indian Journal of Science and Technology, Vol 8(21), DOI: 10.17485/ijs/2015/v8i21/69958, Sepember 2015 ISSN (Prin) : 0974-6846 ISSN (Online) : 0974-5645 Analysis of Various Types of Bugs in he Objec Oriened

More information

Learning nonlinear appearance manifolds for robot localization

Learning nonlinear appearance manifolds for robot localization Learning nonlinear appearance manifolds for robo localizaion Jihun Hamm, Yuanqing Lin, and Daniel. D. Lee GRAS Lab, Deparmen of Elecrical and Sysems Engineering Universiy of ennsylvania, hiladelphia, A

More information

Simultaneous Precise Solutions to the Visibility Problem of Sculptured Models

Simultaneous Precise Solutions to the Visibility Problem of Sculptured Models Simulaneous Precise Soluions o he Visibiliy Problem of Sculpured Models Joon-Kyung Seong 1, Gershon Elber 2, and Elaine Cohen 1 1 Universiy of Uah, Sal Lake Ciy, UT84112, USA, seong@cs.uah.edu, cohen@cs.uah.edu

More information

Video-Based Face Recognition Using Probabilistic Appearance Manifolds

Video-Based Face Recognition Using Probabilistic Appearance Manifolds Video-Based Face Recogniion Using Probabilisic Appearance Manifolds Kuang-Chih Lee Jeffrey Ho Ming-Hsuan Yang David Kriegman klee10@uiuc.edu jho@cs.ucsd.edu myang@honda-ri.com kriegman@cs.ucsd.edu Compuer

More information

Test - Accredited Configuration Engineer (ACE) Exam - PAN-OS 6.0 Version

Test - Accredited Configuration Engineer (ACE) Exam - PAN-OS 6.0 Version Tes - Accredied Configuraion Engineer (ACE) Exam - PAN-OS 6.0 Version ACE Exam Quesion 1 of 50. Which of he following saemens is NOT abou Palo Alo Neworks firewalls? Sysem defauls may be resored by performing

More information

Simple Network Management Based on PHP and SNMP

Simple Network Management Based on PHP and SNMP Simple Nework Managemen Based on PHP and SNMP Krasimir Trichkov, Elisavea Trichkova bsrac: This paper aims o presen simple mehod for nework managemen based on SNMP - managemen of Cisco rouer. The paper

More information

Effects needed for Realism. Ray Tracing. Ray Tracing: History. Outline. Foundations of Computer Graphics (Fall 2012)

Effects needed for Realism. Ray Tracing. Ray Tracing: History. Outline. Foundations of Computer Graphics (Fall 2012) Foundaions of ompuer Graphics (Fall 2012) S 184, Lecure 16: Ray Tracing hp://ins.eecs.berkeley.edu/~cs184 Effecs needed for Realism (Sof) Shadows Reflecions (Mirrors and Glossy) Transparency (Waer, Glass)

More information

Real time 3D face and facial feature tracking

Real time 3D face and facial feature tracking J Real-Time Image Proc (2007) 2:35 44 DOI 10.1007/s11554-007-0032-2 ORIGINAL RESEARCH PAPER Real ime 3D face and facial feaure racking Fadi Dornaika Æ Javier Orozco Received: 23 November 2006 / Acceped:

More information