Bilateral Mesh Denoising

Size: px
Start display at page:

Download "Bilateral Mesh Denoising"

Transcription

1 Outlne Blateral Meh Denong S. Flehman, I. Dror,, D. Cohen-Or Tel Avv Unverty Preented by Derek Bradley Motvaton Prevou ork Blateral Meh Denong Image Proeng Bakground Blateral Image Flterng Tranformng from Image to Mehe Meh Denong Reult Duon (Unle otherwe noted, all mage are from Flehman et al. Motvaton Prevou ork 3D annng reate noy mehe Smoothng an redue hgh frequeny noe Challenge: how do you know what noe and what are feature? Laplaan Smoothng v v + λ v v (, ( v v (, 3 4 Prevou ork Prevou ork eghted Laplaan Laplaan + Expanon v v v + λ v (, w (, w ( v v v v + µλ v (, ( v v (, ( µ λ v v 5 6

2 Prevou ork Blateral Meh Denong Implt Farng (IF [Debrun 999] Implt ntegraton of the dffuon equaton Applaton of an mage moothng tehnque Verte are moved along ther normal dreton X ( I + λdtl X ( I + λdtl X X n + n n+ n Explt Implt Anotrop Feature-Preervng Denong (AFP [Debrun 000] Feature deteted ung loal urvature Denoe ung weghted mean urvature moothng Penalze verte wth large rato between prnple urvature v v + d n Salar value d to be omputed for eah vertex Feature preervng Can be teratve or ngle-pa But frt ome mage proeng ba 7 8 Image Proeng Bakground Image Proeng Bakground An mage an array of nteger (0-55 [Toma and Manduh] v the urrent pxel N(v the et of neghbourng pxel of v I(v the ntenty of v 9 0 Blateral Image Flterng Goal: Smooth the mage ntente, but preerve trong edge (feature New ntenty weghted average of neghbour Two weght: Geometr: : Cloer pxel weghted hgher (loene moothng flter Photometr: : Strong hange n ntenty penalzed (mlarty weght funton Blateral Image Flterng Cloene Smoothng Flter d Gauan Funton [wkpeda.org]

3 Blateral Image Flterng Cloene Smoothng Flter d Gauan Flter Blateral Image Flterng Smlarty eght Funton Another Gauan funton x σ ( x e x σ ( x e x abolute dfferene n ntenty value Reult: pxel wth large hange n ntenty are weghted lower 3 4 Blateral Image Flterng Blateral Image Flterng Combnng the weght and normalzng: Iˆ( v p N ( v p N ( v ( p v ( I( v I( p I( p ( p v ( I( v I( p Reult: In prate, N(v defned by the et of pont: { q}, where v q < σ [Toma and Manduh] 5 6 Tranformng from Image to Mehe Tranformng from Image to Mehe Verte ntead of pxel Neghbourhood N(v,, defned the ame Cloene moothng flter: 3D Euldean dtane ntead of D Smlarty weght funton: Heght of neghbourng verte pxel ntente Dot produt between normal and (v-q( ued ntead of omputng the heght at q 7 8 3

4 Meh Denong Reult DenoePont(Vertex DenoePont(Vertex v, Normal n {q} neghbourhood(v neghbourhood(v K {q {q} um 0, normalzer 0 for : to K t v - q h <n, v - q> exp(exp(-t/(σ exp(exp(-h/(σ um + ( ( * * h normalzer + * end return Vertex v v + n * (um/normalzer (um/normalzer Mean Curvature Implt Farng 9 Blateral Denong Reult 0 Duon Iue when ung an magemage-baed tehnque on a meh: Only apple to manfold mehe Irregularty of mehe Shrnkage Vertex drft Handlng boundare Anotrop Denong of Heght Feld (AFP Blateral Denong Mrror neghbour at boundary verte Vrtual verte at nfnty (ued n th algorthm Duon Duon Settng the parameter (σ (σ, σ, # teraton UerUer-ated method σ and σ : Uer Independently, Jone et al. preent the ame algorthm wth mnor dfferene: elet mooth pont and radu on the meh Large σ few teraton, mall σ more teraton Small σ make ene large value an ro Surfae predtor Sngle pa feature lead to fater teraton maller neghbourhood < 6 teraton for all reult n the paper 3 Jone et al. Blateral Denong 4 4

5 Duon Dadvantage Aume well-behaved mehe Can reult n elf-ntereton Conluon Smple, effetve and fat algorthm for denong mehe Eay to mplement Take advantage of the ue of an mage proeng tehnque ould I mplement th algorthm? 5 6 Referene S. Flehman, I. Dror,, D. Cohen-Or. Blateral meh denong. SIGGRAPH 003. C. Toma,, R. Manduh.. Blateral flterng for gray and olor mage. ICCV 998. T. Jone, F. Durand, M. Debrun.. Non-teratve feature- preervng meh moothng. SIGGRAPH 003. M. Debrun,, M. Meyer, P. Shroder,, A.H. Barr. Implt farng of rregular mehe ung dffuon and urvature flow. SIGGRAPH 999. M. Debrun,, M. Meyer, P. Shroder,, A.H. Barr. Anotrop feature-preervng denong of heght feld and bvarate data. Graph Interfae

Indoor rigid sphere recognition based on 3D point cloud data

Indoor rigid sphere recognition based on 3D point cloud data Indoor rgd phere reognton baed on 3D pont loud data Jfang Duan, Khan Lahhan, Had Baghah, Eero Wllman, Davd R. Selvah Department of Eletron and Eletral Engneerng Unverty College London (UCL) Torrngton Plae

More information

Filling Holes in Triangular Meshes in Engineering

Filling Holes in Triangular Meshes in Engineering JOURNAL OF SOFTWARE, VOL. 7, NO. 1, JANUARY 2012 141 Fllng Hole n Trangular Mehe n Engneerng Png Hu Shool of Automote Engneerng, Faulty of Vehle Engneerng and Mehan, State Key Laboratory of Strutural Analy

More information

Forward Kinematics 1

Forward Kinematics 1 Forward Knemat lnk 2 Lnk and Jont jont 3 jont 4 jont n- jont n jont lnk 3... lnk n- lnk lnk n jont 2 lnk n jont, n + lnk lnk fxed (the bae) q d revolute prmat jont onnet lnk to lnk lnk move when jont atuated

More information

Recap: rigid motions

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

More information

Feature-Preserving Mesh Denoising via Bilateral Normal Filtering

Feature-Preserving Mesh Denoising via Bilateral Normal Filtering Feature-Preservng Mesh Denosng va Blateral Normal Flterng Ka-Wah Lee, Wen-Png Wang Computer Graphcs Group Department of Computer Scence, The Unversty of Hong Kong kwlee@cs.hku.hk, wenpng@cs.hku.hk Abstract

More information

Matrix-Matrix Multiplication Using Systolic Array Architecture in Bluespec

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

More information

Image Representation & Visualization Basic Imaging Algorithms Shape Representation and Analysis. outline

Image Representation & Visualization Basic Imaging Algorithms Shape Representation and Analysis. outline mage Vsualzaton mage Vsualzaton mage Representaton & Vsualzaton Basc magng Algorthms Shape Representaton and Analyss outlne mage Representaton & Vsualzaton Basc magng Algorthms Shape Representaton and

More information

A Nodes Deployment Algorithm in Wireless Sensor Network Based on Distribution

A Nodes Deployment Algorithm in Wireless Sensor Network Based on Distribution Senor & Tranduer 014 by IFSA Publhng, S. L. http://www.enorportal.om A Node Deployment Algorthm n Wrele Senor Network Baed on Dtrbuton Song Yul, Zhang Hongdong Qngdao Voatonal and Tehnal College of Hotel

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

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

More information

Fitting: Deformable contours April 26 th, 2018

Fitting: Deformable contours April 26 th, 2018 4/6/08 Fttng: Deformable contours Aprl 6 th, 08 Yong Jae Lee UC Davs Recap so far: Groupng and Fttng Goal: move from array of pxel values (or flter outputs) to a collecton of regons, objects, and shapes.

More information

Active Contours/Snakes

Active Contours/Snakes Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng

More information

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

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

More information

Lecture notes: Histogram, convolution, smoothing

Lecture notes: Histogram, convolution, smoothing Lecture notes: Hstogram, convoluton, smoothng Hstogram. A plot o the ntensty dstrbuton n an mage. requency (# occurrences) ntensty The ollowng shows an example mage and ts hstogram: I we denote a greyscale

More information

Graph-Based Fast Image Segmentation

Graph-Based Fast Image Segmentation Graph-Baed Fat Image Segmentaton Dongfeng Han, Wenhu L, Xaouo Lu, Ln L, and Y Wang College of Computer Scence and Technology, Key Laboratory of Symbol Computaton and Knowledge Engneerng of the Mntry of

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

Low Complexity Sphere Decoding Algorithms

Low Complexity Sphere Decoding Algorithms Low Complexty Sphere Decodng Algorthm Ramn Sharat-Yazd and ad Kwanewk Department of Electronc, Carleton Unverty Ottawa, Canada ryazd@doe.carleton.ca Abtract he complex phere decodng algorthm ha optmal

More information

SIGGRAPH Interactive Image Cutout. Interactive Graph Cut. Interactive Graph Cut. Interactive Graph Cut. Hard Constraints. Lazy Snapping.

SIGGRAPH Interactive Image Cutout. Interactive Graph Cut. Interactive Graph Cut. Interactive Graph Cut. Hard Constraints. Lazy Snapping. SIGGRAPH 004 Interactve Image Cutout Lazy Snappng Yn L Jan Sun Ch-Keung Tang Heung-Yeung Shum Mcrosoft Research Asa Hong Kong Unversty Separate an object from ts background Compose the object on another

More information

Touring a Sequence of Polygons

Touring a Sequence of Polygons Tourng a Sequence of Polygon Mohe Dror Alon Efrat Anna Lubw Joe Mtchell Unverty of Arzona Unverty of Arzona Unverty of Waterloo Stony Brook Unverty Tourng Polygon Problem Gven: a equence of convex polygon,

More information

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

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

More information

Interface Tracking in Eulerian and MMALE Calculations

Interface Tracking in Eulerian and MMALE Calculations Interface Tracking in Eulerian and MMALE Calculation Gabi Luttwak Rafael P.O.Box 2250, Haifa 31021,Irael Interface Tracking in Eulerian and MMALE Calculation 3D Volume of Fluid (VOF) baed recontruction

More information

Distance based similarity measures of fuzzy sets

Distance based similarity measures of fuzzy sets Johanyák, Z. C., Dr. Kovác Sz.: Dtance baed mlarty meaure of fuzzy et, SAMI 2005, 3rd Slovakan-Hungaran Jont Sympoum on Appled Machne Intellgence, Herl'any, Slovaka, January 2-22 2005, ISBN 963 75 35 3,

More information

LECTURE : MANIFOLD LEARNING

LECTURE : MANIFOLD LEARNING LECTURE : MANIFOLD LEARNING Rta Osadchy Some sldes are due to L.Saul, V. C. Raykar, N. Verma Topcs PCA MDS IsoMap LLE EgenMaps Done! Dmensonalty Reducton Data representaton Inputs are real-valued vectors

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

AVideoStabilizationMethodbasedonInterFrameImageMatchingScore

AVideoStabilizationMethodbasedonInterFrameImageMatchingScore Global Journal of Computer Sene and Tehnology: F Graphs & vson Volume 7 Issue Verson.0 Year 207 Type: Double Blnd Peer Revewed Internatonal Researh Journal Publsher: Global Journals In. (USA) Onlne ISSN:

More information

THE PULL-PUSH ALGORITHM REVISITED

THE PULL-PUSH ALGORITHM REVISITED THE PULL-PUSH ALGORITHM REVISITED Improvements, Computaton of Pont Denstes, and GPU Implementaton Martn Kraus Computer Graphcs & Vsualzaton Group, Technsche Unverstät München, Boltzmannstraße 3, 85748

More information

Session 4.2. Switching planning. Switching/Routing planning

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

More information

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

Anisotropic filtering on normal field and curvature tensor field using optimal estimation theory

Anisotropic filtering on normal field and curvature tensor field using optimal estimation theory Aniotropic filtering on normal field and curvature tenor field uing optimal etimation theory Min Liu Yuhen Liu and Karthik Ramani Purdue Univerity, Wet Lafayette, Indiana, USA Email: {liu66 liu28 ramani}@purdue.edu

More information

An efficient method to build panoramic image mosaics

An efficient method to build panoramic image mosaics An effcent method to buld panoramc mage mosacs Pattern Recognton Letters vol. 4 003 Dae-Hyun Km Yong-In Yoon Jong-Soo Cho School of Electrcal Engneerng and Computer Scence Kyungpook Natonal Unv. Abstract

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

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

1. Answer the following. a. A beam of vertically polarized light of intensity W/m2 encounters two polarizing filters as shown below.

1. Answer the following. a. A beam of vertically polarized light of intensity W/m2 encounters two polarizing filters as shown below. 1. Answer the followng. a. A beam of vertcally lght of ntensty 160.0 W/m2 encounters two polarzng flters as shown below. Vertcally ncdent tu-

More information

Markov Random Fields in Image Segmentation

Markov Random Fields in Image Segmentation Preented at SSIP 2011, Szeged, Hungary Markov Random Field in Image Segmentation Zoltan Kato Image Proceing & Computer Graphic Dept. Univerity of Szeged Hungary Zoltan Kato: Markov Random Field in Image

More information

A Probabilistic Approach to Detect Urban Regions from Remotely Sensed Images Based on Combination of Local Features

A Probabilistic Approach to Detect Urban Regions from Remotely Sensed Images Based on Combination of Local Features A Probablstc Approach to Detect Urban Regons from Remotely Sensed Images Based on Combnaton of Local Features Berl Sırmaçek German Aerospace Center (DLR) Remote Sensng Technology Insttute Weßlng, 82234,

More information

Harmonic Coordinates for Character Articulation PIXAR

Harmonic Coordinates for Character Articulation PIXAR Harmonc Coordnates for Character Artculaton PIXAR Pushkar Josh Mark Meyer Tony DeRose Bran Green Tom Sanock We have a complex source mesh nsde of a smpler cage mesh We want vertex deformatons appled to

More information

Automated System for Criminal Identification Using Fingerprint Clue Found at Crime Scene

Automated System for Criminal Identification Using Fingerprint Clue Found at Crime Scene Volume 2, Issue 11, November 213 ISSN 2319-4847 Automated System for Crmnal Identfcaton Usng Fngerprnt Clue Found at Crme Scene Mansh P.Deshmukh 1, Prof (Dr) Pradeep M.Patl 2 1 Assocate Professor, E&TC

More information

A Novel Accurate Algorithm to Ellipse Fitting for Iris Boundary Using Most Iris Edges. Mohammad Reza Mohammadi 1, Abolghasem Raie 2

A Novel Accurate Algorithm to Ellipse Fitting for Iris Boundary Using Most Iris Edges. Mohammad Reza Mohammadi 1, Abolghasem Raie 2 A Novel Accurate Algorthm to Ellpse Fttng for Irs Boundar Usng Most Irs Edges Mohammad Reza Mohammad 1, Abolghasem Rae 2 1. Department of Electrcal Engneerng, Amrabr Unverst of Technolog, Iran. mrmohammad@aut.ac.r

More information

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

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

More information

Robust Denoising of Point-Sampled Surfaces

Robust Denoising of Point-Sampled Surfaces Jfang L, Renfang Wang Robust Denosng of Pont-Sampled Surfaces Jfang L, Renfang Wang College of Computer Scence and Informaton Technology Zhejang Wanl Unversty Nngbo 315100, Chna Abstract: - Based on samplng

More information

Inverse Kinematics 1 1/29/2018

Inverse Kinematics 1 1/29/2018 Invere Kinemati 1 Invere Kinemati 2 given the poe of the end effetor, find the joint variable that produe the end effetor poe for a -joint robot, given find 1 o R T 3 2 1,,,,, q q q q q q RPP + Spherial

More information

The use of the concept of vague environment in approximate fuzzy reasoning

The use of the concept of vague environment in approximate fuzzy reasoning Kovác, Sz., Kóczy,.T.: The ue of the concept of vague envronment n approxmate fuzzy reaonng, Fuzzy Set Theory and pplcaton, Tatra Mountan Mathematcal Publcaton, Mathematcal Inttute Slovak cademy of Scence,

More information

Fuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches

Fuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches Proceedngs of the Internatonal Conference on Cognton and Recognton Fuzzy Flterng Algorthms for Image Processng: Performance Evaluaton of Varous Approaches Rajoo Pandey and Umesh Ghanekar Department of

More information

Fitting & Matching. Lecture 4 Prof. Bregler. Slides from: S. Lazebnik, S. Seitz, M. Pollefeys, A. Effros.

Fitting & Matching. Lecture 4 Prof. Bregler. Slides from: S. Lazebnik, S. Seitz, M. Pollefeys, A. Effros. Fttng & Matchng Lecture 4 Prof. Bregler Sldes from: S. Lazebnk, S. Setz, M. Pollefeys, A. Effros. How do we buld panorama? We need to match (algn) mages Matchng wth Features Detect feature ponts n both

More information

Similarity-based denoising of point-sampled surfaces *

Similarity-based denoising of point-sampled surfaces * Wang et al. / J Zhejang Unv Sc A 008 9(6):807-85 807 Journal of Zhejang Unversty SCIENCE A ISSN 673-565X (Prnt); ISSN 86-775 (Onlne) www.zju.edu.cn/jzus; www.sprngerlnk.com E-mal: jzus@zju.edu.cn Smlarty-based

More information

Avatar Face Recognition using Wavelet Transform and Hierarchical Multi-scale LBP

Avatar Face Recognition using Wavelet Transform and Hierarchical Multi-scale LBP 2011 10th Internatonal Conferene on Mahne Learnng and Applatons Avatar Fae Reognton usng Wavelet Transform and Herarhal Mult-sale LBP Abdallah A. Mohamed, Darryl D Souza, Naouel Bal and Roman V. Yampolsky

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

Generating Fuzzy Term Sets for Software Project Attributes using and Real Coded Genetic Algorithms

Generating Fuzzy Term Sets for Software Project Attributes using and Real Coded Genetic Algorithms Generatng Fuzzy Ter Sets for Software Proect Attrbutes usng Fuzzy C-Means C and Real Coded Genetc Algorths Al Idr, Ph.D., ENSIAS, Rabat Alan Abran, Ph.D., ETS, Montreal Azeddne Zah, FST, Fes Internatonal

More information

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

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

More information

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

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

12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification

12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification Introducton to Artfcal Intellgence V22.0472-001 Fall 2009 Lecture 24: Nearest-Neghbors & Support Vector Machnes Rob Fergus Dept of Computer Scence, Courant Insttute, NYU Sldes from Danel Yeung, John DeNero

More information

Stereo Depth Continuity

Stereo Depth Continuity Stereo Depth Contnuty Steven Damond (stevend@stanford.edu), Jessca Taylor (jacobt@stanford.edu) March 17, 014 1 Abstract We tackle the problem of producng depth maps from stereo vdeo. Some algorthms for

More information

ANALYSIS OF ADAPTIF LOCAL REGION IMPLEMENTATION ON LOCAL THRESHOLDING METHOD

ANALYSIS OF ADAPTIF LOCAL REGION IMPLEMENTATION ON LOCAL THRESHOLDING METHOD Nusantara Journal of Computers and ts Applcatons ANALYSIS F ADAPTIF LCAL REGIN IMPLEMENTATIN N LCAL THRESHLDING METHD I Gust Agung Socrates Ad Guna 1), Hendra Maulana 2), Agus Zanal Arfn 3) and Dn Adn

More information

MULTIPLE OBJECT DETECTION AND TRACKING IN SONAR MOVIES USING AN IMPROVED TEMPORAL DIFFERENCING APPROACH AND TEXTURE ANALYSIS

MULTIPLE OBJECT DETECTION AND TRACKING IN SONAR MOVIES USING AN IMPROVED TEMPORAL DIFFERENCING APPROACH AND TEXTURE ANALYSIS U.P.B. S. Bull., Seres A, Vol. 74, Iss. 2, 2012 ISSN 1223-7027 MULTIPLE OBJECT DETECTION AND TRACKING IN SONAR MOVIES USING AN IMPROVED TEMPORAL DIFFERENCING APPROACH AND TEXTURE ANALYSIS Tudor BARBU 1

More information

Feature-Preserving Denoising of Point-Sampled Surfaces

Feature-Preserving Denoising of Point-Sampled Surfaces Feature-Preservng Denosng of Pont-Sampled Surfaces Jfang L College of Computer Scence and Informaton Technology Zhejang Wanl Unversty Nngbo 315100 Chna Abstract: Based on samplng lkelhood and feature ntensty,

More information

Unsupervised Learning

Unsupervised Learning Pattern Recognton Lecture 8 Outlne Introducton Unsupervsed Learnng Parametrc VS Non-Parametrc Approach Mxture of Denstes Maxmum-Lkelhood Estmates Clusterng Prof. Danel Yeung School of Computer Scence and

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

Joint Example-based Depth Map Super-Resolution

Joint Example-based Depth Map Super-Resolution Jont Example-based Depth Map Super-Resoluton Yanje L 1, Tanfan Xue,3, Lfeng Sun 1, Janzhuang Lu,3,4 1 Informaton Scence and Technology Department, Tsnghua Unversty, Bejng, Chna Department of Informaton

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

New algorithms for satellite data verification with and without the use of the imaged area vector data

New algorithms for satellite data verification with and without the use of the imaged area vector data WSCG 2015 Conference on Computer Graphc, Vualzaton and Computer Von New algorthm for atellte data verfcaton wth and wthout the ue of the maged area vector data Andrey Kuznetov Samara State Aeropace Unverty

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

Image Alignment CSC 767

Image Alignment CSC 767 Image Algnment CSC 767 Image algnment Image from http://graphcs.cs.cmu.edu/courses/15-463/2010_fall/ Image algnment: Applcatons Panorama sttchng Image algnment: Applcatons Recognton of object nstances

More information

LOCAL BINARY PATTERNS AND ITS VARIANTS FOR FACE RECOGNITION

LOCAL BINARY PATTERNS AND ITS VARIANTS FOR FACE RECOGNITION IEEE-Internatonal Conferene on Reent Trends n Informaton Tehnology, ICRTIT 211 MIT, Anna Unversty, Chenna. June 3-5, 211 LOCAL BINARY PATTERNS AND ITS VARIANTS FOR FACE RECOGNITION K.Meena #1, Dr.A.Suruland

More information

Slide 1 SPH3UW: OPTICS I. Slide 2. Slide 3. Introduction to Mirrors. Light incident on an object

Slide 1 SPH3UW: OPTICS I. Slide 2. Slide 3. Introduction to Mirrors. Light incident on an object Slde 1 SPH3UW: OPTICS I Introducton to Mrrors Slde 2 Lght ncdent on an object Absorpton Relecton (bounces)** See t Mrrors Reracton (bends) Lenses Oten some o each Everythng true or wavelengths

More information

Motivation: Level Sets. Input Data Noisy. Easy Case Use Marching Cubes. Intensity Varies. Non-uniform Exposure. Roger Crawfis

Motivation: Level Sets. Input Data Noisy. Easy Case Use Marching Cubes. Intensity Varies. Non-uniform Exposure. Roger Crawfis Level Set Motivation: Roger Crawfi Slide collected from: Fan Ding, Charle Dyer, Donald Tanguay and Roger Crawfi 4/24/2003 R. Crawfi, Ohio State Univ. 109 Eay Cae Ue Marching Cube Input Data Noiy 4/24/2003

More information

Progressive Hedging In Parallel

Progressive Hedging In Parallel Progreve Hedgng In Parallel Mchael Somervell Department of Engneerng Scence Unverty of Auckland New Zealand Abtract The Progreve Hedgng Algorthm (PHA) a technque for olvng lnear dcrete tochatc program.

More information

An Improved Stereo Matching Algorithm Based on Guided Image Filter

An Improved Stereo Matching Algorithm Based on Guided Image Filter nd Internatonal Conference on Modellng, Identfcaton and Control (MIC 015 An Improved Stereo Matchng Algorthm Based on Guded Image Flter Rudong Gao, Yun Chen, Lna Yan School of nstrumentaton Scence and

More information

3D vector computer graphics

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

More information

Implementing Lattice Boltzmann Computation on Graphics Hardware

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

More information

Evaluation of Segmentation in Magnetic Resonance Images Using k-means and Fuzzy c-means Clustering Algorithms

Evaluation of Segmentation in Magnetic Resonance Images Using k-means and Fuzzy c-means Clustering Algorithms ELEKTROTEHIŠKI VESTIK 79(3): 129-134, 2011 EGLISH EDITIO Evaluaton of Segmentaton n Magnet Resonane Images Usng k-means and Fuzzy -Means Clusterng Algorthms Tomaž Fnkšt Unverza v Lublan, Fakulteta za stronštvo,

More information

Face Recognition University at Buffalo CSE666 Lecture Slides Resources:

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

More information

CleanUp: Improving Quadrilateral Finite Element Meshes

CleanUp: Improving Quadrilateral Finite Element Meshes CleanUp: Improving Quadrilateral Finite Element Meshes Paul Kinney MD-10 ECC P.O. Box 203 Ford Motor Company Dearborn, MI. 8121 (313) 28-1228 pkinney@ford.om Abstrat: Unless an all quadrilateral (quad)

More information

Dijkstra s Single Source Algorithm. All-Pairs Shortest Paths. Dynamic Programming Solution. Performance. Decision Sequence.

Dijkstra s Single Source Algorithm. All-Pairs Shortest Paths. Dynamic Programming Solution. Performance. Decision Sequence. All-Pars Shortest Paths Gven an n-vertex drected weghted graph, fnd a shortest path from vertex to vertex for each of the n vertex pars (,). Dstra s Sngle Source Algorthm Use Dstra s algorthm n tmes, once

More information

12. Segmentation. Computer Engineering, i Sejong University. Dongil Han

12. Segmentation. Computer Engineering, i Sejong University. Dongil Han Computer Vson 1. Segmentaton Computer Engneerng, Sejong Unversty Dongl Han Image Segmentaton t Image segmentaton Subdvdes an mage nto ts consttuent regons or objects - After an mage has been segmented,

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

Detecting Moving Targets in Clutter in Airborne SAR via Keystoning and Multiple Phase Center Interferometry

Detecting Moving Targets in Clutter in Airborne SAR via Keystoning and Multiple Phase Center Interferometry Deteting Moving Targets in Clutter in Airborne SAR via Keystoning and Multiple Phase Center Interferometry D. M. Zasada, P. K. Sanyal The MITRE Corp., 6 Eletroni Parkway, Rome, NY 134 (dmzasada, psanyal)@mitre.org

More information

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

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

More information

Outline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1

Outline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1 4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:

More information

Comparison between post-smoothed maximum-likelihood and penalized-likelihood for image reconstruction with uniform spatial resolution

Comparison between post-smoothed maximum-likelihood and penalized-likelihood for image reconstruction with uniform spatial resolution Comparson between post-smoothed maxmum-lkelhood and penalzed-lkelhood for mage reconstructon wth unform spatal resoluton Johan Nuyts, Jeffrey A. Fessler Abstract Regularzaton s desrable for mage reconstructon

More information

Random Variables and Probability Distributions

Random Variables and Probability Distributions Random Varables and Probablty Dstrbutons Some Prelmnary Informaton Scales on Measurement IE231 - Lecture Notes 5 Mar 14, 2017 Nomnal scale: These are categorcal values that has no relatonshp of order or

More information

A Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines

A Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines A Modfed Medan Flter for the Removal of Impulse Nose Based on the Support Vector Machnes H. GOMEZ-MORENO, S. MALDONADO-BASCON, F. LOPEZ-FERRERAS, M. UTRILLA- MANSO AND P. GIL-JIMENEZ Departamento de Teoría

More information

Dijkstra s Single Source Algorithm. All-Pairs Shortest Paths. Dynamic Programming Solution. Performance

Dijkstra s Single Source Algorithm. All-Pairs Shortest Paths. Dynamic Programming Solution. Performance All-Pars Shortest Paths Gven an n-vertex drected weghted graph, fnd a shortest path from vertex to vertex for each of the n vertex pars (,). Dkstra s Sngle Source Algorthm Use Dkstra s algorthm n tmes,

More information

Robust Model-based 3D Object Recognition by Combining Feature Matching with Tracking

Robust Model-based 3D Object Recognition by Combining Feature Matching with Tracking Proceedng of te 2003 IEEE Internatonal Conference on Robotc & Automaton Tape, Tawan, September 4-9, 2003 Robut Model-baed 3D Object Recognton by Combnng Feature Matcng wt Trackng Sungo Km, Ino Kweon Inceol

More information

Active Contour Models

Active Contour Models Actve Contour Models By Taen Lee A PROJECT submtted to Oregon State Unversty n partal fulfllment of The requrements for the Degree of Master of Scence n Computer Scence Presented September 9 005 Commencement

More information

A Model-Based Approach for Automated Feature Extraction in Fundus Images

A Model-Based Approach for Automated Feature Extraction in Fundus Images A Model-Based Approah for Automated Feature Extraton n Fundus Images Huq L Shool of Computng Natonal Unversty of Sngapore dslhq@nus.edu.sg Opas Chutatape Shool of Eletral and Eletron Engneerng Nanyang

More information

Scale Selective Extended Local Binary Pattern For Texture Classification

Scale Selective Extended Local Binary Pattern For Texture Classification Scale Selectve Extended Local Bnary Pattern For Texture Classfcaton Yutng Hu, Zhlng Long, and Ghassan AlRegb Multmeda & Sensors Lab (MSL) Georga Insttute of Technology 03/09/017 Outlne Texture Representaton

More information

A GENETIC APPROACH FOR THE AUTOMATIC ADAPTATION OF SEGMENTATION PARAMETERS

A GENETIC APPROACH FOR THE AUTOMATIC ADAPTATION OF SEGMENTATION PARAMETERS A GENETIC APPROACH FOR THE AUTOMATIC ADAPTATION OF SEGMENTATION PARAMETERS R. Q. Fetosa a, *, G. A. O. P. Costa a, T. B. Cazes a, B. Fejo b a Dept. of Eletral Engneerng, b Dept of Informats, Cathol Unversty

More information

Region Segmentation Readings: Chapter 10: 10.1 Additional Materials Provided

Region Segmentation Readings: Chapter 10: 10.1 Additional Materials Provided Regon Segmentaton Readngs: hater 10: 10.1 Addtonal Materals Provded K-means lusterng tet EM lusterng aer Grah Parttonng tet Mean-Shft lusterng aer 1 Image Segmentaton Image segmentaton s the oeraton of

More information

Machine Learning 9. week

Machine Learning 9. week Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below

More information

A Multi-step Strategy for Shape Similarity Search In Kamon Image Database

A Multi-step Strategy for Shape Similarity Search In Kamon Image Database A Mult-step Strategy for Shape Smlarty Search In Kamon Image Database Paul W.H. Kwan, Kazuo Torach 2, Kesuke Kameyama 2, Junbn Gao 3, Nobuyuk Otsu 4 School of Mathematcs, Statstcs and Computer Scence,

More information

FUZZY SEGMENTATION IN IMAGE PROCESSING

FUZZY SEGMENTATION IN IMAGE PROCESSING FUZZY SEGMENTATION IN IMAGE PROESSING uevas J. Er,, Zaldívar N. Danel,, Roas Raúl Free Unverstät Berln, Insttut für Inforat Tausstr. 9, D-495 Berln, Gerany. Tel. 0049-030-8385485, Fax. 0049-030-8387509

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

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

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

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

A Robust Algorithm for Text Detection in Color Images

A Robust Algorithm for Text Detection in Color Images A Robust Algorthm for Tet Deteton n Color Images Yangng LIU Satosh GOTO Takesh IKENAGA Abstrat Tet deteton n olor mages has beome an atve researh area sne reent deades. In ths paper we present a novel

More information

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

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

More information

ABHELSINKI UNIVERSITY OF TECHNOLOGY Networking Laboratory

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

More information

AUTOMATICALLY MULTIPLE FEATURES DETECTION OF FACE SKETCH BASED ON MAXIMUM LINE GRADIENT

AUTOMATICALLY MULTIPLE FEATURES DETECTION OF FACE SKETCH BASED ON MAXIMUM LINE GRADIENT AUTOMATICALLY MULTIPLE FEATURES DETECTION OF FACE SKETCH BASED ON MAXIMUM LINE GRADIENT Arf Muntasa, Mohamad Harad, Maurdh Her Purnomo 3,,3 Eletral Engneerng Department, Insttut Teknolog Sepuluh Nopember,

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