Outline. Seamless Image Stitching in the Gradient Domain. Related Approaches. Image Stitching. Introduction Related Work

Size: px
Start display at page:

Download "Outline. Seamless Image Stitching in the Gradient Domain. Related Approaches. Image Stitching. Introduction Related Work"

Transcription

1 Outlne Seamless Image Sttchng n the Gradent Doman Anat Levn, Assaf Zomet, Shmuel Peleg and Yar Wess ECCV 004 Presenter: Pn Wu Oct 007 Introducton Related Work GIST: Gradent-doman Image Sttchng GIST GIST Imlementaton detals Iteratve Otmzaton Exerments & Dscusson Panoramc Vew Obect Parts Image Sttchng Related Aroaches Combne ndvdual mages havng some overla nto a comoste mage Commonly used for generatng anoramc mages + Otmal seam algorthm Otmal seam [3,,4] Otmal seam curve that fts to the mnmal dfference Transton smoothng Featherng [6] or alha blendng Weghted combnaton wth coeffcent functons of dstance [] [6] Vsual artfcal edges at the seam due to the dfferences n Camera gan Scene llumnaton Geometrcal msalgnment 3 Pyramd Blendng [7] Dfferent alha masks n dfferent frequency bands Otmzaton rocess Posson Image Edtng [0] Otmzaton over the gradent doman Gradent-doman Image Sttchng(GIST) 4

2 Photometrc nconsstences Comarson of Sttchng Methods Requrement Good sttchng (seamless) The mosac should be as smlar as ossble to the nut mages, both geometrcally and hoto-metrcally. The seam between the sttched mages should be nvsble. Qualty measurement s oerated n gradent doman. Horzontal msalgnments Vertcal msalgnments 5 6 GIST: Gradent Image STtchng GIST: Otmzng a cost functon over Image Dervatves E ( Iˆ ; I, I, W ) = d ( Iˆ, I, τ ω, W ) + d ( Iˆ, I, τ ω, U W ), where d ( J, J, φ, W ) = W ( q) J ( q) J ( q) GIST: Gradent Image STtchng GIST: Sttchng Dervatve Images I I I I Comute deratves:,,, x y x y Form a feld F = ( F, F ), where x y I I I I Fx, Fy are formed wth sttchng, and, x x y y (Featherng, Pyramd blendng, or otmal seam) mnmze d ( I, F, π, U ) = U ( q) I ( q) F( q) q π f x 7 8

3 GIST Proertes Imlementaton Whch method to use? GIST under l s recommended GIST under l Solvng through FFT d ( J, J, φ, W ) = W ( q) J ( q) J ( q) GIST under l V.S otmal seam Same results n geometrc msalgnments condton GIST > otmal seam f no erfect seam, e.g. hotometrc nconsstences GIST under l V.S GIST under l GIST under l Featherng of the gradent mages (GIST) under l (soluton of Posson Equaton) l : tendng to mx the dervatves and hence blurrng n the overla regon. l : tendng to behave smlarly to the otmal seam methods. 9 GIST under l Solvng through Lnear Programmng Unform ntensty shft (nut mage, mosac mage) l Iteratve otmzaton Intal the soluton mage I Iterate untl convergence: For all x,y n the mage, d ( J, J, φ, W ) = W ( q) J ( q) J ( q) Mn : ( z + z ) Subect to : Ax + ( z + z ) = b, x 0, z 0, z 0 I( x +, y) Dx ( x, y), I ( x, y) + Dx ( x, y), I( x, y) * medan( { }) I ( x, y + ) Dy ( x, y), I ( x, y ) + Dy ( x, y ) 0 Dfferences wth Posson Edtng GIST use the gradents of both mages n the overla regon. The otmzaton s done under dfferent norms Performance + Overcome global nconsstences + Overcome Msalgnments - Seed - Convergence Image-ntensty methods V.S. Gradent-doman methods Double edge 3

4 Sttchng Panoramc Vew Sttchng Panoramc Vew (a) Otmal seam (b) Featherng (c) Pyramd blendng (d) Otmal seam (gradent) (e) Featherng (gradent) (f) Pyramd blendng (gradent) (g) Posson edtng (h) GIST under l (a) Otmal seam (b) Featherng (c) Pyramd blendng (d) Otmal seam (gradent) (e) Featherng (gradent) (f) Pyramd blendng (gradent) (g) Posson edtng (h) GIST under l 3 4 Sttchng Panoramc Vew Sttchng Panoramc Vew (a) Otmal seam (b) Featherng (c) Pyramd blendng (d) Otmal seam (gradent) (e) Featherng (gradent) (f) Pyramd blendng (gradent) (g) Posson edtng (h) GIST under l (a) Otmal seam (b) Featherng (c) Pyramd blendng (d) Otmal seam (gradent) (e) Featherng (gradent) (f) Pyramd blendng (gradent) (g) Posson edtng (h) GIST under l 5 6 4

5 Sttchng Obect Parts Sttchng Obect Parts Orgnal comoston GIST under l GIST under l Pyramd (gradent) 7 GIST under l GIST under l Pyramd (gradent) Pyramd (mage) 8 5

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

IMRT workflow. Optimization and Inverse planning. Intensity distribution IMRT IMRT. Dose optimization for IMRT. Bram van Asselen

IMRT workflow. Optimization and Inverse planning. Intensity distribution IMRT IMRT. Dose optimization for IMRT. Bram van Asselen IMRT workflow Otmzaton and Inverse lannng 69 Gy Bram van Asselen IMRT Intensty dstrbuton Webb 003: IMRT s the delvery of radaton to the atent va felds that have non-unform radaton fluence Purose: Fnd a

More information

Panorama Mosaic Optimization for Mobile Camera Systems

Panorama Mosaic Optimization for Mobile Camera Systems S. J. Ha et al.: Panorama Mosac Optmzaton for Moble Camera Systems Panorama Mosac Optmzaton for Moble Camera Systems Seong Jong Ha, Hyung Il Koo, Sang Hwa Lee, Nam Ik Cho, Soo Kyun Km, Member, IEEE 27

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

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

Stitching of off-axis sub-aperture null measurements of an aspheric surface

Stitching of off-axis sub-aperture null measurements of an aspheric surface Sttchng of off-axs sub-aperture null measurements of an aspherc surface Chunyu Zhao* and James H. Burge College of optcal Scences The Unversty of Arzona 1630 E. Unversty Blvd. Tucson, AZ 85721 ABSTRACT

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

CS 534: Computer Vision Model Fitting

CS 534: Computer Vision Model Fitting CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust

More information

Radial Basis Functions

Radial Basis Functions Radal Bass Functons Mesh Reconstructon Input: pont cloud Output: water-tght manfold mesh Explct Connectvty estmaton Implct Sgned dstance functon estmaton Image from: Reconstructon and Representaton of

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

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

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

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

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

Support Vector Machines. CS534 - Machine Learning

Support Vector Machines. CS534 - Machine Learning Support Vector Machnes CS534 - Machne Learnng Perceptron Revsted: Lnear Separators Bnar classfcaton can be veed as the task of separatng classes n feature space: b > 0 b 0 b < 0 f() sgn( b) Lnear Separators

More information

GSLM Operations Research II Fall 13/14

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

More information

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

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

Fitting and Alignment

Fitting and Alignment Fttng and Algnment Computer Vson Ja-Bn Huang, Vrgna Tech Many sldes from S. Lazebnk and D. Hoem Admnstratve Stuffs HW 1 Competton: Edge Detecton Submsson lnk HW 2 wll be posted tonght Due Oct 09 (Mon)

More information

An Image Fusion Approach Based on Segmentation Region

An Image Fusion Approach Based on Segmentation Region Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

Image Segmentation. Image Segmentation

Image Segmentation. Image Segmentation Image Segmentaton REGION ORIENTED SEGMENTATION Let R reresent the entre mage regon. Segmentaton may be vewed as a rocess that arttons R nto n subregons, R, R,, Rn,such that n= R = R.e., the every xel must

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

Image Processing (Computer Vision) Inverse Photography. Vision in Nature. Image Processing: 2003/2004. See the Big Picture.

Image Processing (Computer Vision) Inverse Photography. Vision in Nature. Image Processing: 2003/2004. See the Big Picture. Image Processng Computer Vson Inverse Photography World Pctures/Vdeo Photography Image Processng Computer Vson Pctures/Vdeo Somethng Vson n Nature Only smart organsms see! Plants do not have eyes Vsual

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

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

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

Classification / Regression Support Vector Machines

Classification / Regression Support Vector Machines Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM

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

Available online at ScienceDirect. Procedia Computer Science 94 (2016 )

Available online at  ScienceDirect. Procedia Computer Science 94 (2016 ) Avalable onlne at www.scencedrect.com ScenceDrect Proceda Comuter Scence 94 (2016 ) 176 182 The 13th Internatonal Conference on Moble Systems and Pervasve Comutng (MobSPC 2016) An Effcent QoS-aware Web

More information

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole

The Research of Ellipse Parameter Fitting Algorithm of Ultrasonic Imaging Logging in the Casing Hole Appled Mathematcs, 04, 5, 37-3 Publshed Onlne May 04 n ScRes. http://www.scrp.org/journal/am http://dx.do.org/0.436/am.04.584 The Research of Ellpse Parameter Fttng Algorthm of Ultrasonc Imagng Loggng

More information

Collaboratively Regularized Nearest Points for Set Based Recognition

Collaboratively Regularized Nearest Points for Set Based Recognition Academc Center for Computng and Meda Studes, Kyoto Unversty Collaboratvely Regularzed Nearest Ponts for Set Based Recognton Yang Wu, Mchhko Mnoh, Masayuk Mukunok Kyoto Unversty 9/1/013 BMVC 013 @ Brstol,

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

FULL-FRAME VIDEO STABILIZATION WITH A POLYLINE-FITTED CAMCORDER PATH

FULL-FRAME VIDEO STABILIZATION WITH A POLYLINE-FITTED CAMCORDER PATH FULL-FRAME VIDEO STABILIZATION WITH A POLYLINE-FITTED CAMCORDER PATH Jong-Shan Ln ( 林蓉珊 ) We-Tng Huang ( 黃惟婷 ) 2 Bng-Yu Chen ( 陳炳宇 ) 3 Mng Ouhyoung ( 歐陽明 ) Natonal Tawan Unversty E-mal: {marukowetng}@cmlab.cse.ntu.edu.tw

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

Multi-stable Perception. Necker Cube

Multi-stable Perception. Necker Cube Mult-stable Percepton Necker Cube Spnnng dancer lluson, Nobuuk Kaahara Fttng and Algnment Computer Vson Szelsk 6.1 James Has Acknowledgment: Man sldes from Derek Hoem, Lana Lazebnk, and Grauman&Lebe 2008

More information

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

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

More information

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

Edge Detection in Noisy Images Using the Support Vector Machines

Edge Detection in Noisy Images Using the Support Vector Machines Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona

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

LEAST SQUARES. RANSAC. HOUGH TRANSFORM.

LEAST SQUARES. RANSAC. HOUGH TRANSFORM. LEAS SQUARES. RANSAC. HOUGH RANSFORM. he sldes are from several sources through James Has (Brown); Srnvasa Narasmhan (CMU); Slvo Savarese (U. of Mchgan); Bll Freeman and Antono orralba (MI), ncludng ther

More information

Hierarchical clustering for gene expression data analysis

Hierarchical clustering for gene expression data analysis Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally

More information

VIDEO COMPLETION USING HIERARCHICAL MOTION ESTIMATION AND COLOR COMPENSATION

VIDEO COMPLETION USING HIERARCHICAL MOTION ESTIMATION AND COLOR COMPENSATION VIDEO COMPLETION USING HIERARCHICAL MOTION ESTIMATION AND COLOR COMPENSATION Jn-Hong Km 1, Rae-Hong Park 1 and Jno Lee 1 1 Department of Electronc Engneerng, Sogang Unversty, Seoul, Korea sosu02kjh@sogang.ac.kr,

More information

Prof. Feng Liu. Spring /24/2017

Prof. Feng Liu. Spring /24/2017 Prof. Feng Lu Sprng 2017 ttp://www.cs.pd.edu/~flu/courses/cs510/ 05/24/2017 Last me Compostng and Mattng 2 oday Vdeo Stablzaton Vdeo stablzaton ppelne 3 Orson Welles, ouc of Evl, 1958 4 Images courtesy

More information

Biostatistics 615/815

Biostatistics 615/815 The E-M Algorthm Bostatstcs 615/815 Lecture 17 Last Lecture: The Smplex Method General method for optmzaton Makes few assumptons about functon Crawls towards mnmum Some recommendatons Multple startng ponts

More information

Calibrating a single camera. Odilon Redon, Cyclops, 1914

Calibrating a single camera. Odilon Redon, Cyclops, 1914 Calbratng a sngle camera Odlon Redon, Cclops, 94 Our goal: Recover o 3D structure Recover o structure rom one mage s nherentl ambguous??? Sngle-vew ambgut Sngle-vew ambgut Rashad Alakbarov shadow sculptures

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

IMAGE STITCHING WITH PERSPECTIVE-PRESERVING WARPING

IMAGE STITCHING WITH PERSPECTIVE-PRESERVING WARPING ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume III-3, 2016 IMAGE STITCHING WITH PERSPECTIVE-PRESERVING WARPING Tanzhu Xang, Gu-Song Xa, Langpe Zhang State Key Laboratory

More information

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan

More information

Lecture Note 08 EECS 4101/5101 Instructor: Andy Mirzaian. All Nearest Neighbors: The Lifting Method

Lecture Note 08 EECS 4101/5101 Instructor: Andy Mirzaian. All Nearest Neighbors: The Lifting Method Lecture Note 08 EECS 4101/5101 Instructor: Andy Mrzaan Introducton All Nearest Neghbors: The Lftng Method Suose we are gven aset P ={ 1, 2,..., n }of n onts n the lane. The gven coordnates of the -th ont

More information

Image Fusion With a Dental Panoramic X-ray Image and Face Image Acquired With a KINECT

Image Fusion With a Dental Panoramic X-ray Image and Face Image Acquired With a KINECT Image Fuson Wth a Dental Panoramc X-ray Image and Face Image Acqured Wth a KINECT Kohe Kawa* 1, Koch Ogawa* 1, Aktosh Katumata* 2 * 1 Graduate School of Engneerng, Hose Unversty * 2 School of Dentstry,

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

Discriminative Dictionary Learning with Pairwise Constraints

Discriminative Dictionary Learning with Pairwise Constraints Dscrmnatve Dctonary Learnng wth Parwse Constrants Humn Guo Zhuoln Jang LARRY S. DAVIS UNIVERSITY OF MARYLAND Nov. 6 th, Outlne Introducton/motvaton Dctonary Learnng Dscrmnatve Dctonary Learnng wth Parwse

More information

Hybrid Non-Blind Color Image Watermarking

Hybrid Non-Blind Color Image Watermarking Hybrd Non-Blnd Color Image Watermarkng Ms C.N.Sujatha 1, Dr. P. Satyanarayana 2 1 Assocate Professor, Dept. of ECE, SNIST, Yamnampet, Ghatkesar Hyderabad-501301, Telangana 2 Professor, Dept. of ECE, AITS,

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

MOTION BLUR ESTIMATION AT CORNERS

MOTION BLUR ESTIMATION AT CORNERS Gacomo Boracch and Vncenzo Caglot Dpartmento d Elettronca e Informazone, Poltecnco d Mlano, Va Ponzo, 34/5-20133 MILANO boracch@elet.polm.t, caglot@elet.polm.t Keywords: Abstract: Pont Spread Functon Parameter

More information

Multi-view 3D Position Estimation of Sports Players

Multi-view 3D Position Estimation of Sports Players Mult-vew 3D Poston Estmaton of Sports Players Robbe Vos and Wlle Brnk Appled Mathematcs Department of Mathematcal Scences Unversty of Stellenbosch, South Afrca Emal: vosrobbe@gmal.com Abstract The problem

More information

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place

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

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

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

Color Consistency Correction Based on Remapping Optimization for Image Stitching

Color Consistency Correction Based on Remapping Optimization for Image Stitching Color Consstency Correcton Based on Remappng Optmzaton for Image Sttchng Menghan Xa Jan Yao* Renpng Xe M Zhang School of Remote Sensng & Informaton Engneerng, Wuhan Unversty, Wuhan, Hube, P.R. Chna *Emal:

More information

Modeling and Solving Nontraditional Optimization Problems Session 2a: Conic Constraints

Modeling and Solving Nontraditional Optimization Problems Session 2a: Conic Constraints Modelng and Solvng Nontradtonal Optmzaton Problems Sesson 2a: Conc Constrants Robert Fourer Industral Engneerng & Management Scences Northwestern Unversty AMPL Optmzaton LLC 4er@northwestern.edu 4er@ampl.com

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

Joint Tracking of Features and Edges

Joint Tracking of Features and Edges Jont Trackng of Features and Edges Stanley T. Brchfeld Shrnvas J. Pundlk Electrcal and Computer Engneerng Department Clemson Unversty, Clemson, SC 29634 {stb, spundl}@clemson.edu Abstract Sparse features

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

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

Lecture 13: High-dimensional Images

Lecture 13: High-dimensional Images Lec : Hgh-dmensonal Images Grayscale Images Lecture : Hgh-dmensonal Images Math 90 Prof. Todd Wttman The Ctadel A grayscale mage s an nteger-valued D matrx. An 8-bt mage takes on values between 0 and 55.

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

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and

More information

Ecient Computation of the Most Probable Motion from Fuzzy. Moshe Ben-Ezra Shmuel Peleg Michael Werman. The Hebrew University of Jerusalem

Ecient Computation of the Most Probable Motion from Fuzzy. Moshe Ben-Ezra Shmuel Peleg Michael Werman. The Hebrew University of Jerusalem Ecent Computaton of the Most Probable Moton from Fuzzy Correspondences Moshe Ben-Ezra Shmuel Peleg Mchael Werman Insttute of Computer Scence The Hebrew Unversty of Jerusalem 91904 Jerusalem, Israel Emal:

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

Distance Calculation from Single Optical Image

Distance Calculation from Single Optical Image 17 Internatonal Conference on Mathematcs, Modellng and Smulaton Technologes and Applcatons (MMSTA 17) ISBN: 978-1-6595-53-8 Dstance Calculaton from Sngle Optcal Image Xao-yng DUAN 1,, Yang-je WEI 1,,*

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

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method

3D Virtual Eyeglass Frames Modeling from Multiple Camera Image Data Based on the GFFD Deformation Method NICOGRAPH Internatonal 2012, pp. 114-119 3D Vrtual Eyeglass Frames Modelng from Multple Camera Image Data Based on the GFFD Deformaton Method Norak Tamura, Somsangouane Sngthemphone and Katsuhro Ktama

More information

Alignment and Object Instance Recognition

Alignment and Object Instance Recognition Algnment and Object Instance Recognton Computer Vson Ja-Bn Huang, Vrgna Tech Man sldes from S. Lazebnk and D. Hoem Admnstratve Stuffs HW 2 due 11:59 PM Oct 9 Anonmous feedback Lectures Mcrophone on our

More information

LECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming

LECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming CEE 60 Davd Rosenberg p. LECTURE NOTES Dualty Theory, Senstvty Analyss, and Parametrc Programmng Learnng Objectves. Revew the prmal LP model formulaton 2. Formulate the Dual Problem of an LP problem (TUES)

More information

Air Transport Demand. Ta-Hui Yang Associate Professor Department of Logistics Management National Kaohsiung First Univ. of Sci. & Tech.

Air Transport Demand. Ta-Hui Yang Associate Professor Department of Logistics Management National Kaohsiung First Univ. of Sci. & Tech. Ar Transport Demand Ta-Hu Yang Assocate Professor Department of Logstcs Management Natonal Kaohsung Frst Unv. of Sc. & Tech. 1 Ar Transport Demand Demand for ar transport between two ctes or two regons

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

REMOTE SENSING REQUIREMENTS DEVELOPMENT: A SIMULATION-BASED APPROACH

REMOTE SENSING REQUIREMENTS DEVELOPMENT: A SIMULATION-BASED APPROACH REMOTE SENSING REQUIREMENTS DEVEOPMENT: A SIMUATION-BASED APPROAC V. Zanon a, B. Davs a, R. Ryan b, G. Gasser c, S. Blonsk b a Earth Scence Applcatons Drectorate, Natonal Aeronautcs and Space Admnstraton,

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

On the Two-level Hybrid Clustering Algorithm

On the Two-level Hybrid Clustering Algorithm On the Two-level Clusterng Algorthm ng Yeow Cheu, Chee Keong Kwoh, Zongln Zhou Bonformatcs Research Centre, School of Comuter ngneerng, Nanyang Technologcal Unversty, Sngaore 639798 ezlzhou@ntu.edu.sg

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

Introduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2

Introduction to Geometrical Optics - a 2D ray tracing Excel model for spherical mirrors - Part 2 Introducton to Geometrcal Optcs - a D ra tracng Ecel model for sphercal mrrors - Part b George ungu - Ths s a tutoral eplanng the creaton of an eact D ra tracng model for both sphercal concave and sphercal

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

y and the total sum of

y and the total sum of Lnear regresson Testng for non-lnearty In analytcal chemstry, lnear regresson s commonly used n the constructon of calbraton functons requred for analytcal technques such as gas chromatography, atomc absorpton

More information

Realistic Rendering. Traditional Computer Graphics. Traditional Computer Graphics. Production Pipeline. Appearance in the Real World

Realistic Rendering. Traditional Computer Graphics. Traditional Computer Graphics. Production Pipeline. Appearance in the Real World Advanced Computer Graphcs (Fall 2009 CS 294, Renderng Lecture 11 Representatons of Vsual Appearance Rav Ramamoorth Realstc Renderng Geometry Renderng Algorthm http://nst.eecs.berkeley.edu/~cs294-13/fa09

More information

MOTION PANORAMA CONSTRUCTION FROM STREAMING VIDEO FOR POWER- CONSTRAINED MOBILE MULTIMEDIA ENVIRONMENTS XUNYU PAN

MOTION PANORAMA CONSTRUCTION FROM STREAMING VIDEO FOR POWER- CONSTRAINED MOBILE MULTIMEDIA ENVIRONMENTS XUNYU PAN MOTION PANORAMA CONSTRUCTION FROM STREAMING VIDEO FOR POWER- CONSTRAINED MOBILE MULTIMEDIA ENVIRONMENTS by XUNYU PAN (Under the Drecton of Suchendra M. Bhandarkar) ABSTRACT In modern tmes, more and more

More information

Learning Discriminative Data Fitting Functions for Blind Image Deblurring

Learning Discriminative Data Fitting Functions for Blind Image Deblurring Learnng Dscrmnatve Data Fttng Functons for Blnd Image Deblurrng Jnshan Pan 1 Jangxn Dong 2 Yu-Wng Ta 3 Zhxun Su 2 Mng-Hsuan Yang 4 1 Nanng Unversty of Scence and Technology 2 Dalan Unversty of Technology

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

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

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

Dynamic wetting property investigation of AFM tips in micro/nanoscale

Dynamic wetting property investigation of AFM tips in micro/nanoscale Dynamc wettng property nvestgaton of AFM tps n mcro/nanoscale The wettng propertes of AFM probe tps are of concern n AFM tp related force measurement, fabrcaton, and manpulaton technques, such as dp-pen

More information

CHAPTER 3 SEQUENTIAL MINIMAL OPTIMIZATION TRAINED SUPPORT VECTOR CLASSIFIER FOR CANCER PREDICTION

CHAPTER 3 SEQUENTIAL MINIMAL OPTIMIZATION TRAINED SUPPORT VECTOR CLASSIFIER FOR CANCER PREDICTION 48 CHAPTER 3 SEQUENTIAL MINIMAL OPTIMIZATION TRAINED SUPPORT VECTOR CLASSIFIER FOR CANCER PREDICTION 3.1 INTRODUCTION The raw mcroarray data s bascally an mage wth dfferent colors ndcatng hybrdzaton (Xue

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

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered

More information

Advances. Photometric Stereo

Advances. Photometric Stereo Advances n Photometrc tereo uggero Pntus Tutor Prof. assmo Vanz Contents ) ntroducton g.3 ) mage formaton and reflectance ma g.5 3) Changng from two to four lghts g. 4) Basc theory gradent extracton g.

More information

Cable optimization of a long span cable stayed bridge in La Coruña (Spain)

Cable optimization of a long span cable stayed bridge in La Coruña (Spain) Computer Aded Optmum Desgn n Engneerng XI 107 Cable optmzaton of a long span cable stayed brdge n La Coruña (Span) A. Baldomr & S. Hernández School of Cvl Engneerng, Unversty of Coruña, La Coruña, Span

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

EECS 730 Introduction to Bioinformatics Sequence Alignment. Luke Huan Electrical Engineering and Computer Science

EECS 730 Introduction to Bioinformatics Sequence Alignment. Luke Huan Electrical Engineering and Computer Science EECS 730 Introducton to Bonformatcs Sequence Algnment Luke Huan Electrcal Engneerng and Computer Scence http://people.eecs.ku.edu/~huan/ HMM Π s a set of states Transton Probabltes a kl Pr( l 1 k Probablty

More information