Image Prediction Based on Kalman Filtering for Interactive Simulation Environments
|
|
- Alice Atkins
- 5 years ago
- Views:
Transcription
1 Image Prediction Based on Kalman Filtering for Interactive Simulation Environments M. SCHNITZLER, A. KUMMERT Department of Electrical and Information Engineering, Communication Theory University of Wuppertal Rainer-Gruenter-Str., 49 Wuppertal GERMANY Abstract: - In this paper a new method is presented to increase the frame rate of CGI (computer generated images) applications by means of image prediction. This method is based on Kalman filtering for the prediction of the new viewpoint position. The method processes two consecutive input images and the dedicated z-buffer content, which are computed by the graphics hardware, for the prediction of the next (future) image frame. The input data is transformed into an output image by 3D image warping. An enhanced z-buffer algorithm decides which pixels are visible. Key-Words: - Kalman filter, image prediction, interactive simulation Introduction The field of computer-based image generation for visual interactive simulation environments (virtual reality) is of great interest in different application areas like architecture, town planing, entertainment, or training by driving simulators. The algorithms which are used today require special graphics hardware for the computation of all pixels of an image. They do not take into account, that successive images in a sequence are related to each other. bviously, the computational load of the graphics hardware is proportional to the frame rate. A high frame rate is desirable to avoid flickering. Frame rate doubling leads to nearly twice the hardware expense and thus to enormous costs. Hence, image interpolation and prediction methods are of interest for achieving higher frame rates, without raising significantly costs. The major drawback of image interpolation methods is the additional latency they produce which can be avoided by applying image prediction techniques. n the other hand, image interpolation methods are performing better with respect to exposure areas compared to image prediction. The main reason for the better results are due to the additional knowledge (informatio about exposure and occlusion areas. I I Fig.: Time flow of interpolation and prediction. = original sequence, I = interpolated sequence, P = predicted sequence Viewpoint Prediction The knowledge about future viewpoint positions is very important for a good prediction quality. The viewpoint prediction is done by a so-called Kalman filter. The latter is efficiently realised due to its recursive description formula. The motion model takes into account the velocity and the acceleration of the viewpoint position.. Kalman filter Kalman filtering allows the prediction of the viewpoint position under system uncertainties. It is tolerant against measurement errors in the P P
2 corrector step. The measurements and the system states are modelled stochastically. The system state equations of the Kalman-filter can be formulated as w ( n + ) = Aw( + (, () o ( = Cw( + -(. () The first equation describes the motion model of the viewpoint position. w( is the current state vector of the system. The new state vector w ( n +) can be estimated via the system matrix A. The uncertainties of the system are modelled by (. The second equation determines the filter output by means of the matrix C and the system state vector w (. -( describes the measurement uncertainties in a stochastic way. The estimation ŵ of the state vector w is computed in the so-called prediction step via and w ˆ ( n n ) = Awˆ ( n n ), (3) T P ( n n ) = AP( n n ) A + Q, (4) o ( = Cwˆ ( n, (5) where the argument ( n n ) symbolizes an estimate for the time step n taking into account measurement data collected up to time step n. In equation (3) and (4) the new state vector and the system covariance matrix P are estimated, respectively, whereas the system uncertainties are characterised by the covariance matrix Q. The correction step is done by the following three equations K = P( R T T n n ) C [ CP( n n ) C + ], (6) w ˆ ( n = wˆ ( n + K[ o( Cwˆ ( n n )], (7) P ( n = P( n n ) + KCP( n n ). (8) The Kalman gain K is defined by equation (6). The system uncertainties and the measurement uncertainties influence via covariance matrix P and matrix R, respectively, the Kalman gain. The matrix R represents the covariance of the measurement noise.. Motion model The motion model for viewpoint estimation is based on velocity and acceleration of the viewpoint position. In the continuous-time case differential equations are used to describe the problem. In the discrete-time model difference equations have to be solved. The relationship between the position s ( and the velocity v ( can be described by t s( = v(ϑ) dϑ (9) in the continuous-time case, and for the velocity v ( and the acceleration a ( we have t v( = a(ϑ) dϑ. (0) The position at time t + T as function of the position at time t is defined as s ( t + T ) = s( + Tv(, () if the velocity is constant during time interval T. An improved modelling takes into account acceleration a ( by s ( t + T ) = s( + Tv( + T a(. () The last term always contains the uncertainty of the model. If the acceleration a ( is changing in the time interval T, the estimation would be degraded. 3 3-D Image Warping The output image I is calculated by means of 3D image warping. The parameters for the warping equations can be determined from the known viewpoint positions c 0 and c of the available images I 0 and I, the estimated viewpoint position c, and the used camera model. In our case a pine hole camera model is used. The pine hole camera can be described by the projection matrix P as follows width 0 height P = 0. (3) 0 0 f
3 The parameter f represents the distance between the projection centre and the projection plane. If only the field of view fov is given, the projection matrix is defined as width 0 height P = 0. (4) width fov 0 0 cot The warping equation itself can be formulated as c z u = P P z u + P ( c ). (5) u and u are the input pixel coordinate vector and the output coordinate vector, respectively, of the input and output images x u = y, x u = y. (6) z and z are the input and output image z-buffer values at the position u and u, respectively. Please notice that at this step only images I and I are considered. The third image I 0 will be taken into account later on in section 4. If we define w w w3 P P = w w w3 (7) w3 w3 w33 and w4 P ( c c) = w4, (8) w34 we obtain after some algebraic transformations the new pixel coordinates x, ) as ( y wx + w y + w3 + w4z = w3x + w3 y + w34z x, (9) The last equations enable to calculate the new pixel position and fill them with the RGB and z-buffer values. The z-buffer is important for visibility during warping and the following combination step. The new z-buffer content is given by w 34 z = w3x + w3y +. () z z 4 Combination A single image does not contain enough information to describe the whole 3D scenario. That is the reason why 3D image warper produce exposure artefacts. We need at least two consecutive images I 0 and I to avoid or reduce this effects. In other words, transformations (5) to () are performed twice. At first on basis of input image I 0 producing a first output image I /0. At second, input image I is used to compute a second output image I /. Finally, the actual output image I is computed on basis of I /0 and I /0 in the combine step where the z-buffer content of I / and I /0 plays a key role. The image information associated with the smaller z-value is used. If both input images have the same occlusion areas, a different approach has to be used. This areas are filled with information from the neighbour pixels by using a weighted sum. Fig. : Exposure artefacts generated by the warping stage are shown as black regions. wx + w y + w3 + w4z = w3x + w3 y + w34z y. (0)
4 5 Results PSNR[dB] = 0log NM N M i= j= 55 ( x [ i, j] x [ i, j] ) pic ref E ( x [ i, j] ) M ( x [ i, ) N M SMSE = M pic ref j] NM i= j= 3 3 Table : Definition of objective (PSNR) and subjective (SMSE) assessment criteria. The quality of the generated images is the most important aspect. Hence, images had been tested subjectively and objectively. Subjective means that a group of people has to look at the new images and has to judge the quality. The objective assessment uses mathematically formulated error or quality measures. bjective results have the advantage that the method can be easily compared with other methods. However, a good correlation between the Fig. 3: An example for an RGB-image and the corresponding z-buffer content. objective results and the subjective perception is not always available. The objective tests are based on the PSNR (Peak Signal to Noise Ratio) and the SMSE (Subjective Mean Square Error) which are defined in table. For a correct interpretation it is important to take into account, that an algorithm performs better if the corresponding PSNR value is high and the SMSE value is low. 3D image warping was tested with an image sequence having a resolution of pixels. Fig. 3 shows a sample of the image sequence and the corresponding z-buffer content. For objective tests the original testsequence was sub-sampled from 60 fps to 30 fps to generate reference images for the evaluation of the prediction algorithm. 6 Conclusion In this paper a new image prediction method for CGI systems has been presented. The use of Kalman filter for the prediction of the upcoming viewpoint is leading to good results. The image prediction using 3D image warping nearly reaches the same good results as a comparable interpolation method [6] (in spite of the fact that prediction algorithms have less information available than interpolation techniques). n the other hand, prediction methods do not add additional latency to the overall performance of the CGI system. The presented method is a profound basis for further research and development in the field of image prediction.
5 References: [] J. et al Gomes, Warping and Morphing of Graphical bjects, Morgan Kaufmann Publishers, 999. [] R. E. Kalman, A new approach to linear filtering and prediction theory, Journal of Basic Engineering, Vol. 83D, No., pp [3] W. Mark, Post-Rendering 3D Image Warping: Visibility, Reconstruction and Performance for Depth-Image Warping, Dissertation, University of North Carolina at Chapel Hill, 999. [4] L. McMillan, An image-based approach to three-dimensional computer graphics, Dissertation, University of North Carolina at Chapel Hill, 997. [5] G. Wohlberg, Digital image warping, IEEE Computer Society Press, 99. [6] W. Zeise, Zwischenbildinterpolation zur Erhöhung der Bildgenerierrate in visuellen interaktiven Simulationsumgebungen, Dissertation, University of Wuppertal, 999.
Spatio-Temporal Stereo Disparity Integration
Spatio-Temporal Stereo Disparity Integration Sandino Morales and Reinhard Klette The.enpeda.. Project, The University of Auckland Tamaki Innovation Campus, Auckland, New Zealand pmor085@aucklanduni.ac.nz
More informationCompression of Light Field Images using Projective 2-D Warping method and Block matching
Compression of Light Field Images using Projective 2-D Warping method and Block matching A project Report for EE 398A Anand Kamat Tarcar Electrical Engineering Stanford University, CA (anandkt@stanford.edu)
More informationJUST-IN-TIME PIXELS. Mark Mine and Gary Bishop. Department of Computer Science University of North Carolina Chapel Hill, NC
JUST-IN-TIME PIXELS Mark Mine and Gary Bishop Department of Computer Science University of North Carolina Chapel Hill, NC 27599-3175 Abstract This paper describes Just-In-Time Pixels, a technique for generating
More informationEvaluation of Moving Object Tracking Techniques for Video Surveillance Applications
International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Evaluation
More informationLecture 20: Tracking. Tuesday, Nov 27
Lecture 20: Tracking Tuesday, Nov 27 Paper reviews Thorough summary in your own words Main contribution Strengths? Weaknesses? How convincing are the experiments? Suggestions to improve them? Extensions?
More informationPuzzle games (like Rubik s cube) solver
Puzzle games (like Rubik s cube) solver Vitalii Zakharov University of Tartu vitaliiz@ut.ee 1. INTRODUCTION This project is a continuation of the PTAM (Parallel Tracking and Mapping for Small AR Workspaces)
More informationDEPTH-LEVEL-ADAPTIVE VIEW SYNTHESIS FOR 3D VIDEO
DEPTH-LEVEL-ADAPTIVE VIEW SYNTHESIS FOR 3D VIDEO Ying Chen 1, Weixing Wan 2, Miska M. Hannuksela 3, Jun Zhang 2, Houqiang Li 2, and Moncef Gabbouj 1 1 Department of Signal Processing, Tampere University
More informationTracking. Establish where an object is, other aspects of state, using time sequence Biggest problem -- Data Association
Tracking Establish where an object is, other aspects of state, using time sequence Biggest problem -- Data Association Key ideas Tracking by detection Tracking through flow Track by detection (simple form)
More informationMobile Robotics. Mathematics, Models, and Methods. HI Cambridge. Alonzo Kelly. Carnegie Mellon University UNIVERSITY PRESS
Mobile Robotics Mathematics, Models, and Methods Alonzo Kelly Carnegie Mellon University HI Cambridge UNIVERSITY PRESS Contents Preface page xiii 1 Introduction 1 1.1 Applications of Mobile Robots 2 1.2
More informationEnhanced Still 3D Integral Images Rendering Based on Multiprocessor Ray Tracing System
Journal of Image and Graphics, Volume 2, No.2, December 2014 Enhanced Still 3D Integral Images Rendering Based on Multiprocessor Ray Tracing System M. G. Eljdid Computer Sciences Department, Faculty of
More informationCV: 3D to 2D mathematics. Perspective transformation; camera calibration; stereo computation; and more
CV: 3D to 2D mathematics Perspective transformation; camera calibration; stereo computation; and more Roadmap of topics n Review perspective transformation n Camera calibration n Stereo methods n Structured
More informationMeasurements using three-dimensional product imaging
ARCHIVES of FOUNDRY ENGINEERING Published quarterly as the organ of the Foundry Commission of the Polish Academy of Sciences ISSN (1897-3310) Volume 10 Special Issue 3/2010 41 46 7/3 Measurements using
More informationDIGITAL SURFACE MODELS OF CITY AREAS BY VERY HIGH RESOLUTION SPACE IMAGERY
DIGITAL SURFACE MODELS OF CITY AREAS BY VERY HIGH RESOLUTION SPACE IMAGERY Jacobsen, K. University of Hannover, Institute of Photogrammetry and Geoinformation, Nienburger Str.1, D30167 Hannover phone +49
More informationCONVERSION OF FREE-VIEWPOINT 3D MULTI-VIEW VIDEO FOR STEREOSCOPIC DISPLAYS
CONVERSION OF FREE-VIEWPOINT 3D MULTI-VIEW VIDEO FOR STEREOSCOPIC DISPLAYS Luat Do 1, Svitlana Zinger 1, and Peter H. N. de With 1,2 1 Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven,
More information5LSH0 Advanced Topics Video & Analysis
1 Multiview 3D video / Outline 2 Advanced Topics Multimedia Video (5LSH0), Module 02 3D Geometry, 3D Multiview Video Coding & Rendering Peter H.N. de With, Sveta Zinger & Y. Morvan ( p.h.n.de.with@tue.nl
More informationImage Inpainting by Hyperbolic Selection of Pixels for Two Dimensional Bicubic Interpolations
Image Inpainting by Hyperbolic Selection of Pixels for Two Dimensional Bicubic Interpolations Mehran Motmaen motmaen73@gmail.com Majid Mohrekesh mmohrekesh@yahoo.com Mojtaba Akbari mojtaba.akbari@ec.iut.ac.ir
More informationImage-Based Modeling and Rendering. Image-Based Modeling and Rendering. Final projects IBMR. What we have learnt so far. What IBMR is about
Image-Based Modeling and Rendering Image-Based Modeling and Rendering MIT EECS 6.837 Frédo Durand and Seth Teller 1 Some slides courtesy of Leonard McMillan, Wojciech Matusik, Byong Mok Oh, Max Chen 2
More informationReduction of Blocking artifacts in Compressed Medical Images
ISSN 1746-7659, England, UK Journal of Information and Computing Science Vol. 8, No. 2, 2013, pp. 096-102 Reduction of Blocking artifacts in Compressed Medical Images Jagroop Singh 1, Sukhwinder Singh
More informationDEPTH LESS 3D RENDERING. Mashhour Solh and Ghassan AlRegib
DEPTH LESS 3D RENDERING Mashhour Solh and Ghassan AlRegib School of Electrical and Computer Engineering Georgia Institute of Technology { msolh,alregib } @gatech.edu ABSTRACT We propose a new view synthesis
More informationBasic Elements. Geometry is the study of the relationships among objects in an n-dimensional space
Basic Elements Geometry is the study of the relationships among objects in an n-dimensional space In computer graphics, we are interested in objects that exist in three dimensions We want a minimum set
More informationReal-time Detection of Illegally Parked Vehicles Using 1-D Transformation
Real-time Detection of Illegally Parked Vehicles Using 1-D Transformation Jong Taek Lee, M. S. Ryoo, Matthew Riley, and J. K. Aggarwal Computer & Vision Research Center Dept. of Electrical & Computer Engineering,
More informationA Statistical Consistency Check for the Space Carving Algorithm.
A Statistical Consistency Check for the Space Carving Algorithm. A. Broadhurst and R. Cipolla Dept. of Engineering, Univ. of Cambridge, Cambridge, CB2 1PZ aeb29 cipolla @eng.cam.ac.uk Abstract This paper
More informationComparison of Vessel Segmentations using STAPLE
Comparison of Vessel Segmentations using STAPLE Julien Jomier, Vincent LeDigarcher, and Stephen R. Aylward Computer-Aided Diagnosis and Display Lab The University of North Carolina at Chapel Hill, Department
More informationRobust Fingertip Tracking with Improved Kalman Filter
Robust Fingertip Tracking with Improved Kalman Filter Chunyang Wang and Bo Yuan Intelligent Computing Lab, Division of Informatics Graduate School at Shenzhen, Tsinghua University Shenzhen 518055, P.R.
More informationReview and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding.
Project Title: Review and Implementation of DWT based Scalable Video Coding with Scalable Motion Coding. Midterm Report CS 584 Multimedia Communications Submitted by: Syed Jawwad Bukhari 2004-03-0028 About
More informationINTERNATIONAL ORGANISATION FOR STANDARISATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO/IEC JTC1/SC29/WG11 CODING OF MOVING PICTURES AND AUDIO
INTERNATIONAL ORGANISATION FOR STANDARISATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO/IEC JTC1/SC29/WG11 CODING OF MOVING PICTURES AND AUDIO ISO/IEC JTC1/SC29/WG11 MPEG/M15672 July 2008, Hannover,
More informationWATERMARKING FOR LIGHT FIELD RENDERING 1
ATERMARKING FOR LIGHT FIELD RENDERING 1 Alper Koz, Cevahir Çığla and A. Aydın Alatan Department of Electrical and Electronics Engineering, METU Balgat, 06531, Ankara, TURKEY. e-mail: koz@metu.edu.tr, cevahir@eee.metu.edu.tr,
More informationVolumetric Scene Reconstruction from Multiple Views
Volumetric Scene Reconstruction from Multiple Views Chuck Dyer University of Wisconsin dyer@cs cs.wisc.edu www.cs cs.wisc.edu/~dyer Image-Based Scene Reconstruction Goal Automatic construction of photo-realistic
More informationNOISE PROPAGATION FROM VIBRATING STRUCTURES
NOISE PROPAGATION FROM VIBRATING STRUCTURES Abstract R. Helfrich, M. Spriegel (INTES GmbH, Germany) Noise and noise exposure are becoming more important in product development due to environmental legislation.
More informationMulti-view stereo. Many slides adapted from S. Seitz
Multi-view stereo Many slides adapted from S. Seitz Beyond two-view stereo The third eye can be used for verification Multiple-baseline stereo Pick a reference image, and slide the corresponding window
More informationArchbold Area Schools Math Curriculum Map
Math 8 August - May Mathematical Processes Formulate a problem or mathematical model in response to a specific need or situation, determine information required to solve the problem, choose method for
More informationA Comparative Study & Analysis of Image Restoration by Non Blind Technique
A Comparative Study & Analysis of Image Restoration by Non Blind Technique Saurav Rawat 1, S.N.Tazi 2 M.Tech Student, Assistant Professor, CSE Department, Government Engineering College, Ajmer Abstract:
More informationA Real-time Detection for Traffic Surveillance Video Shaking
International Conference on Mechatronics, Control and Electronic Engineering (MCE 201) A Real-time Detection for Traffic Surveillance Video Shaking Yaoyao Niu Zhenkuan Pan e-mail: 11629830@163.com e-mail:
More informationImage Quality Assessment Techniques: An Overview
Image Quality Assessment Techniques: An Overview Shruti Sonawane A. M. Deshpande Department of E&TC Department of E&TC TSSM s BSCOER, Pune, TSSM s BSCOER, Pune, Pune University, Maharashtra, India Pune
More informationMRT based Fixed Block size Transform Coding
3 MRT based Fixed Block size Transform Coding Contents 3.1 Transform Coding..64 3.1.1 Transform Selection...65 3.1.2 Sub-image size selection... 66 3.1.3 Bit Allocation.....67 3.2 Transform coding using
More informationImage Transfer Methods. Satya Prakash Mallick Jan 28 th, 2003
Image Transfer Methods Satya Prakash Mallick Jan 28 th, 2003 Objective Given two or more images of the same scene, the objective is to synthesize a novel view of the scene from a view point where there
More information3D Sensing and Reconstruction Readings: Ch 12: , Ch 13: ,
3D Sensing and Reconstruction Readings: Ch 12: 12.5-6, Ch 13: 13.1-3, 13.9.4 Perspective Geometry Camera Model Stereo Triangulation 3D Reconstruction by Space Carving 3D Shape from X means getting 3D coordinates
More informationJPEG compression of monochrome 2D-barcode images using DCT coefficient distributions
Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome D-barcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai
More informationOPTIMISATION OF PIN FIN HEAT SINK USING TAGUCHI METHOD
CHAPTER - 5 OPTIMISATION OF PIN FIN HEAT SINK USING TAGUCHI METHOD The ever-increasing demand to lower the production costs due to increased competition has prompted engineers to look for rigorous methods
More informationSSIM Image Quality Metric for Denoised Images
SSIM Image Quality Metric for Denoised Images PETER NDAJAH, HISAKAZU KIKUCHI, MASAHIRO YUKAWA, HIDENORI WATANABE and SHOGO MURAMATSU Department of Electrical and Electronics Engineering, Niigata University,
More informationVision-Motion Planning with Uncertainty
Vision-Motion Planning with Uncertainty Jun MIURA Yoshiaki SHIRAI Dept. of Mech. Eng. for Computer-Controlled Machinery, Osaka University, Suita, Osaka 565, Japan jun@ccm.osaka-u.ac.jp Abstract This paper
More informationForward Mapped Planar Mirror Reflections
Forward Mapped Planar Mirror Reflections Rui Bastos, Wolfgang Stürlinger Department of Computer Science University of North Carolina at Chapel Hill Bastos@cs.unc.edu Abstract: This paper presents a new
More informationMultimedia Technology CHAPTER 4. Video and Animation
CHAPTER 4 Video and Animation - Both video and animation give us a sense of motion. They exploit some properties of human eye s ability of viewing pictures. - Motion video is the element of multimedia
More informationA CELLULAR, LANGUAGE DIRECTED COMPUTER ARCHITECTURE. (Extended Abstract) Gyula A. Mag6. University of North Carolina at Chapel Hill
447 A CELLULAR, LANGUAGE DIRECTED COMPUTER ARCHITECTURE (Extended Abstract) Gyula A. Mag6 University of North Carolina at Chapel Hill Abstract If a VLSI computer architecture is to influence the field
More informationInternational Journal of Advanced Research in Computer Science and Software Engineering
Volume 2, Issue 1, January 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: An analytical study on stereo
More informationSecret Image Sharing Scheme Based on a Boolean Operation
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 14, No 2 Sofia 2014 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.2478/cait-2014-0023 Secret Image Sharing Scheme Based
More informationDD2423 Image Analysis and Computer Vision IMAGE FORMATION. Computational Vision and Active Perception School of Computer Science and Communication
DD2423 Image Analysis and Computer Vision IMAGE FORMATION Mårten Björkman Computational Vision and Active Perception School of Computer Science and Communication November 8, 2013 1 Image formation Goal:
More informationMultiview Depth-Image Compression Using an Extended H.264 Encoder Morvan, Y.; Farin, D.S.; de With, P.H.N.
Multiview Depth-Image Compression Using an Extended H.264 Encoder Morvan, Y.; Farin, D.S.; de With, P.H.N. Published in: Proceedings of the 9th international conference on Advanced Concepts for Intelligent
More informationLecture 6 Stereo Systems Multi-view geometry
Lecture 6 Stereo Systems Multi-view geometry Professor Silvio Savarese Computational Vision and Geometry Lab Silvio Savarese Lecture 6-5-Feb-4 Lecture 6 Stereo Systems Multi-view geometry Stereo systems
More informationFRACTAL IMAGE COMPRESSION OF GRAYSCALE AND RGB IMAGES USING DCT WITH QUADTREE DECOMPOSITION AND HUFFMAN CODING. Moheb R. Girgis and Mohammed M.
322 FRACTAL IMAGE COMPRESSION OF GRAYSCALE AND RGB IMAGES USING DCT WITH QUADTREE DECOMPOSITION AND HUFFMAN CODING Moheb R. Girgis and Mohammed M. Talaat Abstract: Fractal image compression (FIC) is a
More informationAugmented Reality, Advanced SLAM, Applications
Augmented Reality, Advanced SLAM, Applications Prof. Didier Stricker & Dr. Alain Pagani alain.pagani@dfki.de Lecture 3D Computer Vision AR, SLAM, Applications 1 Introduction Previous lectures: Basics (camera,
More informationImage-Based Rendering. Johns Hopkins Department of Computer Science Course : Rendering Techniques, Professor: Jonathan Cohen
Image-Based Rendering Image-Based Rendering What is it? Still a difficult question to answer Uses images (photometric( info) as key component of model representation What s Good about IBR Model acquisition
More informationComputing Visibility. Backface Culling for General Visibility. One More Trick with Planes. BSP Trees Ray Casting Depth Buffering Quiz
Computing Visibility BSP Trees Ray Casting Depth Buffering Quiz Power of Plane Equations We ve gotten a lot of mileage out of one simple equation. Basis for D outcode-clipping Basis for plane-at-a-time
More informationMotion Estimation. There are three main types (or applications) of motion estimation:
Members: D91922016 朱威達 R93922010 林聖凱 R93922044 謝俊瑋 Motion Estimation There are three main types (or applications) of motion estimation: Parametric motion (image alignment) The main idea of parametric motion
More informationThe optimum design of a moving PM-type linear motor for resonance operating refrigerant compressor
International Journal of Applied Electromagnetics and Mechanics 33 (2010) 673 680 673 DOI 10.3233/JAE-2010-1172 IOS Press The optimum design of a moving PM-type linear motor for resonance operating refrigerant
More informationImproved Geometric Warping-Based Watermarking
Improved Geometric Warping-Based Watermarking Dima Pröfrock, Mathias Schlauweg, Erika Müller Institute of Communication Engineering, Faculty of Computer Science and Electrical Engineering, University of
More informationc 1999 William R. Mark ALL RIGHTS RESERVED
Post-Rendering 3D Image Warping: Visibility, Reconstruction, and Performance for Depth-Image Warping TR99-022, April 21, 1999 William R. Mark Graphics and Image Processing Laboratory Department of Computer
More informationImage-Based Rendering. Johns Hopkins Department of Computer Science Course : Rendering Techniques, Professor: Jonathan Cohen
Image-Based Rendering Image-Based Rendering What is it? Still a difficult question to answer Uses images (photometric( info) as key component of model representation What s Good about IBR Model acquisition
More informationImage-Based Rendering. Image-Based Rendering
Image-Based Rendering Image-Based Rendering What is it? Still a difficult question to answer Uses images (photometric info) as key component of model representation 1 What s Good about IBR Model acquisition
More informationMotion Estimation for Video Coding Standards
Motion Estimation for Video Coding Standards Prof. Ja-Ling Wu Department of Computer Science and Information Engineering National Taiwan University Introduction of Motion Estimation The goal of video compression
More informationPoint Sample Rendering
Point Sample Rendering Efficient Screen Space Approach for HW Accelerated Surfel Rendering VMV03, november 2003 Gaël GUENNEBAUD - Mathias PAULIN IRIT-CNRS-UPS TOULOUSE-FRANCE http://www.irit.fr/recherches/sirv/vis/surfel/index.html
More informationФУНДАМЕНТАЛЬНЫЕ НАУКИ. Информатика 9 ИНФОРМАТИКА MOTION DETECTION IN VIDEO STREAM BASED ON BACKGROUND SUBTRACTION AND TARGET TRACKING
ФУНДАМЕНТАЛЬНЫЕ НАУКИ Информатика 9 ИНФОРМАТИКА UDC 6813 OTION DETECTION IN VIDEO STREA BASED ON BACKGROUND SUBTRACTION AND TARGET TRACKING R BOGUSH, S ALTSEV, N BROVKO, E IHAILOV (Polotsk State University
More informationUnit Maps: Grade 8 Math
Real Number Relationships 8.3 Number and operations. The student represents and use real numbers in a variety of forms. Representation of Real Numbers 8.3A extend previous knowledge of sets and subsets
More informationNo-reference perceptual quality metric for H.264/AVC encoded video. Maria Paula Queluz
No-reference perceptual quality metric for H.264/AVC encoded video Tomás Brandão Maria Paula Queluz IT ISCTE IT IST VPQM 2010, Scottsdale, USA, January 2010 Outline 1. Motivation and proposed work 2. Technical
More informationAugmented Reality VU. Computer Vision 3D Registration (2) Prof. Vincent Lepetit
Augmented Reality VU Computer Vision 3D Registration (2) Prof. Vincent Lepetit Feature Point-Based 3D Tracking Feature Points for 3D Tracking Much less ambiguous than edges; Point-to-point reprojection
More informationInput Nodes. Surface Input. Surface Input Nodal Motion Nodal Displacement Instance Generator Light Flocking
Input Nodes Surface Input Nodal Motion Nodal Displacement Instance Generator Light Flocking The different Input nodes, where they can be found, what their outputs are. Surface Input When editing a surface,
More informationRuch (Motion) Rozpoznawanie Obrazów Krzysztof Krawiec Instytut Informatyki, Politechnika Poznańska. Krzysztof Krawiec IDSS
Ruch (Motion) Rozpoznawanie Obrazów Krzysztof Krawiec Instytut Informatyki, Politechnika Poznańska 1 Krzysztof Krawiec IDSS 2 The importance of visual motion Adds entirely new (temporal) dimension to visual
More informationImage Compression: An Artificial Neural Network Approach
Image Compression: An Artificial Neural Network Approach Anjana B 1, Mrs Shreeja R 2 1 Department of Computer Science and Engineering, Calicut University, Kuttippuram 2 Department of Computer Science and
More informationCapturing, Modeling, Rendering 3D Structures
Computer Vision Approach Capturing, Modeling, Rendering 3D Structures Calculate pixel correspondences and extract geometry Not robust Difficult to acquire illumination effects, e.g. specular highlights
More informationDEEP LEARNING OF COMPRESSED SENSING OPERATORS WITH STRUCTURAL SIMILARITY (SSIM) LOSS
DEEP LEARNING OF COMPRESSED SENSING OPERATORS WITH STRUCTURAL SIMILARITY (SSIM) LOSS ABSTRACT Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small
More informationMesh Based Interpolative Coding (MBIC)
Mesh Based Interpolative Coding (MBIC) Eckhart Baum, Joachim Speidel Institut für Nachrichtenübertragung, University of Stuttgart An alternative method to H.6 encoding of moving images at bit rates below
More informationNine Weeks: Mathematical Process Standards
HPISD Grade 7 TAG 7/8 Math Nine Weeks: 1 2 3 4 Mathematical Process Standards Apply mathematics to problems arising in everyday life, society, and the workplace. 8.1A Use a problem solving model that incorporates
More informationLet s start with occluding contours (or interior and exterior silhouettes), and look at image-space algorithms. A very simple technique is to render
1 There are two major classes of algorithms for extracting most kinds of lines from 3D meshes. First, there are image-space algorithms that render something (such as a depth map or cosine-shaded model),
More informationSummary and Conclusions
Chapter 13 Summary and Conclusions 13.1 Summary Focusing on the abstract response mechanism of multiple-bolt joints in timber, this work presented the derivation of MULTBOLT, a robust model that predicts
More informationOBSTACLE DETECTION USING STRUCTURED BACKGROUND
OBSTACLE DETECTION USING STRUCTURED BACKGROUND Ghaida Al Zeer, Adnan Abou Nabout and Bernd Tibken Chair of Automatic Control, Faculty of Electrical, Information and Media Engineering University of Wuppertal,
More informationEstimating the wavelength composition of scene illumination from image data is an
Chapter 3 The Principle and Improvement for AWB in DSC 3.1 Introduction Estimating the wavelength composition of scene illumination from image data is an important topics in color engineering. Solutions
More informationFuzzy Inference System based Edge Detection in Images
Fuzzy Inference System based Edge Detection in Images Anjali Datyal 1 and Satnam Singh 2 1 M.Tech Scholar, ECE Department, SSCET, Badhani, Punjab, India 2 AP, ECE Department, SSCET, Badhani, Punjab, India
More informationUsing Optical Flow for Stabilizing Image Sequences. Peter O Donovan
Using Optical Flow for Stabilizing Image Sequences Peter O Donovan 502425 Cmpt 400 Supervisor: Dr. Mark Eramian April 6,2005 1 Introduction In the summer of 1999, the small independent film The Blair Witch
More informationAPS Seventh Grade Math District Benchmark Assessment NM Math Standards Alignment
APS Seventh Grade Math District Benchmark NM Math Standards Alignment SEVENTH GRADE NM STANDARDS Strand: NUMBER AND OPERATIONS Standard: Students will understand numerical concepts and mathematical operations.
More informationCHAPTER 5 MOTION DETECTION AND ANALYSIS
CHAPTER 5 MOTION DETECTION AND ANALYSIS 5.1. Introduction: Motion processing is gaining an intense attention from the researchers with the progress in motion studies and processing competence. A series
More informationComputer Animation. Algorithms and Techniques. z< MORGAN KAUFMANN PUBLISHERS. Rick Parent Ohio State University AN IMPRINT OF ELSEVIER SCIENCE
Computer Animation Algorithms and Techniques Rick Parent Ohio State University z< MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF ELSEVIER SCIENCE AMSTERDAM BOSTON LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO
More informationHYBRID IMAGE COMPRESSION TECHNIQUE
HYBRID IMAGE COMPRESSION TECHNIQUE Eranna B A, Vivek Joshi, Sundaresh K Professor K V Nagalakshmi, Dept. of E & C, NIE College, Mysore.. ABSTRACT With the continuing growth of modern communication technologies,
More informationMassachusetts Institute of Technology Department of Computer Science and Electrical Engineering 6.801/6.866 Machine Vision QUIZ II
Massachusetts Institute of Technology Department of Computer Science and Electrical Engineering 6.801/6.866 Machine Vision QUIZ II Handed out: 001 Nov. 30th Due on: 001 Dec. 10th Problem 1: (a (b Interior
More informationInterpolation and Splines
Interpolation and Splines Anna Gryboś October 23, 27 1 Problem setting Many of physical phenomenona are described by the functions that we don t know exactly. Often we can calculate or measure the values
More informationMATHEMATICS Grade 7 Advanced Standard: Number, Number Sense and Operations
Standard: Number, Number Sense and Operations Number and Number Systems A. Use scientific notation to express large numbers and numbers less than one. 1. Use scientific notation to express large numbers
More informationOptimizing an Inverse Warper
Optimizing an Inverse Warper by Robert W. Marcato, Jr. Submitted to the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degrees of Bachelor
More informationThree-Dimensional Motion Tracking using Clustering
Three-Dimensional Motion Tracking using Clustering Andrew Zastovnik and Ryan Shiroma Dec 11, 2015 Abstract Tracking the position of an object in three dimensional space is a fascinating problem with many
More informationVolume Rendering. Computer Animation and Visualisation Lecture 9. Taku Komura. Institute for Perception, Action & Behaviour School of Informatics
Volume Rendering Computer Animation and Visualisation Lecture 9 Taku Komura Institute for Perception, Action & Behaviour School of Informatics Volume Rendering 1 Volume Data Usually, a data uniformly distributed
More informationCS 787: Assignment 4, Stereo Vision: Block Matching and Dynamic Programming Due: 12:00noon, Fri. Mar. 30, 2007.
CS 787: Assignment 4, Stereo Vision: Block Matching and Dynamic Programming Due: 12:00noon, Fri. Mar. 30, 2007. In this assignment you will implement and test some simple stereo algorithms discussed in
More informationCar tracking in tunnels
Czech Pattern Recognition Workshop 2000, Tomáš Svoboda (Ed.) Peršlák, Czech Republic, February 2 4, 2000 Czech Pattern Recognition Society Car tracking in tunnels Roman Pflugfelder and Horst Bischof Pattern
More informationImage Processing: Motivation Rendering from Images. Related Work. Overview. Image Morphing Examples. Overview. View and Image Morphing CS334
Motivation Rendering from Images Image rocessing: View and CS334 Given left image right image Create intermediate images simulates camera movement [Seitz96] Related Work anoramas ([Chen95/QuicktimeVR],
More informationA Comparative Study of DCT, DWT & Hybrid (DCT-DWT) Transform
A Comparative Study of DCT, DWT & Hybrid (DCT-DWT) Transform Archana Deshlahra 1, G. S.Shirnewar 2,Dr. A.K. Sahoo 3 1 PG Student, National Institute of Technology Rourkela, Orissa (India) deshlahra.archana29@gmail.com
More informationMulti-View Stereo for Static and Dynamic Scenes
Multi-View Stereo for Static and Dynamic Scenes Wolfgang Burgard Jan 6, 2010 Main references Yasutaka Furukawa and Jean Ponce, Accurate, Dense and Robust Multi-View Stereopsis, 2007 C.L. Zitnick, S.B.
More informationPhotorealistic 3D Rendering for VW in Mobile Devices
Abstract University of Arkansas CSCE Department Advanced Virtual Worlds Spring 2013 Photorealistic 3D Rendering for VW in Mobile Devices Rafael Aroxa In the past few years, the demand for high performance
More informationInformation Driven Healthcare:
Information Driven Healthcare: Machine Learning course Lecture: Feature selection I --- Concepts Centre for Doctoral Training in Healthcare Innovation Dr. Athanasios Tsanas ( Thanasis ), Wellcome Trust
More informationCS 498 VR. Lecture 20-4/11/18. go.illinois.edu/vrlect20
CS 498 VR Lecture 20-4/11/18 go.illinois.edu/vrlect20 Review from last lecture Texture, Normal mapping Three types of optical distortion? How does texture mipmapping work? Improving Latency and Frame Rates
More informationA Thin-Client Approach for Porting OpenGL Applications to Pocket PC s
A Thin-Client Approach for Porting OpenGL Applications to Pocket PC s Zhe-Yu Lin Shyh-Haur Ger Yung-Feng Chiu Chun-Fa Chang Department of Computer Science National Tsing Hua University Abstract The display
More informationEfficient View-Dependent Sampling of Visual Hulls
Efficient View-Dependent Sampling of Visual Hulls Wojciech Matusik Chris Buehler Leonard McMillan Computer Graphics Group MIT Laboratory for Computer Science Cambridge, MA 02141 Abstract In this paper
More informationIMAGE PROCESSING AND IMAGE REGISTRATION ON SPIRAL ARCHITECTURE WITH salib
IMAGE PROCESSING AND IMAGE REGISTRATION ON SPIRAL ARCHITECTURE WITH salib Stefan Bobe 1 and Gerald Schaefer 2,* 1 University of Applied Sciences, Bielefeld, Germany. 2 School of Computing and Informatics,
More informationReference Stream Selection for Multiple Depth Stream Encoding
Reference Stream Selection for Multiple Depth Stream Encoding Sang-Uok Kum Ketan Mayer-Patel kumsu@cs.unc.edu kmp@cs.unc.edu University of North Carolina at Chapel Hill CB #3175, Sitterson Hall Chapel
More information