Dynamic Tone Mapping with Head-Mounted Displays
|
|
- Randell Bond
- 6 years ago
- Views:
Transcription
1 Dynmic Tone Mpping with Hed-Mounted Displys Mtt Yu Deprtment of Electricl Engineering, Stnford University Abstrct The rel world consists of mny scenes which contin high dynmic rnge. While modern cmers re cpble of cpturing the dynmic rnge of these scenes, displys still only show low dynmic rnge. Mny tone mp opertors exist but very few consider the use of hed-mounted displys. We crete dynmic tone mp opertor for use on pnorm high dynmic rnge imges by considering user s hed position nd subsequent viewport. The tone mp opertor normlizes the imge shown to the user by the log verge luminnce of the viewport. Furthermore, we use simple model of eye dpttion to mimic the effects of light nd drk dpttion. A simple A/B test shows our dynmic tone opertor is preferred over stndrd globl tone mp opertor. 1. Introduction Recently, there hs been surge in virtul nd ugment relity technologies. At the forefront of these technologies re hed-mounted displys which include Oculus Rift, Microsoft HoloLens, nd the HTC Vive. While the min driving force behind these devices hs been to crete compelling gmes, mny other pplictions cn tke dvntge of the immersive experience provided by hed-mounted displys. One such ppliction is using one or more cmers to cpture pictures or video of the world round single point. When these pictures or videos re viewed on hedmounted disply, user s cn view the world s if they re stnding t the cptured loction. This immersive content cn consist of very high dynmic rnge (HDR) scenes. Consider, for exmple, typicl outdoor scene. While trditionl photogrpher cn choose to shoot wy from the sun, photogrpher trying to cpture ll the views round fixed point will inevitbly tke picture where the sun is present. Thus, multiple exposures could be used to cpture such n HDR scene. However, modern displys cn still only show limited dynmic rnge. The problem of HDR tone mpping is the process of reducing the dynmic rnge of HDR content such tht the content cn be displyed on regulr, limited dynmic Figure 1: HDR content tone mpped using the Reinhrd globl TMO [12]. Since pnorm content is sphericl in nture, the content must be mpped to plne for trditionl disply. In this cse, the equirectngulr projection is used. rnge disply. While there hs been lot of work on HDR tone mpping for trditionl imges, there hs been reltively little work on HDR tone mpping for pnorm HDR content. The two min contributions of our work re the following: We propose new HDR tone mpping opertor which tkes into ccount the fct tht user only looks t portion of n HDR pnorm. We introduce simple method to mimic light nd drk dpttion in humn vision. Fig. 1 shows n exmple of the HDR content1 used in this project. 2. Relted Work HDR tone mpping for trditionl plnr imges is well studied field. In this section, we offer very brief nd incomplete review. However, for reltively thorough review of tone mpping, [13] my be consulted. 1 HDR pnorms used in this project cn be found t nd
2 Generlly, HDR tone mpping opertors cn be broken down into globl opertors nd locl opertors. Globl opertors pply the sme mpping to ll pixels nd re generlly fst nd computtionlly efficient. Locl opertors, on the other hnd, vry sptilly by considering smll neighborhood round ech pixel. While more computtionlly demnding, locl opertors my preserve locl contrst better thn globl opertors. Some exmples of globl tone mpping opertors include scling the dynmic rnge by the scene s key vlue [12] nd dptive logrithmic mpping [2]. Moreover, both globl nd locl tone mpping opertors my consider the perceptul response of the humn visul system [7, 9, 11, 5, 10, 8] to generte more relistic imges. Some exmples of locl tone mpping opertors include grdient domin HDR compression [4] nd bilterl filtering [3]. Recently, there hs even been work on temporlly coherent tone mp opertors for use in such pplictions s HDR video [6, 1]. However, while there is plenty of work on HDR for imges nd videos presented on stndrd displys, there hs been reltively few work on HDR tone mpping for use with hed-mounted displys. Perhps the closest work is [14] which performs tone-mpping with hed-mounted disply but only in the context of low-vision id nd not for the genertion of ccurte or plesing imges. 3. Method Due to the lck of work regrding HDR tone mpping for hed-mounted displys, this work begins by considering how to extend simple globl opertor for use in the sitution when user only looks t portion of the imge. Then, simple model for humn eye dpttion is introduced in the second hlf of the section Viewport Luminnce Adjustment Scling the dynmic rnge of n imge by the scene s key vlue cn be seen s setting the exposure on cmer. The key vlue cn be pproximted by the log-verge luminnce [12, 13] of the imge pixels: L w = X 1 exp( log(δ + )) N x,y () (b) Figure 2: () The user s field of view (pproximted by the red box) cn be significntly smller thn the entire pnorm. (b) The log verge luminnce of the viewport surrounding ech pixel. bright objects will be perceptible. On the other hnd, detil round drk objects will be more perceptible if the key vlue is very drk. This mpping, unfortuntely, tkes into ccount ll vlues in the HDR pnorm in order to compute the key vlue. Since the user only looks t portion of pnorm t time (s shown in Fig. 2), more ccurte mpping should consider only the portion which the user cn see with hed-mounted disply. Thus, we introduce seprte key vlue for ech user viewport. Specificlly, we cn now introduce n dditionl temporl component to the key vlue clcultion: (1) L w (V (t)) = X 1 exp( (log(δ + ))) N (3) x,y V (t) where is the world luminnce of the pixels t loction x, y. Then, the displyed luminnce cn be clculted s: (2) Ld (x, y) = L w where is user prmeter specifying the vlue which the key vlue of the scene is mpped. Thus, we cn see tht, if the scene is bright, i.e., the vlue of L w is lrge, then the dynmic rnge will be mpped such tht detils round so tht the key vlue is clculted only over the pixels in the user s viewport t given time. The displyed luminnce cn be modified ccordingly: Ld (x, y, t) = L w (V (t)) (4) Note tht the clcultion of the viewport which user views is complicted by the fct tht the viewport is pro-
3 jection of the pnorm onto rectngulr plne. One potentil solution is to compute the log verge luminnce t run time, thus ensuring the log verge is computed over the correct vlues. However, this introduces noticeble nd uncceptble dely into hed-mounted disply system which requires very low ltency. To mitigte this problem, the log verge luminnce ws clculted offline nd stored s lookup tble t run time (see Fig. 2b). Furthermore, the viewport ws pproximted by lrge window in the equirectngulr pnorm domin. This pproximtion works well t the regions corresponding to the equtor but contins lrge distortions ner the poles Simple Adpttion Model While there hs been much prior work modeling the dpttion of the humn visul system, this work ims only to simulte smll fctor. In prticulr, while light dpttion (going from drk bckground to bright bckground) occurs quickly, drk dpttion occurs reltively slowly. We rewrite our displyed luminnce s: (5) Ld (x, y, t) = y(t) Without considering dpttion, we hve (s in our previous eqution): y(t) = L w (V (t)) (6) Figure 3: Simple response curves for light nd drk dpttion. Note tht the updte occurs in the liner luminnce domin. user would view different portion of the pnorm depending on their viewing direction. Due to the simplicity of the tone mpping opertion nd the use of offline computtions, the system rn t greter thn 60fps resulting in smooth opertion with the hed-mounted disply. Fig. 4 To consider dpttion, we introduce the following updte rule: y(t) = αl w (V (t)) + (1 α)y(t 1) (7) This results in rpid pproch to the trget vlue where the rte of pproch decys s the trget vlue is reched. See Fig. 3 for n illustrtion of the behvior of the updte rule. While the behvior is similr for both drk nd light dpttion in the liner luminnce domin, the perceptible effect is different. As described by Weber s lw, chnges in luminnce re more perceptible t low bckground intensities thn t high bckground intensities. Thus, modeling drk dpttion s n exponentil decy to the trget vlue will be perceived s liner drop in key vlue. Modeling light dpttion s n exponentil rise to the trget vlue will be perceived s much fster rise to the key vlue. In other words, while our updte rule is the sme for both drk nd light dpttion, the user will feel s if drk dpttion occurs reltively slower thn light dpttion. 4. Results This system ws deployed using n OpenGL pnorm viewer in combintion with n Oculus Rift DK2. HDR content long with offline computtions (e.g,. viewport luminnce verges) were loded nd shders were used to dynmiclly tone mp HDR content to resulting texture. These textures were mpped to spheres so tht the Figure 4: Globl (left eye) vs. viewport (right eye) tone mpping opertors. The top view nd bottom view shows how the viewport method chnges its tone mpping method bsed on the viewble pixels.
4 5. Discussion We introduced dynmic tone mpping opertor which tkes into ccount tht user wers hed-mounted disply to view n HDR pnorm. This llows us consider only the pixels displyed to the user t ny given time rther thn ll pixels. This simple tone mpping opertor resulted in rel-time processing, suitble for use with hed-mounted disply. Furthermore, we introduced simple dpttion model which ccounted for the fct tht drk dpttion tkes reltively longer mount of time thn light dpttion. The performnce of our new tone mpping opertor ws verified with smll subjective study. 6. Future Work Figure 5: Drk dpttion simultion. The user hs just viewed bright scene nd strts viewing drk region t t = 0. As time progresses, the viewport gets brighter to simulte the effect of the user dpting from light to drk region. shows the difference between using globl tone mpping opertor nd the viewport tone mpping opertor used in this report. The globl opertor uses the sme function s the viewport opertor except tht the key vlue pproximtion is computed over the entire imge rther thn just the viewport. Fig. 5 shows the effect of drk dpttion with the globl tone mpping opertor used gin for comprison. While the dpttion model is simple, it produces temporlly smooth nd plesing result. To verify these results, smll subjective test ws performed. A stndrd A/B comprison ws used to compre the globl nd viewport bsed tone mpping opertors. 8 dults rnging between were shown results from both tone mpping opertors nd sked which they preferred. The results re shown in Tb. 1. While the test ws smll, there is cler preference towrds the viewport tone mpping opertor. Globl Viewport Tble 1: Results from n A/B comprison between using globl vs. viewport tone mpping opertor. Number represents the count of people who preferred tht method. One person noted the differences between the methods but could not choose which he preferred (hence the 0.5). There re lest three mjor venues still left for explortion. First, the dpttion model used in this report ws very simple. There hs been much work in ccurtely modeling the dpttion of the humn visul system nd pplying the concepts lerned in this re could led to more relistic result. Second, humns perceive objects in their fovel vision different thn in their peripherl vision. In prticulr, detil cn only be perceived in the fovel region. This suggests tht tone mpping opertor for hed-mounted displys should tret these regions differently. Third, eye-trcking would llow the tone mpper to know exctly wht user is looking t. The limittions of the hed-mounted disply re tht user does not lwys look directly t the center pixels. These possibilities, long with the rpid development of new hed-mounted displys nd even HDR pnorm video, mke the study of HDR with hed-mounted displys n interesting topic to study further. References [1] T. O. Aydin, N. Stefnoski, S. Croci, M. H. Gross, nd A. Smolic. Temporlly coherent locl tone mpping of HDR video. ACM Trns. Grph. (), 33(6):196 13, [2] F. Drgo, K. Myszkowski, T. Annen, nd N. Chib. Adptive Logrithmic Mpping For Displying High Contrst Scenes. Comput. Grph. Forum (), 22(3): , [3] F. Durnd nd J. Dorsey. Fst bilterl filtering for the disply of high-dynmic-rnge imges. SIGGRAPH, 21(3): , [4] R. Fttl, D. Lischinski, nd M. Wermn. Grdient domin high dynmic rnge compression. SIGGRAPH, 21(3): , [5] J. A. Ferwerd, S. N. Pttnik, P. Shirley, nd D. P. Greenberg. A Model of Visul Adpttion for Rel-
5 istic Imge Synthesis. SIGGRAPH, pges , [6] S. B. Kng, M. Uyttendele, S. Winder, nd R. Szeliski. High dynmic rnge video. ACM Trnsctions on Grphics, 22(3): , July [7] P. Ledd, L. P. Sntos, nd A. Chlmers. A locl model of eye dpttion for high dynmic rnge imges. Afrigrph, pges , [8] R. Mntiuk, S. J. Dly, nd L. Kerofsky. Disply dptive tone mpping. ACM Trns. Grph. (TOG) 27(3), 27(3):1, [9] R. Mntiuk, K. Myszkowski, nd H.-P. Seidel. A perceptul frmework for contrst processing of high dynmic rnge imges. TAP, 3(3): , [10] S. N. Pttnik, J. Tumblin, Y. H. Yee, nd D. P. Greenberg. Time-dependent visul dpttion for fst relistic imge disply. SIGGRAPH, pges 47 54, [11] E. Reinhrd nd K. Devlin. Dynmic Rnge Reduction Inspired by Photoreceptor Physiology. IEEE Trns. Vis. Comput. Grph. (), 11(1):13 24, [12] E. Reinhrd, M. M. Strk, P. Shirley, nd J. A. Ferwerd. Photogrphic tone reproduction for digitl imges. SIGGRAPH, 21(3): , [13] E. Reinhrd, G. Wrd, S. N. Pttnik, P. E. Debevec, nd W. Heidrich. High Dynmic Rnge Imging - Acquisition, Disply, nd Imge-Bsed Lighting (2. ed.). Acdemic Press, [14] R. Ure, P. Mrtnez-Cd, J. Gmez-Lpez, C. Morills, nd F. Pelyo. Rel-time tone mpping on gpu nd fpg. EURASIP Journl on Imge nd Video Processing, 2012(1), 2012.
Geometric transformations
Geometric trnsformtions Computer Grphics Some slides re bsed on Shy Shlom slides from TAU mn n n m m T A,,,,,, 2 1 2 22 12 1 21 11 Rows become columns nd columns become rows nm n n m m A,,,,,, 1 1 2 22
More informationBefore We Begin. Introduction to Spatial Domain Filtering. Introduction to Digital Image Processing. Overview (1): Administrative Details (1):
Overview (): Before We Begin Administrtive detils Review some questions to consider Winter 2006 Imge Enhncement in the Sptil Domin: Bsics of Sptil Filtering, Smoothing Sptil Filters, Order Sttistics Filters
More informationUnit #9 : Definite Integral Properties, Fundamental Theorem of Calculus
Unit #9 : Definite Integrl Properties, Fundmentl Theorem of Clculus Gols: Identify properties of definite integrls Define odd nd even functions, nd reltionship to integrl vlues Introduce the Fundmentl
More informationParallel Square and Cube Computations
Prllel Squre nd Cube Computtions Albert A. Liddicot nd Michel J. Flynn Computer Systems Lbortory, Deprtment of Electricl Engineering Stnford University Gtes Building 5 Serr Mll, Stnford, CA 945, USA liddicot@stnford.edu
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology
More informationTilt-Sensing with Kionix MEMS Accelerometers
Tilt-Sensing with Kionix MEMS Accelerometers Introduction Tilt/Inclintion sensing is common ppliction for low-g ccelerometers. This ppliction note describes how to use Kionix MEMS low-g ccelerometers to
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology
More informationEngineer To Engineer Note
Engineer To Engineer Note EE-169 Technicl Notes on using Anlog Devices' DSP components nd development tools Contct our technicl support by phone: (800) ANALOG-D or e-mil: dsp.support@nlog.com Or visit
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationA New Learning Algorithm for the MAXQ Hierarchical Reinforcement Learning Method
A New Lerning Algorithm for the MAXQ Hierrchicl Reinforcement Lerning Method Frzneh Mirzzdeh 1, Bbk Behsz 2, nd Hmid Beigy 1 1 Deprtment of Computer Engineering, Shrif University of Technology, Tehrn,
More informationComplete Coverage Path Planning of Mobile Robot Based on Dynamic Programming Algorithm Peng Zhou, Zhong-min Wang, Zhen-nan Li, Yang Li
2nd Interntionl Conference on Electronic & Mechnicl Engineering nd Informtion Technology (EMEIT-212) Complete Coverge Pth Plnning of Mobile Robot Bsed on Dynmic Progrmming Algorithm Peng Zhou, Zhong-min
More informationMA1008. Calculus and Linear Algebra for Engineers. Course Notes for Section B. Stephen Wills. Department of Mathematics. University College Cork
MA1008 Clculus nd Liner Algebr for Engineers Course Notes for Section B Stephen Wills Deprtment of Mthemtics University College Cork s.wills@ucc.ie http://euclid.ucc.ie/pges/stff/wills/teching/m1008/ma1008.html
More informationOn the Detection of Step Edges in Algorithms Based on Gradient Vector Analysis
On the Detection of Step Edges in Algorithms Bsed on Grdient Vector Anlysis A. Lrr6, E. Montseny Computer Engineering Dept. Universitt Rovir i Virgili Crreter de Slou sin 43006 Trrgon, Spin Emil: lrre@etse.urv.es
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology
More informationOverview. Network characteristics. Network architecture. Data dissemination. Network characteristics (cont d) Mobile computing and databases
Overview Mobile computing nd dtbses Generl issues in mobile dt mngement Dt dissemintion Dt consistency Loction dependent queries Interfces Detils of brodcst disks thlis klfigopoulos Network rchitecture
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationAn Integrated Simulation System for Human Factors Study
An Integrted Simultion System for Humn Fctors Study Ying Wng, Wei Zhng Deprtment of Industril Engineering, Tsinghu University, Beijing 100084, Chin Foud Bennis, Dmien Chblt IRCCyN, Ecole Centrle de Nntes,
More informationImproper Integrals. October 4, 2017
Improper Integrls October 4, 7 Introduction We hve seen how to clculte definite integrl when the it is rel number. However, there re times when we re interested to compute the integrl sy for emple 3. Here
More informationL. Yaroslavsky. Fundamentals of Digital Image Processing. Course
L. Yroslvsky. Fundmentls of Digitl Imge Processing. Course 0555.330 Lecture. Imge enhncement.. Imge enhncement s n imge processing tsk. Clssifiction of imge enhncement methods Imge enhncement is processing
More informationLecture 10 Evolutionary Computation: Evolution strategies and genetic programming
Lecture 10 Evolutionry Computtion: Evolution strtegies nd genetic progrmming Evolution strtegies Genetic progrmming Summry Negnevitsky, Person Eduction, 2011 1 Evolution Strtegies Another pproch to simulting
More informationAccelerating 3D convolution using streaming architectures on FPGAs
Accelerting 3D convolution using streming rchitectures on FPGAs Hohun Fu, Robert G. Clpp, Oskr Mencer, nd Oliver Pell ABSTRACT We investigte FPGA rchitectures for ccelerting pplictions whose dominnt cost
More information1. SEQUENCES INVOLVING EXPONENTIAL GROWTH (GEOMETRIC SEQUENCES)
Numbers nd Opertions, Algebr, nd Functions 45. SEQUENCES INVOLVING EXPONENTIAL GROWTH (GEOMETRIC SEQUENCES) In sequence of terms involving eponentil growth, which the testing service lso clls geometric
More informationA COLOUR CORRECTION PREPROCESSING METHOD FOR MULTIVIEW VIDEO CODING
A COLOR CORRECTO REROCESSG METHOD FOR MLTEW DEO CODG Colin Doutre nd nos siopoulos Deprtment of Electricl nd Computer Engineering, niversity of British Columbi 66 Min Mll, 6T Z4, ncouver, BC, Cnd emil:
More informationAgilent Mass Hunter Software
Agilent Mss Hunter Softwre Quick Strt Guide Use this guide to get strted with the Mss Hunter softwre. Wht is Mss Hunter Softwre? Mss Hunter is n integrl prt of Agilent TOF softwre (version A.02.00). Mss
More informationDTM: Dynamic Tone Mapping for Backlight Scaling
38.1 : Dynmic Tone Mpping for Bcklight Scling Ali Irnli University of Southern Cliforni irnli@usc.edu Abstrct This pper proposes n pproch for pixel trnsformtion of the displyed imge to increse the potentil
More informationGENERATING ORTHOIMAGES FOR CLOSE-RANGE OBJECTS BY AUTOMATICALLY DETECTING BREAKLINES
GENEATING OTHOIMAGES FO CLOSE-ANGE OBJECTS BY AUTOMATICALLY DETECTING BEAKLINES Efstrtios Stylinidis 1, Lzros Sechidis 1, Petros Ptis 1, Spiros Sptls 2 Aristotle University of Thessloniki 1 Deprtment of
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationStained Glass Design. Teaching Goals:
Stined Glss Design Time required 45-90 minutes Teching Gols: 1. Students pply grphic methods to design vrious shpes on the plne.. Students pply geometric trnsformtions of grphs of functions in order to
More information3.5.1 Single slit diffraction
3.5.1 Single slit diffrction Wves pssing through single slit will lso diffrct nd produce n interference pttern. The reson for this is to do with the finite width of the slit. We will consider this lter.
More informationDynamic Programming. Andreas Klappenecker. [partially based on slides by Prof. Welch] Monday, September 24, 2012
Dynmic Progrmming Andres Klppenecker [prtilly bsed on slides by Prof. Welch] 1 Dynmic Progrmming Optiml substructure An optiml solution to the problem contins within it optiml solutions to subproblems.
More information6.3 Volumes. Just as area is always positive, so is volume and our attitudes towards finding it.
6.3 Volumes Just s re is lwys positive, so is volume nd our ttitudes towrds finding it. Let s review how to find the volume of regulr geometric prism, tht is, 3-dimensionl oject with two regulr fces seprted
More informationA Heuristic Approach for Discovering Reference Models by Mining Process Model Variants
A Heuristic Approch for Discovering Reference Models by Mining Process Model Vrints Chen Li 1, Mnfred Reichert 2, nd Andres Wombcher 3 1 Informtion System Group, University of Twente, The Netherlnds lic@cs.utwente.nl
More informationUnit 5 Vocabulary. A function is a special relationship where each input has a single output.
MODULE 3 Terms Definition Picture/Exmple/Nottion 1 Function Nottion Function nottion is n efficient nd effective wy to write functions of ll types. This nottion llows you to identify the input vlue with
More informationFig.1. Let a source of monochromatic light be incident on a slit of finite width a, as shown in Fig. 1.
Answer on Question #5692, Physics, Optics Stte slient fetures of single slit Frunhofer diffrction pttern. The slit is verticl nd illuminted by point source. Also, obtin n expression for intensity distribution
More informationHVS-Aware Dynamic Backlight Scaling in TFT LCD s
HVS-Awre Dynmic Bcklight Scling in TFT LCD s Ali Irnli, Wonbok Lee, nd Mssoud Pedrm Dept. of Electricl Engineering University of Southern Cliforni Los Angeles CA 90089 Abstrct: Liquid Crystl Displys hve
More informationEngineer To Engineer Note
Engineer To Engineer Note EE-186 Technicl Notes on using Anlog Devices' DSP components nd development tools Contct our technicl support by phone: (800) ANALOG-D or e-mil: dsp.support@nlog.com Or visit
More informationP(r)dr = probability of generating a random number in the interval dr near r. For this probability idea to make sense we must have
Rndom Numers nd Monte Crlo Methods Rndom Numer Methods The integrtion methods discussed so fr ll re sed upon mking polynomil pproximtions to the integrnd. Another clss of numericl methods relies upon using
More informationIf f(x, y) is a surface that lies above r(t), we can think about the area between the surface and the curve.
Line Integrls The ide of line integrl is very similr to tht of single integrls. If the function f(x) is bove the x-xis on the intervl [, b], then the integrl of f(x) over [, b] is the re under f over the
More informationAVolumePreservingMapfromCubetoOctahedron
Globl Journl of Science Frontier Reserch: F Mthemtics nd Decision Sciences Volume 18 Issue 1 Version 1.0 er 018 Type: Double Blind Peer Reviewed Interntionl Reserch Journl Publisher: Globl Journls Online
More informationRadiometric Compensation of Images Projected on Non-White Surfaces by Exploiting Chromatic Adaptation and Perceptual Anchoring
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < Rdiometric Compenstion of Imges Projected on Non-White Surfces by Exploiting Chromtic Adpttion nd Perceptul Anchoring
More information4452 Mathematical Modeling Lecture 4: Lagrange Multipliers
Mth Modeling Lecture 4: Lgrnge Multipliers Pge 4452 Mthemticl Modeling Lecture 4: Lgrnge Multipliers Lgrnge multipliers re high powered mthemticl technique to find the mximum nd minimum of multidimensionl
More informationIntroduction to Computer Engineering EECS 203 dickrp/eecs203/ CMOS transmission gate (TG) TG example
Introduction to Computer Engineering EECS 23 http://ziyng.eecs.northwestern.edu/ dickrp/eecs23/ CMOS trnsmission gte TG Instructor: Robert Dick Office: L477 Tech Emil: dickrp@northwestern.edu Phone: 847
More information3.5.1 Single slit diffraction
3..1 Single slit diffrction ves pssing through single slit will lso diffrct nd produce n interference pttern. The reson for this is to do with the finite width of the slit. e will consider this lter. Tke
More informationSection 10.4 Hyperbolas
66 Section 10.4 Hyperbols Objective : Definition of hyperbol & hyperbols centered t (0, 0). The third type of conic we will study is the hyperbol. It is defined in the sme mnner tht we defined the prbol
More informationCHAPTER III IMAGE DEWARPING (CALIBRATION) PROCEDURE
CHAPTER III IMAGE DEWARPING (CALIBRATION) PROCEDURE 3.1 Scheimpflug Configurtion nd Perspective Distortion Scheimpflug criterion were found out to be the best lyout configurtion for Stereoscopic PIV, becuse
More informationA Fast Imaging Algorithm for Near Field SAR
Journl of Computing nd Electronic Informtion Mngement ISSN: 2413-1660 A Fst Imging Algorithm for Ner Field SAR Guoping Chen, Lin Zhng, * College of Optoelectronic Engineering, Chongqing University of Posts
More informationSolutions to Math 41 Final Exam December 12, 2011
Solutions to Mth Finl Em December,. ( points) Find ech of the following its, with justifiction. If there is n infinite it, then eplin whether it is or. ( ) / ln() () (5 points) First we compute the it:
More informationFile Manager Quick Reference Guide. June Prepared for the Mayo Clinic Enterprise Kahua Deployment
File Mnger Quick Reference Guide June 2018 Prepred for the Myo Clinic Enterprise Khu Deployment NVIGTION IN FILE MNGER To nvigte in File Mnger, users will mke use of the left pne to nvigte nd further pnes
More informationDistributed Systems Principles and Paradigms
Distriuted Systems Principles nd Prdigms Chpter 11 (version April 7, 2008) Mrten vn Steen Vrije Universiteit Amsterdm, Fculty of Science Dept. Mthemtics nd Computer Science Room R4.20. Tel: (020) 598 7784
More informationMidterm 2 Sample solution
Nme: Instructions Midterm 2 Smple solution CMSC 430 Introduction to Compilers Fll 2012 November 28, 2012 This exm contins 9 pges, including this one. Mke sure you hve ll the pges. Write your nme on the
More informationReplicating Web Applications On-Demand
Replicting Web Applictions On-Demnd Swminthn Sivsubrmnin Guillume Pierre Mrten vn Steen Dept. of Computer Science, Vrije Universiteit, Amsterdm {swmi,gpierre,steen}@cs.vu.nl Abstrct Mny Web-bsed commercil
More informationHW Stereotactic Targeting
HW Stereotctic Trgeting We re bout to perform stereotctic rdiosurgery with the Gmm Knife under CT guidnce. We instrument the ptient with bse ring nd for CT scnning we ttch fiducil cge (FC). Above: bse
More informationIllumination and Shading
Illumintion nd hding In order to produce relistic imges, we must simulte the ppernce of surfces under vrious lighting conditions. Illumintion models: given the illumintion incident t point on surfce, wht
More informationChapter Spline Method of Interpolation More Examples Electrical Engineering
Chpter. Spline Method of Interpoltion More Exmples Electricl Engineering Exmple Thermistors re used to mesure the temperture of bodies. Thermistors re bsed on mterils chnge in resistnce with temperture.
More information12-B FRACTIONS AND DECIMALS
-B Frctions nd Decimls. () If ll four integers were negtive, their product would be positive, nd so could not equl one of them. If ll four integers were positive, their product would be much greter thn
More informationx )Scales are the reciprocal of each other. e
9. Reciprocls A Complete Slide Rule Mnul - eville W Young Chpter 9 Further Applictions of the LL scles The LL (e x ) scles nd the corresponding LL 0 (e -x or Exmple : 0.244 4.. Set the hir line over 4.
More information1 Quad-Edge Construction Operators
CS48: Computer Grphics Hndout # Geometric Modeling Originl Hndout #5 Stnford University Tuesdy, 8 December 99 Originl Lecture #5: 9 November 99 Topics: Mnipultions with Qud-Edge Dt Structures Scribe: Mike
More information10.5 Graphing Quadratic Functions
0.5 Grphing Qudrtic Functions Now tht we cn solve qudrtic equtions, we wnt to lern how to grph the function ssocited with the qudrtic eqution. We cll this the qudrtic function. Grphs of Qudrtic Functions
More informationUSING HOUGH TRANSFORM IN LINE EXTRACTION
Stylinidis, Efstrtios USING HOUGH TRANSFORM IN LINE EXTRACTION Efstrtios STYLIANIDIS, Petros PATIAS The Aristotle University of Thessloniki, Deprtment of Cdstre Photogrmmetry nd Crtogrphy Univ. Box 473,
More informationMisrepresentation of Preferences
Misrepresenttion of Preferences Gicomo Bonnno Deprtment of Economics, University of Cliforni, Dvis, USA gfbonnno@ucdvis.edu Socil choice functions Arrow s theorem sys tht it is not possible to extrct from
More informationCSEP 573 Artificial Intelligence Winter 2016
CSEP 573 Artificil Intelligence Winter 2016 Luke Zettlemoyer Problem Spces nd Serch slides from Dn Klein, Sturt Russell, Andrew Moore, Dn Weld, Pieter Abbeel, Ali Frhdi Outline Agents tht Pln Ahed Serch
More informationQuestions About Numbers. Number Systems and Arithmetic. Introduction to Binary Numbers. Negative Numbers?
Questions About Numbers Number Systems nd Arithmetic or Computers go to elementry school How do you represent negtive numbers? frctions? relly lrge numbers? relly smll numbers? How do you do rithmetic?
More informationAn Efficient Divide and Conquer Algorithm for Exact Hazard Free Logic Minimization
An Efficient Divide nd Conquer Algorithm for Exct Hzrd Free Logic Minimiztion J.W.J.M. Rutten, M.R.C.M. Berkelr, C.A.J. vn Eijk, M.A.J. Kolsteren Eindhoven University of Technology Informtion nd Communiction
More informationThe gamuts of input and output colour imaging media
In Proceedings of IS&T/SPIE Electronic Imging 1 The gmuts of input nd output colour imging medi án Morovic,* Pei Li Sun* nd Peter Morovic * Colour & Imging Institute, University of Dery, UK School of Informtion
More informationII. THE ALGORITHM. A. Depth Map Processing
Lerning Plnr Geometric Scene Context Using Stereo Vision Pul G. Bumstrck, Bryn D. Brudevold, nd Pul D. Reynolds {pbumstrck,brynb,pulr2}@stnford.edu CS229 Finl Project Report December 15, 2006 Abstrct A
More informationPointwise convergence need not behave well with respect to standard properties such as continuity.
Chpter 3 Uniform Convergence Lecture 9 Sequences of functions re of gret importnce in mny res of pure nd pplied mthemtics, nd their properties cn often be studied in the context of metric spces, s in Exmples
More informationLECT-10, S-1 FP2P08, Javed I.
A Course on Foundtions of Peer-to-Peer Systems & Applictions LECT-10, S-1 CS /799 Foundtion of Peer-to-Peer Applictions & Systems Kent Stte University Dept. of Computer Science www.cs.kent.edu/~jved/clss-p2p08
More informationAlignment of Long Sequences. BMI/CS Spring 2012 Colin Dewey
Alignment of Long Sequences BMI/CS 776 www.biostt.wisc.edu/bmi776/ Spring 2012 Colin Dewey cdewey@biostt.wisc.edu Gols for Lecture the key concepts to understnd re the following how lrge-scle lignment
More information9.1 apply the distance and midpoint formulas
9.1 pply the distnce nd midpoint formuls DISTANCE FORMULA MIDPOINT FORMULA To find the midpoint between two points x, y nd x y 1 1,, we Exmple 1: Find the distnce between the two points. Then, find the
More informationMATH 25 CLASS 5 NOTES, SEP
MATH 25 CLASS 5 NOTES, SEP 30 2011 Contents 1. A brief diversion: reltively prime numbers 1 2. Lest common multiples 3 3. Finding ll solutions to x + by = c 4 Quick links to definitions/theorems Euclid
More informationTransparent neutral-element elimination in MPI reduction operations
Trnsprent neutrl-element elimintion in MPI reduction opertions Jesper Lrsson Träff Deprtment of Scientific Computing University of Vienn Disclimer Exploiting repetition nd sprsity in input for reducing
More informationIntegration. September 28, 2017
Integrtion September 8, 7 Introduction We hve lerned in previous chpter on how to do the differentition. It is conventionl in mthemtics tht we re supposed to lern bout the integrtion s well. As you my
More informationSketching Reaction-Diffusion Texture
EUROGRAPHICS Workshop on Sketch-Bsed Interfces nd Modeling (006), pp. 1 8 Sketching Rection-Diffusion Texture Pper ID 100 Abstrct In this work, we present n interctive interfce for sketching synthesized
More informationComputing offsets of freeform curves using quadratic trigonometric splines
Computing offsets of freeform curves using qudrtic trigonometric splines JIULONG GU, JAE-DEUK YUN, YOONG-HO JUNG*, TAE-GYEONG KIM,JEONG-WOON LEE, BONG-JUN KIM School of Mechnicl Engineering Pusn Ntionl
More informationMATH 2530: WORKSHEET 7. x 2 y dz dy dx =
MATH 253: WORKSHT 7 () Wrm-up: () Review: polr coordintes, integrls involving polr coordintes, triple Riemnn sums, triple integrls, the pplictions of triple integrls (especilly to volume), nd cylindricl
More informationCSCI 104. Rafael Ferreira da Silva. Slides adapted from: Mark Redekopp and David Kempe
CSCI 0 fel Ferreir d Silv rfsilv@isi.edu Slides dpted from: Mrk edekopp nd Dvid Kempe LOG STUCTUED MEGE TEES Series Summtion eview Let n = + + + + k $ = #%& #. Wht is n? n = k+ - Wht is log () + log ()
More informationDigital Design. Chapter 6: Optimizations and Tradeoffs
Digitl Design Chpter 6: Optimiztions nd Trdeoffs Slides to ccompny the tetbook Digitl Design, with RTL Design, VHDL, nd Verilog, 2nd Edition, by Frnk Vhid, John Wiley nd Sons Publishers, 2. http://www.ddvhid.com
More informationSlides for Data Mining by I. H. Witten and E. Frank
Slides for Dt Mining y I. H. Witten nd E. Frnk Simplicity first Simple lgorithms often work very well! There re mny kinds of simple structure, eg: One ttriute does ll the work All ttriutes contriute eqully
More informationWhat do all those bits mean now? Number Systems and Arithmetic. Introduction to Binary Numbers. Questions About Numbers
Wht do ll those bits men now? bits (...) Number Systems nd Arithmetic or Computers go to elementry school instruction R-formt I-formt... integer dt number text chrs... floting point signed unsigned single
More informationMTH 146 Conics Supplement
105- Review of Conics MTH 146 Conics Supplement In this section we review conics If ou ne more detils thn re present in the notes, r through section 105 of the ook Definition: A prol is the set of points
More informationSUPPLEMENTARY INFORMATION
Supplementry Figure y (m) x (m) prllel perpendiculr Distnce (m) Bird Stndrd devition for distnce (m) c 6 prllel perpendiculr 4 doi:.8/nture99 SUPPLEMENTARY FIGURE Confirmtion tht movement within the flock
More informationFrom Dependencies to Evaluation Strategies
From Dependencies to Evlution Strtegies Possile strtegies: 1 let the user define the evlution order 2 utomtic strtegy sed on the dependencies: use locl dependencies to determine which ttriutes to compute
More informationMath 17 - Review. Review for Chapter 12
Mth 17 - eview Ying Wu eview for hpter 12 1. Given prmetric plnr curve x = f(t), y = g(t), where t b, how to eliminte the prmeter? (Use substitutions, or use trigonometry identities, etc). How to prmeterize
More informationA Priority-based Distributed Call Admission Protocol for Multi-hop Wireless Ad hoc Networks
A Priority-bsed Distributed Cll Admission Protocol for Multi-hop Wireless Ad hoc Networks un Sun Elizbeth M. Belding-Royer Deprtment of Computer Science University of Cliforni, Snt Brbr suny, ebelding
More informationTixeo compared to other videoconferencing solutions
compred to other videoconferencing solutions for V171026EN , unique solution on the video conferencing field Adobe Connect Web RTC Vydio for High security level, privcy Zero impct on network security policies
More informationA REINFORCEMENT LEARNING APPROACH TO SCHEDULING DUAL-ARMED CLUSTER TOOLS WITH TIME VARIATIONS
A REINFORCEMENT LEARNING APPROACH TO SCHEDULING DUAL-ARMED CLUSTER TOOLS WITH TIME VARIATIONS Ji-Eun Roh (), Te-Eog Lee (b) (),(b) Deprtment of Industril nd Systems Engineering, Kore Advnced Institute
More informationSolving Problems by Searching. CS 486/686: Introduction to Artificial Intelligence Winter 2016
Solving Prolems y Serching CS 486/686: Introduction to Artificil Intelligence Winter 2016 1 Introduction Serch ws one of the first topics studied in AI - Newell nd Simon (1961) Generl Prolem Solver Centrl
More informationMemory-Optimized Software Synthesis from Dataflow Program Graphs withlargesizedatasamples
EURSIP Journl on pplied Signl Processing 2003:6, 54 529 c 2003 Hindwi Publishing orportion Memory-Optimized Softwre Synthesis from tflow Progrm Grphs withlrgesizetsmples Hyunok Oh The School of Electricl
More informationCS311H: Discrete Mathematics. Graph Theory IV. A Non-planar Graph. Regions of a Planar Graph. Euler s Formula. Instructor: Işıl Dillig
CS311H: Discrete Mthemtics Grph Theory IV Instructor: Işıl Dillig Instructor: Işıl Dillig, CS311H: Discrete Mthemtics Grph Theory IV 1/25 A Non-plnr Grph Regions of Plnr Grph The plnr representtion of
More informationCHAPTER 5 Spline Approximation of Functions and Data
CHAPTER 5 Spline Approximtion of Functions nd Dt This chpter introduces number of methods for obtining spline pproximtions to given functions, or more precisely, to dt obtined by smpling function. In Section
More information2 Computing all Intersections of a Set of Segments Line Segment Intersection
15-451/651: Design & Anlysis of Algorithms Novemer 14, 2016 Lecture #21 Sweep-Line nd Segment Intersection lst chnged: Novemer 8, 2017 1 Preliminries The sweep-line prdigm is very powerful lgorithmic design
More informationMath 142, Exam 1 Information.
Mth 14, Exm 1 Informtion. 9/14/10, LC 41, 9:30-10:45. Exm 1 will be bsed on: Sections 7.1-7.5. The corresponding ssigned homework problems (see http://www.mth.sc.edu/ boyln/sccourses/14f10/14.html) At
More informationVulnerability Analysis of Electric Power Communication Network. Yucong Wu
2nd Interntionl Conference on Advnces in Mechnicl Engineering nd Industril Informtics (AMEII 2016 Vulnerbility Anlysis of Electric Power Communiction Network Yucong Wu Deprtment of Telecommunictions Engineering,
More informationMath 35 Review Sheet, Spring 2014
Mth 35 Review heet, pring 2014 For the finl exm, do ny 12 of the 15 questions in 3 hours. They re worth 8 points ech, mking 96, with 4 more points for netness! Put ll your work nd nswers in the provided
More information1 Drawing 3D Objects in Adobe Illustrator
Drwing 3D Objects in Adobe Illustrtor 1 1 Drwing 3D Objects in Adobe Illustrtor This Tutoril will show you how to drw simple objects with three-dimensionl ppernce. At first we will drw rrows indicting
More informationINTRODUCTION TO SIMPLICIAL COMPLEXES
INTRODUCTION TO SIMPLICIAL COMPLEXES CASEY KELLEHER AND ALESSANDRA PANTANO 0.1. Introduction. In this ctivity set we re going to introduce notion from Algebric Topology clled simplicil homology. The min
More information6.2 Volumes of Revolution: The Disk Method
mth ppliction: volumes by disks: volume prt ii 6 6 Volumes of Revolution: The Disk Method One of the simplest pplictions of integrtion (Theorem 6) nd the ccumultion process is to determine so-clled volumes
More information