Perception of Shape from Shading
|
|
- Anastasia Sims
- 5 years ago
- Views:
Transcription
1 1
2 Percetion of Shae from Shading Continuous image brightness variation due to shae variations is called shading Our ercetion of shae deends on shading Circular region on left is erceived as a flat disk Circular region on right has a varying brightness and is erceived as a shere
3 From Image to Shae Four main factors Geometry of the scene Reflectance of the visible surfaces Piel brightness Illumination direction and distribution Viewoint Can we comute scene geometry from distribution of iel brightness in scene image? Only in very simle situations Too many unknowns in general Viewoint Illumination Scene geometry 3
4 How Do We Do It? Humans have to make assumtions about illumination: bum (left) is erceived as hole (right) when uside down 4 Illumination direction is unknown. It is assumed to come from above
5 Does Shading Play a Central Role? Contour lays a more imortant role Variations in intensity are same on both shaes Uer region is erceived as comosed of three cylindrical ieces illuminated from above Lower region is erceived as sinusoidal, illuminated from one side Note the ambiguities of the surface ercetions, deending on assumed illumination direction ossible illumination hyotheses 5
6 Psychohysics (Percetion of Solid Shae from Shading, Mingolla & Todd, 1986) What assumtions do eole make about surface reflectance? Is an estimate of illumination direction necessary? Stimuli: Shaded ellisoids with varying Elongations Directions of light source Reflectance Cast shadows Test: judge direction of light and surface orientations at discrete oints 6
7 Results Task is hard: errors 15 to 0 degrees No effect of glossiness, no Lambertian surface assumtion No correlation between judgement of light directions and shae No rior estimate of light direction Poor discrimination between elongated and rounded ellisoids Qualitative information 7
8 Human Shading Interretation Is it metric or ordinal? Metric: deth Ordinal: deth order Answer: Ordinal, ualitative Magnitude of shading gradient is not imortant 8
9 Quantitative Shae Recovery Orthograhic rojection We have gray levels at iels (, y) We want to recover the orientations of the normals at oints (, y, z) By integration, we want to obtain z f (,y) Image lane (, y) Orthograhic rojection z θ (, y, z) z f (,y) 9
10 From Normals to Surface Shae Fit a surface that is locally erendicular to the normals 10
11 Review: Radiance Radiance L ( θ 1 ) is ower emitted er unit area (flu) into a cone having unit solid angle Area used is foreshortened area in direction θ 1 L ( θ 1 ) d P / (da cos θ 1 dω), in W/m /sr θ 1 dω Scene, da 11
12 Review: Reflectance Reflection is characterized by reflectance Reflectance is ratio radiance/irradiance Described by a function called Bidirectional Reflectance Distribution Function BRDF BRDF f (θ i,φ i, θ e,φ e ) L (θ e,φ e )/ de(θ i,φ i ) n (θ i,φ i ) (θ e,φ e ) θ e φ e 1
13 Review: Lambertian Surfaces If BRDF is a constant K, surface is called a Lambertian surface de L cos θ 0 dω k L cos θ 0 L K de K 1 L cos θ 0 Radiance is same in all directions and is roortional to cos θ 0 L θ 0 n θ 1 L Scene, da 13
14 14 Piel Brightness and Scene Brightness ) cos / ( cos cos / ( cos α θ α α Z da f da ) cos cos f Z da da θ α θ α π cos cos 4 3 Z D LdA dp θ α π cos cos 4 3 Z D da da L da dp E D f da W a L f D E α π 4 cos 4 da dp LdA Ω cosθ Z L E k
15 Simle Radiometric Modeling Piel Brightness is roortional to radiance of corresonding scene atch Radiance of scene atch is indeendent of viewoint Radiance of scene atch is roortional to cosine of angle between normal to atch and direction of illumination source Therefore iel brightness is roortional to cosine of angle between normal to atch and direction of illumination source 15
16 Normals to z f (, y) We intersect surface zf(,y) with red lane and blue lane We find tangents to red curve and blue curve We write that normal is erendicular to tangents and is in direction of cross-roduct 0 y 0 z f (, y 0 ) Normal 16 ( f /, f / y, y z f ( 0, y) z Red tangent ( 1, 0, f / ) Blue tangent ( 0, 1, f / y) 1)
17 Gradient Sace Orientations of normal ( f /, f / y, 1) can be reresented by arameters f f / d dy The comonents and are called the gradient sace coordinates of the normal / Any direction (a, b, c) can be reresented by (-a/c, -b/c, -1), and by a oint with comonents ( -a/c, -b/c) in the same D gradient sace Eamle: direction of light source can be written ( s, s ) 17
18 Geometric Interretation of Gradient Sace A direction (a, b, c) can be reresented by a oint on the lane Z -1 by constructing the intersection between the vector of same direction (drawn from the origin) and the lane (,, -1) Plane Z -1 (a, b, c) Origin Z0 Z 18
19 Reflectance Ma A reflectance ma is a D looku table that gives the iel brightness as a function of the orientation of the scene surface in camera coordinates 19
20 0 Reflectance Ma for Point Light Source and Lambertian Surface Piel brightness at iel (, y) is roortional to cosine of angle between normal to atch and direction of illumination source For a given iel brightness, the locus of ossible normals (,) in gradient sace is a conic 1 1),, ( 1 1),, ( ) cos( ), ( k k y I s s s s θ ' ) /, ( k k y I s s s s
21 Locus of Iso-Brightness in Reflectance Ma Surface normals that roduce a given brightness are at a constant angle with resect to direction of illumination The directions belong to a cone The locus corresonding to each brightness in the reflectance ma is the intersection of the cone with the lane Z -1 ( s, s ) (, ) Plane Z -1 For a given light source, maimum brightness occurs when (, ) ( s, s ) Z0 Z 1
22 Reflectance Ma Obtained by Calibration Object A shere can be used as a calibration object 1. Find distance of iel to center of shere. If distance < radius, comute direction of normal to shere surface, and (, ) for iel 3. At osition (, ) of reflectance ma, store iel value Useful only for scene material similar to shere Image lane (, y) R y, R R y y
23 Using Reflectance Ma to Find Normals We are on the image at a iel where we know the direction of the normal, a oint in the reflectance ma Find Gradient 1 at iel Find Gradient at reflectance ma oint Move in image by Gradient Then the corresonding oint in reflectance ma is moved by Gradient 1 Image y Nose ti Reflectance Ma 3
24 4 Proof Gradient 1 in image Gradient in reflectance ma If (d, dy) Gradient, Then (d, d) Gradient 1 y I I, R R, y I y R y R d I R R d dy d dy y d d
25 From Normals to Surface Shae Ste by ste Global least suare formulation leads to eression for Lalacian of z z + y z( + d, y + dy) z(, y) + dz z d + z dy d + y dz dy Second order differential euation 5
26 Alication to Face Recognition (Zhao and Chellaa) Aearance of faces changes when viewing and lighting directions change Face databases use front views and frontal lighting If we can reconstruct 3D face shae, we can convert any face image into a front-view with frontal lighting and comare to the database faces Use shae from shading and symmetry of face Or assume generic shae, but varying albedo, and remove unknown albedo by using symmetry of face Synthetic faces for 4 angles and illuminations 6
27 Photometric Stereo Viewoint Move light source at different known ositions to obtain different shadings of object with unknown geometry Find geometry from shading information 3 1 Scene geometry 7
28 Photometric Stereo Different illumination conditions lead to different reflectance mas Each reflectance ma can be comuted if we know osition of oint light source Intersection of iso-brightness contours corresonding to same brightness rovides ossible normal directions for iels having that brightness value Three mas give unambiguous normals for each iel I 19 Reflectance Ma 8
29 Assumtions of Shae from Shading Surfaces with constant albedo Orthograhic rojection Distant oint sources Absence of cast shadows Insignificance of secondary illumination This one is a real roblem: inter-reflections are everywhere 9
30 Inter-Reflections Illumination Gray levels with black surface Accurate comutation of shae from shading is unlikely to succeed in real world Shae from shading may be used as a comlementary rocess Edges are more reliable indicators of shae Gray levels with white surface 30
31 The Real World Diffuse light sources (overcast sky) Interreflections between surfaces generate secondary light sources Surfaces have varying light absortion (albedo) Surface reflections range from Lambertian to secular Surfaces cast shadows on each other 31
32 Conclusions Accurate comutation of shae from shading is unlikely to succeed in the real world Edges are more reliable indicators of shae Shae from shading may be used as a comlementary rocess in combination with shae inference from edges There is still a lot of research activity in this area, so it is useful to have an idea of the terminology and the techniues (reflectance ma, etc.) 3
33 References A Guided Tour of Comuter Vision, Vishvjit S. Nalwa, AT&T Press, 1993 Robot Vision, B.K.P. Horn, MIT Press Perceiving Shae from Shading, V.S. Ramachandran, Scientific American, 1988, SFS Based View Synthesis for Robust Face Recognition, W.Y. Zhao and R. Chellaa, 33
Perception of Shape from Shading. How Do We Do It? From Image to Shape. Does Shading Play a Central Role?
Percetion of Shae from Shading Continuou image brightne variation due to hae variation i called hading Our ercetion of hae deend on hading Circular region on left i erceived a a flat dik Circular region
More informationLast time: Disparity. Lecture 11: Stereo II. Last time: Triangulation. Last time: Multi-view geometry. Last time: Epipolar geometry
Last time: Disarity Lecture 11: Stereo II Thursday, Oct 4 CS 378/395T Prof. Kristen Grauman Disarity: difference in retinal osition of same item Case of stereo rig for arallel image lanes and calibrated
More informationPhotometric Stereo. Lighting and Photometric Stereo. Computer Vision I. Last lecture in a nutshell BRDF. CSE252A Lecture 7
Lighting and Photometric Stereo Photometric Stereo HW will be on web later today CSE5A Lecture 7 Radiometry of thin lenses δa Last lecture in a nutshell δa δa'cosα δacos β δω = = ( z' / cosα ) ( z / cosα
More informationAnnouncement. Lighting and Photometric Stereo. Computer Vision I. Surface Reflectance Models. Lambertian (Diffuse) Surface.
Lighting and Photometric Stereo CSE252A Lecture 7 Announcement Read Chapter 2 of Forsyth & Ponce Might find section 12.1.3 of Forsyth & Ponce useful. HW Problem Emitted radiance in direction f r for incident
More informationCS 450: COMPUTER GRAPHICS 2D TRANSFORMATIONS SPRING 2016 DR. MICHAEL J. REALE
CS 45: COMUTER GRAHICS 2D TRANSFORMATIONS SRING 26 DR. MICHAEL J. REALE INTRODUCTION Now that we hae some linear algebra under our resectie belts, we can start ug it in grahics! So far, for each rimitie,
More information11/2/2010. In the last lecture. Monte-Carlo Ray Tracing : Path Tracing. Today. Shadow ray towards the light at each vertex. Path Tracing : algorithm
Comuter Grahics Global Illumination: Monte-Carlo Ray Tracing and Photon Maing Lecture 11 In the last lecture We did ray tracing and radiosity Ray tracing is good to render secular objects but cannot handle
More informationGrouping of Patches in Progressive Radiosity
Grouing of Patches in Progressive Radiosity Arjan J.F. Kok * Abstract The radiosity method can be imroved by (adatively) grouing small neighboring atches into grous. Comutations normally done for searate
More informationGlobal Illumination with Photon Map Compensation
Institut für Comutergrahik und Algorithmen Technische Universität Wien Karlslatz 13/186/2 A-1040 Wien AUSTRIA Tel: +43 (1) 58801-18688 Fax: +43 (1) 58801-18698 Institute of Comuter Grahics and Algorithms
More informationShape from shading. Surface brightness and Surface Orientation --> Reflectance map READING: Nalwa Chapter 5. BKP Horn, Chapter 10.
Shape from shading Surface brightness and Surface Orientation --> Reflectance map READING: Nalwa Chapter 5. BKP Horn, Chapter 10. May 2004 SFS 1 Shading produces a compelling perception of 3-D shape. One
More informationCapturing light. Source: A. Efros
Capturing light Source: A. Efros Review Pinhole projection models What are vanishing points and vanishing lines? What is orthographic projection? How can we approximate orthographic projection? Lenses
More information521493S Computer Graphics Exercise 3 (Chapters 6-8)
521493S Comuter Grahics Exercise 3 (Chaters 6-8) 1 Most grahics systems and APIs use the simle lighting and reflection models that we introduced for olygon rendering Describe the ways in which each of
More informationImage Processing 1 (IP1) Bildverarbeitung 1
MIN-Fakultät Fachbereich Informatik Arbeitsbereich SAV/BV (KOGS) Image Processing 1 (IP1) Bildverarbeitung 1 Lecture 20: Shape from Shading Winter Semester 2015/16 Slides: Prof. Bernd Neumann Slightly
More informationRemember: The equation of projection. Imaging Geometry 1. Basic Geometric Coordinate Transforms. C306 Martin Jagersand
Imaging Geometr 1. Basic Geometric Coordinate Transorms emember: The equation o rojection Cartesian coordinates: (,, z) ( z, z ) C36 Martin Jagersand How do we develo a consistent mathematical ramework
More informationLearning Motion Patterns in Crowded Scenes Using Motion Flow Field
Learning Motion Patterns in Crowded Scenes Using Motion Flow Field Min Hu, Saad Ali and Mubarak Shah Comuter Vision Lab, University of Central Florida {mhu,sali,shah}@eecs.ucf.edu Abstract Learning tyical
More informationP Z. parametric surface Q Z. 2nd Image T Z
Direct recovery of shae from multile views: a arallax based aroach Rakesh Kumar. Anandan Keith Hanna Abstract Given two arbitrary views of a scene under central rojection, if the motion of oints on a arametric
More informationGeneral Principles of 3D Image Analysis
General Principles of 3D Image Analysis high-level interpretations objects scene elements Extraction of 3D information from an image (sequence) is important for - vision in general (= scene reconstruction)
More informationCENG 477 Introduction to Computer Graphics. Ray Tracing: Shading
CENG 477 Introduction to Computer Graphics Ray Tracing: Shading Last Week Until now we learned: How to create the primary rays from the given camera and image plane parameters How to intersect these rays
More informationIllumination Model. The governing principles for computing the. Apply the lighting model at a set of points across the entire surface.
Illumination and Shading Illumination (ighting) Model the interaction of light with surface oints to determine their final color and brightness OenG comutes illumination at vertices illumination Shading
More informationPhotometric Stereo.
Photometric Stereo Photometric Stereo v.s.. Structure from Shading [1] Photometric stereo is a technique in computer vision for estimating the surface normals of objects by observing that object under
More informationImage Formation. 2. Camera Geometry. Focal Length, Field Of View. Pinhole Camera Model. Computer Vision. Zoltan Kato
Image Formation 2. amera Geometr omuter Vision oltan Kato htt://www.in.u-seged.hu/~kato seged.hu/~kato/ 3D Scene Surace Light (Energ) Source inhole Lens Imaging lane World Otics Sensor Signal amera: Sec
More informationEE Light & Image Formation
EE 576 - Light & Electric Electronic Engineering Bogazici University January 29, 2018 EE 576 - Light & EE 576 - Light & The image of a three-dimensional object depends on: 1. Shape 2. Reflectance properties
More informationCS 428: Fall Introduction to. Geometric Transformations. Andrew Nealen, Rutgers, /15/2010 1
CS 428: Fall 21 Introduction to Comuter Grahics Geometric Transformations Andrew Nealen, Rutgers, 21 9/15/21 1 Toic overview Image formation and OenGL (last week) Modeling the image formation rocess OenGL
More informationShading Models. Simulate physical phenomena
Illumination Models & Shading Shading Models Simulate hysical henomena Real illumination simulation is comlicated & exensive Use aroximation and heuristics with little hysical basis that looks surrisingly
More informationIntroduction to Computer Vision. Week 8, Fall 2010 Instructor: Prof. Ko Nishino
Introduction to Computer Vision Week 8, Fall 2010 Instructor: Prof. Ko Nishino Midterm Project 2 without radial distortion correction with radial distortion correction Light Light Light! How do you recover
More informationRe-rendering from a Dense/Sparse Set of Images
Re-rendering from a Dense/Sparse Set of Images Ko Nishino Institute of Industrial Science The Univ. of Tokyo (Japan Science and Technology) kon@cvl.iis.u-tokyo.ac.jp Virtual/Augmented/Mixed Reality Three
More informationStructure from Motion
04/4/ Structure from Motion Comuter Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Many slides adated from Lana Lazebnik, Silvio Saverese, Steve Seitz his class: structure from motion Reca
More informationStarting this chapter
Computer Vision 5. Source, Shadow, Shading Department of Computer Engineering Jin-Ho Choi 05, April, 2012. 1/40 Starting this chapter The basic radiometric properties of various light sources Develop models
More informationAnd if that 120MP Camera was cool
Reflectance, Lights and on to photometric stereo CSE 252A Lecture 7 And if that 120MP Camera was cool Large Synoptic Survey Telescope 3.2Gigapixel camera 189 CCD s, each with 16 megapixels Pixels are 10µm
More informationFace Recognition Using Legendre Moments
Face Recognition Using Legendre Moments Dr.S.Annadurai 1 A.Saradha Professor & Head of CSE & IT Research scholar in CSE Government College of Technology, Government College of Technology, Coimbatore, Tamilnadu,
More informationL ENSES. Lenses Spherical refracting surfaces. n 1 n 2
Lenses 2 L ENSES 2. Sherical reracting suraces In order to start discussing lenses uantitatively, it is useul to consider a simle sherical surace, as shown in Fig. 2.. Our lens is a semi-ininte rod with
More informationCamera Models. Acknowledgements Used slides/content with permission from
Camera Models Acknowledgements Used slides/content with ermission rom Marc Polleeys or the slides Hartley and isserman: book igures rom the web Matthew Turk: or the slides Single view geometry Camera model
More informationIntroduction to Radiosity
Introduction to Radiosity Produce photorealistic pictures using global illumination Mathematical basis from the theory of heat transfer Enables color bleeding Provides view independent representation Unfortunately,
More informationPhotometric Stereo. Photometric Stereo. Shading reveals 3-D surface geometry BRDF. HW3 is assigned. An example of photometric stereo
Photometric Stereo Photometric Stereo HW3 is assigned Introduction to Computer Vision CSE5 Lecture 6 Shading reveals 3-D surface geometry Shape-from-shading: Use just one image to recover shape. Requires
More informationColour Reading: Chapter 6. Black body radiators
Colour Reading: Chapter 6 Light is produced in different amounts at different wavelengths by each light source Light is differentially reflected at each wavelength, which gives objects their natural colours
More informationCS 229 Final Project: Single Image Depth Estimation From Predicted Semantic Labels
CS 229 Final Project: Single Image Deth Estimation From Predicted Semantic Labels Beyang Liu beyangl@cs.stanford.edu Stehen Gould sgould@stanford.edu Prof. Dahne Koller koller@cs.stanford.edu December
More information6. Illumination, Lighting
Jorg s Graphics Lecture Notes 6. Illumination, Lighting 1 6. Illumination, Lighting No ray tracing in OpenGL! ray tracing: direct paths COP interreflection: soft shadows, color bleeding. umbra, penumbra,
More informationGEOMETRIC CONSTRAINT SOLVING IN < 2 AND < 3. Department of Computer Sciences, Purdue University. and PAMELA J. VERMEER
GEOMETRIC CONSTRAINT SOLVING IN < AND < 3 CHRISTOPH M. HOFFMANN Deartment of Comuter Sciences, Purdue University West Lafayette, Indiana 47907-1398, USA and PAMELA J. VERMEER Deartment of Comuter Sciences,
More informationCMSC 425: Lecture 16 Motion Planning: Basic Concepts
: Lecture 16 Motion lanning: Basic Concets eading: Today s material comes from various sources, including AI Game rogramming Wisdom 2 by S. abin and lanning Algorithms by S. M. LaValle (Chats. 4 and 5).
More informationRadiance. Radiance properties. Radiance properties. Computer Graphics (Fall 2008)
Computer Graphics (Fall 2008) COMS 4160, Lecture 19: Illumination and Shading 2 http://www.cs.columbia.edu/~cs4160 Radiance Power per unit projected area perpendicular to the ray per unit solid angle in
More informationReconstruction of Discrete Surfaces from Shading Images by Propagation of Geometric Features
Reconstruction of Discrete Surfaces from Shading Images by Propagation of Geometric Features Achille Braquelaire and Bertrand Kerautret LaBRI, Laboratoire Bordelais de Recherche en Informatique UMR 58,
More informationAnnouncements. Photometric Stereo. Shading reveals 3-D surface geometry. Photometric Stereo Rigs: One viewpoint, changing lighting
Announcements Today Photometric Stereo, next lecture return to stereo Photometric Stereo Introduction to Computer Vision CSE152 Lecture 16 Shading reveals 3-D surface geometry Two shape-from-x methods
More informationSources, shadows and shading
Sources, shadows and shading But how bright (or what colour) are objects? One more definition: Exitance of a source is the internally generated power radiated per unit area on the radiating surface similar
More informationAssignment #2. (Due date: 11/6/2012)
Computer Vision I CSE 252a, Fall 2012 David Kriegman Assignment #2 (Due date: 11/6/2012) Name: Student ID: Email: Problem 1 [1 pts] Calculate the number of steradians contained in a spherical wedge with
More informationOverview. Radiometry and Photometry. Foundations of Computer Graphics (Spring 2012)
Foundations of Computer Graphics (Spring 2012) CS 184, Lecture 21: Radiometry http://inst.eecs.berkeley.edu/~cs184 Overview Lighting and shading key in computer graphics HW 2 etc. ad-hoc shading models,
More informationChapter 8: Adaptive Networks
Chater : Adative Networks Introduction (.1) Architecture (.2) Backroagation for Feedforward Networks (.3) Jyh-Shing Roger Jang et al., Neuro-Fuzzy and Soft Comuting: A Comutational Aroach to Learning and
More information3D Computer Vision Camera Models
3D Comuter Vision Camera Models Nassir Navab based on a course given at UNC by Marc Pollefeys & the book Multile View Geometry by Hartley & Zisserman July 2, 202 chair for comuter aided medical rocedures
More informationA Survey of Light Source Detection Methods
A Survey of Light Source Detection Methods Nathan Funk University of Alberta Mini-Project for CMPUT 603 November 30, 2003 Abstract This paper provides an overview of the most prominent techniques for light
More informationAnnouncements. Radiometry and Sources, Shadows, and Shading
Announcements Radiometry and Sources, Shadows, and Shading CSE 252A Lecture 6 Instructor office hours This week only: Thursday, 3:45 PM-4:45 PM Tuesdays 6:30 PM-7:30 PM Library (for now) Homework 1 is
More informationDIRECT SHAPE FROM ISOPHOTES
DIRECT SHAPE FROM ISOPHOTES V.Dragnea, E.Angelopoulou Computer Science Department, Stevens Institute of Technology, Hoboken, NJ 07030, USA - (vdragnea, elli)@ cs.stevens.edu KEY WORDS: Isophotes, Reflectance
More informationConvex Hulls. Helen Cameron. Helen Cameron Convex Hulls 1/101
Convex Hulls Helen Cameron Helen Cameron Convex Hulls 1/101 What Is a Convex Hull? Starting Point: Points in 2D y x Helen Cameron Convex Hulls 3/101 Convex Hull: Informally Imagine that the x, y-lane is
More informationTD C. Space filling designs in R
TD C Sace filling designs in R 8/05/0 Tyical engineering ractice : One-At-a-Time (OAT design X P P3 P X Main remarks : OAT brings some information, but otentially wrong Eloration is oor : on monotonicity?
More informationMATHEMATICAL MODELING OF COMPLEX MULTI-COMPONENT MOVEMENTS AND OPTICAL METHOD OF MEASUREMENT
MATHEMATICAL MODELING OF COMPLE MULTI-COMPONENT MOVEMENTS AND OPTICAL METHOD OF MEASUREMENT V.N. Nesterov JSC Samara Electromechanical Plant, Samara, Russia Abstract. The rovisions of the concet of a multi-comonent
More informationLambertian model of reflectance I: shape from shading and photometric stereo. Ronen Basri Weizmann Institute of Science
Lambertian model of reflectance I: shape from shading and photometric stereo Ronen Basri Weizmann Institute of Science Variations due to lighting (and pose) Relief Dumitru Verdianu Flying Pregnant Woman
More informationSkeleton Cube for Lighting Environment Estimation
(MIRU2004) 2004 7 606 8501 E-mail: {takesi-t,maki,tm}@vision.kuee.kyoto-u.ac.jp 1) 2) Skeleton Cube for Lighting Environment Estimation Takeshi TAKAI, Atsuto MAKI, and Takashi MATSUYAMA Graduate School
More informationOptical Design with Zemax
Otical Design with Zemax Lecture 5: Imaging and illumination 01-09-11 Herbert Gross Summer term 01 www.ia.uni-jena.de Time schedule Contents 3 1. Fundamentals of Fourier otics. Physical otical image formation
More informationLast update: May 4, Vision. CMSC 421: Chapter 24. CMSC 421: Chapter 24 1
Last update: May 4, 200 Vision CMSC 42: Chapter 24 CMSC 42: Chapter 24 Outline Perception generally Image formation Early vision 2D D Object recognition CMSC 42: Chapter 24 2 Perception generally Stimulus
More informationA Novel Iris Segmentation Method for Hand-Held Capture Device
A Novel Iris Segmentation Method for Hand-Held Cature Device XiaoFu He and PengFei Shi Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, China {xfhe,
More informationRadiometry and reflectance
Radiometry and reflectance http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 16 Course announcements Homework 4 is still ongoing - Any questions?
More informationLecture 22: Basic Image Formation CAP 5415
Lecture 22: Basic Image Formation CAP 5415 Today We've talked about the geometry of scenes and how that affects the image We haven't talked about light yet Today, we will talk about image formation and
More informationShape from Second-bounce of Light Transport
Shae from Second-bounce of Light Transort Siying Liu 1, Tian-Tsong Ng 1, and Yasuyuki Matsushita 2 1 Institute for Infocomm Research Singaore 2 Microsoft Research Asia Abstract. This aer describes a method
More informationLecture 3: Geometric Algorithms(Convex sets, Divide & Conquer Algo.)
Advanced Algorithms Fall 2015 Lecture 3: Geometric Algorithms(Convex sets, Divide & Conuer Algo.) Faculty: K.R. Chowdhary : Professor of CS Disclaimer: These notes have not been subjected to the usual
More informationPassive 3D Photography
SIGGRAPH 99 Course on 3D Photography Passive 3D Photography Steve Seitz Carnegie Mellon University http:// ://www.cs.cmu.edu/~seitz Talk Outline. Visual Cues 2. Classical Vision Algorithms 3. State of
More informationCHAPTER 9. Classification Scheme Using Modified Photometric. Stereo and 2D Spectra Comparison
CHAPTER 9 Classification Scheme Using Modified Photometric Stereo and 2D Spectra Comparison 9.1. Introduction In Chapter 8, even we combine more feature spaces and more feature generators, we note that
More informationSPARSE SIGNAL REPRESENTATION FOR COMPLEX-VALUED IMAGING Sadegh Samadi 1, M üjdat Çetin 2, Mohammad Ali Masnadi-Shirazi 1
SPARSE SIGNAL REPRESENTATION FOR COMPLEX-VALUED IMAGING Sadegh Samadi 1, M üjdat Çetin, Mohammad Ali Masnadi-Shirazi 1 1. Shiraz University, Shiraz, Iran,. Sabanci University, Istanbul, Turkey ssamadi@shirazu.ac.ir,
More informationStereo Disparity Estimation in Moment Space
Stereo Disarity Estimation in oment Sace Angeline Pang Faculty of Information Technology, ultimedia University, 63 Cyberjaya, alaysia. angeline.ang@mmu.edu.my R. ukundan Deartment of Comuter Science, University
More informationUnderstanding Variability
Understanding Variability Why so different? Light and Optics Pinhole camera model Perspective projection Thin lens model Fundamental equation Distortion: spherical & chromatic aberration, radial distortion
More informationCS5670: Computer Vision
CS5670: Computer Vision Noah Snavely Light & Perception Announcements Quiz on Tuesday Project 3 code due Monday, April 17, by 11:59pm artifact due Wednesday, April 19, by 11:59pm Can we determine shape
More informationImage Formation: Light and Shading. Introduction to Computer Vision CSE 152 Lecture 3
Image Formation: Light and Shading CSE 152 Lecture 3 Announcements Homework 1 is due Apr 11, 11:59 PM Homework 2 will be assigned on Apr 11 Reading: Chapter 2: Light and Shading Geometric image formation
More informationEarthenware Reconstruction Based on the Shape Similarity among Potsherds
Original Paer Forma, 16, 77 90, 2001 Earthenware Reconstruction Based on the Shae Similarity among Potsherds Masayoshi KANOH 1, Shohei KATO 2 and Hidenori ITOH 1 1 Nagoya Institute of Technology, Gokiso-cho,
More information3D Motion Analysis Based on 2D Point Displacements
3D Motion Analysis Based on 2D Point Displacements 2D displacements of points observed on an unknown moving rigid body may provide information about - the 3D structure of the points - the 3D motion parameters
More informationImproved Image Super-Resolution by Support Vector Regression
Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, July 3 August 5, 0 Imroved Image Suer-Resolution by Suort Vector Regression Le An and Bir Bhanu Abstract Suort
More informationShading and Recognition OR The first Mrs Rochester. D.A. Forsyth, UIUC
Shading and Recognition OR The first Mrs Rochester D.A. Forsyth, UIUC Structure Argument: History why shading why shading analysis died reasons for hope Classical SFS+Critiques Primitives Reconstructions
More informationdq dt I = Irradiance or Light Intensity is Flux Φ per area A (W/m 2 ) Φ =
Radiometry (From Intro to Optics, Pedrotti -4) Radiometry is measurement of Emag radiation (light) Consider a small spherical source Total energy radiating from the body over some time is Q total Radiant
More informationGlobal Illumination. CMPT 361 Introduction to Computer Graphics Torsten Möller. Machiraju/Zhang/Möller
Global Illumination CMPT 361 Introduction to Computer Graphics Torsten Möller Reading Foley, van Dam (better): Chapter 16.7-13 Angel: Chapter 5.11, 11.1-11.5 2 Limitation of local illumination A concrete
More informationPhysics-based Vision: an Introduction
Physics-based Vision: an Introduction Robby Tan ANU/NICTA (Vision Science, Technology and Applications) PhD from The University of Tokyo, 2004 1 What is Physics-based? An approach that is principally concerned
More informationRadiometry. Reflectance & Lighting. Solid Angle. Radiance. Radiance Power is energy per unit time
Radiometry Reflectance & Lighting Computer Vision I CSE5A Lecture 6 Read Chapter 4 of Ponce & Forsyth Homework 1 Assigned Outline Solid Angle Irradiance Radiance BRDF Lambertian/Phong BRDF By analogy with
More information3D RECONSTRUCTION OF ROADS AND TREES FOR CITY MODELLING
3D RECONSTRUCTION OF ROADS AND TREES FOR CITY MODELLING George Vosselman Deartment of Geodesy, Delft University of Technology, Thijsseweg 11, NL-2629 JA Delft, The Netherlands g.vosselman@geo.tudelft.nl
More information3D Computer Vision. Photometric stereo. Prof. Didier Stricker
3D Computer Vision Photometric stereo Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 Physical parameters
More informationModule 5: Video Modeling Lecture 28: Illumination model. The Lecture Contains: Diffuse and Specular Reflection. Objectives_template
The Lecture Contains: Diffuse and Specular Reflection file:///d /...0(Ganesh%20Rana)/MY%20COURSE_Ganesh%20Rana/Prof.%20Sumana%20Gupta/FINAL%20DVSP/lecture%2028/28_1.htm[12/30/2015 4:22:29 PM] Diffuse and
More informationSelecting Discriminative Binary Patterns for a Local Feature
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 15, No 3 Sofia 015 rint ISSN: 1311-970; Online ISSN: 1314-4081 DOI: 10.1515/cait-015-0044 Selecting Discriminative Binary atterns
More informationHomework 4 Computer Vision CS 4731, Fall 2011 Due Date: Nov. 15, 2011 Total Points: 40
Homework 4 Computer Vision CS 4731, Fall 2011 Due Date: Nov. 15, 2011 Total Points: 40 Note 1: Both the analytical problems and the programming assignments are due at the beginning of class on Nov 15,
More informationCross products Line segments The convex combination of two distinct points p
CHAPTER Comutational Geometry Is the branch of comuter science that studies algorithms for solving geometric roblems. Has alications in many fields, including comuter grahics robotics, VLSI design comuter
More informationRealistic Image Synthesis
Realistic Image Synthesis - BRDFs and Direct ighting - Philipp Slusalle Karol Myszowsi Gurprit Singh Realistic Image Synthesis SS8 BRDFs and Direct ighting Importance Sampling Example Example: Generate
More informationComputer Graphics. Computer Graphics. Lecture 3 Line & Circle Drawing
Comuter Grahics Comuter Grahics Lecture 3 Line & Circle Drawing Comuter Grahics Towards the Ideal Line We can onl do a discrete aroimation Illuminate iels as close to the true ath as ossible, consider
More informationGabriel Taubin. Desktop 3D Photography
Sring 06 ENGN50 --- D Photograhy Lecture 7 Gabriel Taubin Brown University Deskto D Photograhy htt://www.vision.caltech.edu/bouguetj/iccv98/.index.html D triangulation: ray-lane Intersection lane ray intersection
More informationComputer Vision I - Algorithms and Applications: Multi-View 3D reconstruction
Computer Vision I - Algorithms and Applications: Multi-View 3D reconstruction Carsten Rother 09/12/2013 Computer Vision I: Multi-View 3D reconstruction Roadmap this lecture Computer Vision I: Multi-View
More informationOther approaches to obtaining 3D structure
Other approaches to obtaining 3D structure Active stereo with structured light Project structured light patterns onto the object simplifies the correspondence problem Allows us to use only one camera camera
More informationLecture 24: More on Reflectance CAP 5415
Lecture 24: More on Reflectance CAP 5415 Recovering Shape We ve talked about photometric stereo, where we assumed that a surface was diffuse Could calculate surface normals and albedo What if the surface
More informationAnnouncements. Lighting. Camera s sensor. HW1 has been posted See links on web page for readings on color. Intro Computer Vision.
Announcements HW1 has been posted See links on web page for readings on color. Introduction to Computer Vision CSE 152 Lecture 6 Deviations from the lens model Deviations from this ideal are aberrations
More informationAnnouncements. Image Formation: Light and Shading. Photometric image formation. Geometric image formation
Announcements Image Formation: Light and Shading Homework 0 is due Oct 5, 11:59 PM Homework 1 will be assigned on Oct 5 Reading: Chapters 2: Light and Shading CSE 252A Lecture 3 Geometric image formation
More informationLigh%ng and Reflectance
Ligh%ng and Reflectance 2 3 4 Ligh%ng Ligh%ng can have a big effect on how an object looks. Modeling the effect of ligh%ng can be used for: Recogni%on par%cularly face recogni%on Shape reconstruc%on Mo%on
More informationLecture 2: Fixed-Radius Near Neighbors and Geometric Basics
structure arises in many alications of geometry. The dual structure, called a Delaunay triangulation also has many interesting roerties. Figure 3: Voronoi diagram and Delaunay triangulation. Search: Geometric
More informationLecture 15: Shading-I. CITS3003 Graphics & Animation
Lecture 15: Shading-I CITS3003 Graphics & Animation E. Angel and D. Shreiner: Interactive Computer Graphics 6E Addison-Wesley 2012 Objectives Learn that with appropriate shading so objects appear as threedimensional
More informationAUTOMATIC 3D SURFACE RECONSTRUCTION BY COMBINING STEREOVISION WITH THE SLIT-SCANNER APPROACH
AUTOMATIC 3D SURFACE RECONSTRUCTION BY COMBINING STEREOVISION WITH THE SLIT-SCANNER APPROACH A. Prokos 1, G. Karras 1, E. Petsa 2 1 Deartment of Surveying, National Technical University of Athens (NTUA),
More informationLights, Surfaces, and Cameras. Light sources emit photons Surfaces reflect & absorb photons Cameras measure photons
Reflectance 1 Lights, Surfaces, and Cameras Light sources emit photons Surfaces reflect & absorb photons Cameras measure photons 2 Light at Surfaces Many effects when light strikes a surface -- could be:
More informationSOURCES, SHADOWS AND SHADING
Chapter 3 SOURCES, SHADOWS AND SHADING We shall start by describing the basic radiometric properties of various light sources. We shall then develop models of source geometries and discuss the radiosity
More informationdip3d Computing Structural Dip Program dip3d Computation flow chart Volumetric Attributes: Program dip3d
Comuting Structural Di Program di3d Comutation flow chart Program di3d uses a seismic amlitude volume as its inut and generates estimates of inline di, crossline di, di magnitude, di azimuth, and a confidence
More informationCALCULATION METHOD OF MILLING CONTACT AREA FOR BALL-END MILLING TOOL WITH TOOL INCLINATION ANGLE
U.P.B. Sci. Bull., Series D, Vol. 76, Iss. 3, 214 ISSN 1454-2358 CALCULATION METHOD OF MILLING CONTACT AREA FOR BALL-END MILLING TOOL WITH TOOL INCLINATION ANGLE Yishu HAO 1, Guoqing TANG 2, Meng ZHANG
More informationxy plane was set to coincide with the dorsal surface of the pronotum. (4) The locust coordinate
4 5 6 7 8 9 0 4 5 6 7 8 9 0 Sulementary text Coordinate systems and kinematic analysis. Four coordinate systems were defined in order to analyse data obtained from the video (Fig. S): () the video coordinate
More informationPrecomputed Radiance Transfer: Theory and Practice
1 Precomputed Radiance Transfer: Peter-Pike Sloan Microsoft Jaakko Lehtinen Helsinki Univ. of Techn. & Remedy Entertainment Jan Kautz MIT 2 Introduction Jan Kautz MIT 3 Introduction We see here an example
More information