Two strategies for realistic rendering capture real world data synthesize from bottom up
|
|
- Della Short
- 5 years ago
- Views:
Transcription
1
2 Recap from Wednesday Two strategies for realistic rendering capture real world data synthesize from bottom up Both have existed for 500 years. Both are successful. Attempts to take the best of both world have been successful. We re going to take it further.
3 Administrative Stuff Any questions? Syllabus Textbook Matlab Tutorial Office hours James: Monday and Wednesday, 1pm to 2pm Sam: Sunday 7:30-9:30pm Emanuel: Monday 5-7pm Project 1 is out
4 Project 1
5 The Camera Many slides by Alexei A. Efros CS 129: Computational Photography James Hays, Brown, Spring 2011
6 How do we see the world? Let s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable image? Slide by Steve Seitz
7 Pinhole camera Add a barrier to block off most of the rays This reduces blurring The opening known as the aperture How does this transform the image? Slide by Steve Seitz
8 Pinhole camera model Pinhole model: Captures pencil of rays all rays through a single point The point is called Center of Projection (COP) The image is formed on the Image Plane Effective focal length f is distance from COP to Image Plane Slide by Steve Seitz
9 Dimensionality Reduction Machine (3D to 2D) 3D world 2D image Point of observation What have we lost? Angles Depth, lengths Figures Stephen E. Palmer, 2002
10 Funny things happen
11 Lengths can t be trusted... B C A Figure by David Forsyth
12 but humans adopt! Müller-Lyer Illusion We don t make measurements in the image plane
13 Modeling projection The coordinate system We will use the pin-hole model as an approximation Put the optical center (Center Of Projection) at the origin Put the image plane (Projection Plane) in front of the COP Why? The camera looks down the negative z axis we need this if we want right-handed-coordinates Slide by Steve Seitz
14 Modeling projection Projection equations Compute intersection with PP of ray from (x,y,z) to COP Derived using similar triangles We get the projection by throwing out the last coordinate: Slide by Steve Seitz
15 Homogeneous coordinates Is this a linear transformation? no division by z is nonlinear Trick: add one more coordinate: homogeneous image coordinates homogeneous scene coordinates Converting from homogeneous coordinates Slide by Steve Seitz
16 Perspective Projection Projection is a matrix multiply using homogeneous coordinates: divide by third coordinate This is known as perspective projection The matrix is the projection matrix Can also formulate as a 4x4 divide by fourth coordinate Slide by Steve Seitz
17 Orthographic Projection Special case of perspective projection Distance from the COP to the PP is infinite Image World Also called parallel projection What s the projection matrix? Slide by Steve Seitz
18 Building a real camera
19 Camera Obscura Camera Obscura, Gemma Frisius, 1558 The first camera Known to Aristotle Depth of the room is the effective focal length
20 Home-made pinhole camera Why so blurry?
21 Shrinking the aperture Less light gets through Why not make the aperture as small as possible? Less light gets through Diffraction effects Slide by Steve Seitz
22 Shrinking the aperture
23 The reason for lenses Slide by Steve Seitz
24 Focus
25 Focus and Defocus circle of confusion A lens focuses light onto the film There is a specific distance at which objects are in focus other points project to a circle of confusion in the image Changing the shape of the lens changes this distance Slide by Steve Seitz
26 Thin lenses Thin lens equation: Any object point satisfying this equation is in focus What is the shape of the focus region? How can we change the focus region? Thin lens applet: (by Fu-Kwun Hwang ) Slide by Steve Seitz
27 Depth Of Field
28 Depth of Field
29 Aperture controls Depth of Field Changing the aperture size affects depth of field A smaller aperture increases the range in which the object is approximately in focus But small aperture reduces amount of light need to increase exposure
30 Large apeture = small DOF Small apeture = large DOF Varying the aperture
31 Depth of Field
32 Field of View (Zoom)
33 Field of View (Zoom)
34 Field of View (Zoom) = Cropping
35 FOV depends of Focal Length f Smaller FOV = larger Focal Length
36 From Zisserman & Hartley
37 Field of View / Focal Length Large FOV, small f Camera close to car Small FOV, large f Camera far from the car
38 Fun with Focal Length (Jim Sherwood)
39 Lens Flaws
40 Lens Flaws: Chromatic Aberration Dispersion: wavelength-dependent refractive index (enables prism to spread white light beam into rainbow) Modifies ray-bending and lens focal length: f( ) color fringes near edges of image Corrections: add doublet lens of flint glass, etc.
41 Chromatic Aberration Near Lens Center Near Lens Outer Edge
42 Radial Distortion (e.g. Barrel and pin-cushion ) straight lines curve around the image center
43 Radial Distortion No distortion Pin cushion Barrel Radial distortion of the image Caused by imperfect lenses Deviations are most noticeable for rays that pass through the edge of the lens
The Camera : Computational Photography Alexei Efros, CMU, Fall 2008
The Camera 15-463: Computational Photography Alexei Efros, CMU, Fall 2008 How do we see the world? object film Let s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable
More informationThe Camera : Computational Photography Alexei Efros, CMU, Fall 2005
The Camera 15-463: Computational Photography Alexei Efros, CMU, Fall 2005 How do we see the world? object film Let s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable
More informationHow do we see the world?
The Camera 1 How do we see the world? Let s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable image? Credit: Steve Seitz 2 Pinhole camera Idea 2: Add a barrier to
More informationProjection. Readings. Szeliski 2.1. Wednesday, October 23, 13
Projection Readings Szeliski 2.1 Projection Readings Szeliski 2.1 Müller-Lyer Illusion by Pravin Bhat Müller-Lyer Illusion by Pravin Bhat http://www.michaelbach.de/ot/sze_muelue/index.html Müller-Lyer
More informationProjection. Announcements. Müller-Lyer Illusion. Image formation. Readings Nalwa 2.1
Announcements Mailing list (you should have received messages) Project 1 additional test sequences online Projection Readings Nalwa 2.1 Müller-Lyer Illusion Image formation object film by Pravin Bhat http://www.michaelbach.de/ot/sze_muelue/index.html
More informationProjection. Projection. Image formation. Müller-Lyer Illusion. Readings. Readings. Let s design a camera. Szeliski 2.1. Szeliski 2.
Projection Projection Readings Szeliski 2.1 Readings Szeliski 2.1 Müller-Lyer Illusion Image formation object film by Pravin Bhat http://www.michaelbach.de/ot/sze_muelue/index.html Let s design a camera
More informationTo Do. Advanced Computer Graphics. Outline. Computational Imaging. How do we see the world? Pinhole camera
Advanced Computer Graphics CSE 163 [Spring 2017], Lecture 14 Ravi Ramamoorthi http://www.cs.ucsd.edu/~ravir To Do Assignment 2 due May 19 Any last minute issues or questions? Next two lectures: Imaging,
More informationCameras. CSE 455, Winter 2010 January 25, 2010
Cameras CSE 455, Winter 2010 January 25, 2010 Announcements New Lecturer! Neel Joshi, Ph.D. Post-Doctoral Researcher Microsoft Research neel@cs Project 1b (seam carving) was due on Friday the 22 nd Project
More informationBuilding a Real Camera
Building a Real Camera Home-made pinhole camera Slide by A. Efros http://www.debevec.org/pinhole/ Shrinking the aperture Why not make the aperture as small as possible? Less light gets through Diffraction
More informationBuilding a Real Camera. Slides Credit: Svetlana Lazebnik
Building a Real Camera Slides Credit: Svetlana Lazebnik Home-made pinhole camera Slide by A. Efros http://www.debevec.org/pinhole/ Shrinking the aperture Why not make the aperture as small as possible?
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 5: Cameras and Projection Szeliski 2.1.3-2.1.6 Reading Announcements Project 1 assigned, see projects page: http://www.cs.cornell.edu/courses/cs6670/2011sp/projects/projects.html
More informationImage formation - Cameras. Grading & Project. About the course. Tentative Schedule. Course Content. Students introduction
About the course Instructors: Haibin Ling (hbling@temple, Wachman 35) Hours Lecture: Tuesda 5:3-8:pm, TTLMAN 43B Office hour: Tuesda 3: - 5:pm, or b appointment Textbook Computer Vision: Models, Learning,
More informationUnit 1: Image Formation
Unit 1: Image Formation 1. Geometry 2. Optics 3. Photometry 4. Sensor Readings Szeliski 2.1-2.3 & 6.3.5 1 Physical parameters of image formation Geometric Type of projection Camera pose Optical Sensor
More informationCSE 473/573 Computer Vision and Image Processing (CVIP)
CSE 473/573 Computer Vision and Image Processing (CVIP) Ifeoma Nwogu inwogu@buffalo.edu Lecture 4 Image formation(part I) Schedule Last class linear algebra overview Today Image formation and camera properties
More informationAnnouncement A total of 5 (five) late days are allowed for projects. Office hours
Announcement A total of 5 (five) late days are allowed for projects. Office hours Me: 3:50-4:50pm Thursday (or by appointment) Jake: 12:30-1:30PM Monday and Wednesday Image Formation Digital Camera Film
More informationOverview. Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image
Camera & Color Overview Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image Book: Hartley 6.1, Szeliski 2.1.5, 2.2, 2.3 The trip
More informationImage Formation. Dr. Gerhard Roth. COMP 4102A Winter 2015 Version 3
Image Formation Dr. Gerhard Roth COMP 4102A Winter 2015 Version 3 1 Image Formation Two type of images Intensity image encodes light intensities (passive sensor) Range (depth) image encodes shape and distance
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 4a: Cameras Source: S. Lazebnik Reading Szeliski chapter 2.2.3, 2.3 Image formation Let s design a camera Idea 1: put a piece of film in front of an object
More informationLENSES. INEL 6088 Computer Vision
LENSES INEL 6088 Computer Vision Digital camera A digital camera replaces film with a sensor array Each cell in the array is a Charge Coupled Device light-sensitive diode that converts photons to electrons
More informationComputer Vision. The Pinhole Camera Model
Computer Vision The Pinhole Camera Model Filippo Bergamasco (filippo.bergamasco@unive.it) http://www.dais.unive.it/~bergamasco DAIS, Ca Foscari University of Venice Academic year 2017/2018 Imaging device
More informationAcquisition. Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros
Acquisition Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros Image Acquisition Digital Camera Film Outline Pinhole camera Lens Lens aberrations Exposure Sensors Noise
More informationLecture 7: Camera Models
Lecture 7: Camera Models Professor Stanford Vision Lab 1 What we will learn toda? Pinhole cameras Cameras & lenses The geometr of pinhole cameras Reading: [FP]Chapters 1 3 [HZ] Chapter 6 2 What we will
More informationProj 2. Looks like the evaluation function changed in converting to Python, and 80% on Notre Dame is more tricky to reach.
Proj 2 Looks like the evaluation function changed in converting to Python, and 80% on Notre Dame is more tricky to reach. We will tweak the percentages. Leaderboard / Gradescope is up. Extra Credit Please
More informationIMAGE FORMATION. Light source properties. Sensor characteristics Surface. Surface reflectance properties. Optics
IMAGE FORMATION Light source properties Sensor characteristics Surface Exposure shape Optics Surface reflectance properties ANALOG IMAGES An image can be understood as a 2D light intensity function f(x,y)
More informationImage Formation. Dr. Gerhard Roth. COMP 4102A Winter 2014 Version 1
Image Formation Dr. Gerhard Roth COMP 4102A Winter 2014 Version 1 Image Formation Two type of images Intensity image encodes light intensities (passive sensor) Range (depth) image encodes shape and distance
More informationCameras. Shrinking the aperture. Camera trial #1. Pinhole camera. Digital Visual Effects Yung-Yu Chuang. Put a piece of film in front of an object.
Camera trial #1 Cameras Digital Visual Effects Yung-Yu Chuang scene film with slides by Fredo Durand, Brian Curless, Steve Seitz and Alexei Efros Put a piece of film in front of an object. Pinhole camera
More informationLenses, exposure, and (de)focus
Lenses, exposure, and (de)focus http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 15 Course announcements Homework 4 is out. - Due October 26
More informationApplied Optics. , Physics Department (Room #36-401) , ,
Applied Optics Professor, Physics Department (Room #36-401) 2290-0923, 019-539-0923, shsong@hanyang.ac.kr Office Hours Mondays 15:00-16:30, Wednesdays 15:00-16:30 TA (Ph.D. student, Room #36-415) 2290-0921,
More informationCameras. Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017
Cameras Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017 Camera Focus Camera Focus So far, we have been simulating pinhole cameras with perfect focus Often times, we want to simulate more
More informationCameras. Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/2/26. with slides by Fredo Durand, Brian Curless, Steve Seitz and Alexei Efros
Cameras Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/2/26 with slides by Fredo Durand, Brian Curless, Steve Seitz and Alexei Efros Camera trial #1 scene film Put a piece of film in front of
More informationLecture 02 Image Formation 1
Institute of Informatics Institute of Neuroinformatics Lecture 02 Image Formation 1 Davide Scaramuzza http://rpg.ifi.uzh.ch 1 Lab Exercise 1 - Today afternoon Room ETH HG E 1.1 from 13:15 to 15:00 Work
More informationCameras. Outline. Pinhole camera. Camera trial #1. Pinhole camera Film camera Digital camera Video camera
Outline Cameras Pinhole camera Film camera Digital camera Video camera Digital Visual Effects, Spring 2007 Yung-Yu Chuang 2007/3/6 with slides by Fredo Durand, Brian Curless, Steve Seitz and Alexei Efros
More informationLecture 2 Camera Models
Lecture 2 Camera Models Professor Silvio Savarese Computational Vision and Geometr Lab Silvio Savarese Lecture 2-4-Jan-4 Announcements Prerequisites: an questions? This course requires knowledge of linear
More informationSingle-view Metrology and Cameras
Single-view Metrology and Cameras 10/10/17 Computational Photography Derek Hoiem, University of Illinois Project 2 Results Incomplete list of great project pages Haohang Huang: Best presented project;
More informationImage Formation and Capture. Acknowledgment: some figures by B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, and A. Theuwissen
Image Formation and Capture Acknowledgment: some figures by B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, and A. Theuwissen Image Formation and Capture Real world Optics Sensor Devices Sources of Error
More informationVC 11/12 T2 Image Formation
VC 11/12 T2 Image Formation Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Miguel Tavares Coimbra Outline Computer Vision? The Human Visual System
More informationCS 443: Imaging and Multimedia Cameras and Lenses
CS 443: Imaging and Multimedia Cameras and Lenses Spring 2008 Ahmed Elgammal Dept of Computer Science Rutgers University Outlines Cameras and lenses! 1 They are formed by the projection of 3D objects.
More informationImage Formation III Chapter 1 (Forsyth&Ponce) Cameras Lenses & Sensors
Image Formation III Chapter 1 (Forsyth&Ponce) Cameras Lenses & Sensors Guido Gerig CS-GY 6643, Spring 2017 (slides modified from Marc Pollefeys, UNC Chapel Hill/ ETH Zurich, With content from Prof. Trevor
More informationImage Formation and Capture
Figure credits: B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, A. Theuwissen, and J. Malik Image Formation and Capture COS 429: Computer Vision Image Formation and Capture Real world Optics Sensor Devices
More informationVC 14/15 TP2 Image Formation
VC 14/15 TP2 Image Formation Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Miguel Tavares Coimbra Outline Computer Vision? The Human Visual System
More informationCameras and Sensors. Today. Today. It receives light from all directions. BIL721: Computational Photography! Spring 2015, Lecture 2!
!! Cameras and Sensors Today Pinhole camera! Lenses! Exposure! Sensors! photo by Abelardo Morell BIL721: Computational Photography! Spring 2015, Lecture 2! Aykut Erdem! Hacettepe University! Computer Vision
More informationTSBB09 Image Sensors 2018-HT2. Image Formation Part 1
TSBB09 Image Sensors 2018-HT2 Image Formation Part 1 Basic physics Electromagnetic radiation consists of electromagnetic waves With energy That propagate through space The waves consist of transversal
More information6.098 Digital and Computational Photography Advanced Computational Photography. Bill Freeman Frédo Durand MIT - EECS
6.098 Digital and Computational Photography 6.882 Advanced Computational Photography Bill Freeman Frédo Durand MIT - EECS Administrivia PSet 1 is out Due Thursday February 23 Digital SLR initiation? During
More informationCSE 527: Introduction to Computer Vision
CSE 527: Introduction to Computer Vision Week 2 - Class 2: Vision, Physics, Cameras September 7th, 2017 Today Physics Human Vision Eye Brain Perspective Projection Camera Models Image Formation Digital
More informationLenses. Overview. Terminology. The pinhole camera. Pinhole camera Lenses Principles of operation Limitations
Overview Pinhole camera Principles of operation Limitations 1 Terminology The pinhole camera The first camera - camera obscura - known to Aristotle. In 3D, we can visualize the blur induced by the pinhole
More informationChapter 25 Optical Instruments
Chapter 25 Optical Instruments Units of Chapter 25 Cameras, Film, and Digital The Human Eye; Corrective Lenses Magnifying Glass Telescopes Compound Microscope Aberrations of Lenses and Mirrors Limits of
More informationMirrors, Lenses &Imaging Systems
Mirrors, Lenses &Imaging Systems We describe the path of light as straight-line rays And light rays from a very distant point arrive parallel 145 Phys 24.1 Mirrors Standing away from a plane mirror shows
More informationCPSC 425: Computer Vision
1 / 55 CPSC 425: Computer Vision Instructor: Fred Tung ftung@cs.ubc.ca Department of Computer Science University of British Columbia Lecture Notes 2015/2016 Term 2 2 / 55 Menu January 7, 2016 Topics: Image
More informationChapter 18 Optical Elements
Chapter 18 Optical Elements GOALS When you have mastered the content of this chapter, you will be able to achieve the following goals: Definitions Define each of the following terms and use it in an operational
More informationCh 24. Geometric Optics
text concept Ch 24. Geometric Optics Fig. 24 3 A point source of light P and its image P, in a plane mirror. Angle of incidence =angle of reflection. text. Fig. 24 4 The blue dashed line through object
More informationLaboratory experiment aberrations
Laboratory experiment aberrations Obligatory laboratory experiment on course in Optical design, SK2330/SK3330, KTH. Date Name Pass Objective This laboratory experiment is intended to demonstrate the most
More informationName. Light Chapter Summary Cont d. Refraction
Page 1 of 17 Physics Week 12(Sem. 2) Name Light Chapter Summary Cont d with a smaller index of refraction to a material with a larger index of refraction, the light refracts towards the normal line. Also,
More informationVC 16/17 TP2 Image Formation
VC 16/17 TP2 Image Formation Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Hélder Filipe Pinto de Oliveira Outline Computer Vision? The Human Visual
More informationCS559: Computer Graphics. Lecture 2: Image Formation in Eyes and Cameras Li Zhang Spring 2008
CS559: Computer Graphics Lecture 2: Image Formation in Eyes and Cameras Li Zhang Spring 2008 Today Eyes Cameras Light Why can we see? Visible Light and Beyond Infrared, e.g. radio wave longer wavelength
More informationPHYS 1020 LAB 7: LENSES AND OPTICS. Pre-Lab
PHYS 1020 LAB 7: LENSES AND OPTICS Note: Print and complete the separate pre-lab assignment BEFORE the lab. Hand it in at the start of the lab. Pre-Lab Start by reading the entire prelab and lab write-up.
More informationWaves & Oscillations
Physics 42200 Waves & Oscillations Lecture 33 Geometric Optics Spring 2013 Semester Matthew Jones Aberrations We have continued to make approximations: Paraxial rays Spherical lenses Index of refraction
More informationChapter 23. Mirrors and Lenses
Chapter 23 Mirrors and Lenses Notation for Mirrors and Lenses The object distance is the distance from the object to the mirror or lens Denoted by p The image distance is the distance from the image to
More informationMirrors and Lenses. Images can be formed by reflection from mirrors. Images can be formed by refraction through lenses.
Mirrors and Lenses Images can be formed by reflection from mirrors. Images can be formed by refraction through lenses. Notation for Mirrors and Lenses The object distance is the distance from the object
More informationImage Formation: Camera Model
Image Formation: Camera Model Ruigang Yang COMP 684 Fall 2005, CS684-IBMR Outline Camera Models Pinhole Perspective Projection Affine Projection Camera with Lenses Digital Image Formation The Human Eye
More informationCH. 23 Mirrors and Lenses HW# 6, 7, 9, 11, 13, 21, 25, 31, 33, 35
CH. 23 Mirrors and Lenses HW# 6, 7, 9, 11, 13, 21, 25, 31, 33, 35 Mirrors Rays of light reflect off of mirrors, and where the reflected rays either intersect or appear to originate from, will be the location
More informationCHAPTER 18 REFRACTION & LENSES
Physics Approximate Timeline Students are expected to keep up with class work when absent. CHAPTER 18 REFRACTION & LENSES Day Plans for the day Assignments for the day 1 18.1 Refraction of Light o Snell
More informationLenses. A transparent object used to change the path of light Examples: Human eye Eye glasses Camera Microscope Telescope
SNC2D Lenses A transparent object used to change the path of light Examples: Human eye Eye glasses Camera Microscope Telescope Reading stones used by monks, nuns, and scholars ~1000 C.E. Lenses THERE ARE
More informationChapter 23. Mirrors and Lenses
Chapter 23 Mirrors and Lenses Mirrors and Lenses The development of mirrors and lenses aided the progress of science. It led to the microscopes and telescopes. Allowed the study of objects from microbes
More informationChapters 1 & 2. Definitions and applications Conceptual basis of photogrammetric processing
Chapters 1 & 2 Chapter 1: Photogrammetry Definitions and applications Conceptual basis of photogrammetric processing Transition from two-dimensional imagery to three-dimensional information Automation
More informationOptical Systems: Pinhole Camera Pinhole camera: simple hole in a box: Called Camera Obscura Aristotle discussed, Al-Hazen analyzed in Book of Optics
Optical Systems: Pinhole Camera Pinhole camera: simple hole in a box: Called Camera Obscura Aristotle discussed, Al-Hazen analyzed in Book of Optics 1011CE Restricts rays: acts as a single lens: inverts
More informationLecture 7: Camera Models
Lecture 7: Camera Models Professor Fei- Fei Li Stanford Vision Lab Lecture 7 -! 1 What we will learn toda? Pinhole cameras Cameras & lenses The geometr of pinhole cameras Reading: [FP] Chapters 1 3 [HZ]
More informationDigital Image Processing COSC 6380/4393
Digital Image Processing COSC 6380/4393 Lecture 2 Aug 24 th, 2017 Slides from Dr. Shishir K Shah, Rajesh Rao and Frank (Qingzhong) Liu 1 Instructor TA Digital Image Processing COSC 6380/4393 Pranav Mantini
More informationAstronomy 80 B: Light. Lecture 9: curved mirrors, lenses, aberrations 29 April 2003 Jerry Nelson
Astronomy 80 B: Light Lecture 9: curved mirrors, lenses, aberrations 29 April 2003 Jerry Nelson Sensitive Countries LLNL field trip 2003 April 29 80B-Light 2 Topics for Today Optical illusion Reflections
More informationImage Processing & Projective geometry
Image Processing & Projective geometry Arunkumar Byravan Partial slides borrowed from Jianbo Shi & Steve Seitz Color spaces RGB Red, Green, Blue HSV Hue, Saturation, Value Why HSV? HSV separates luma,
More informationDr F. Cuzzolin 1. September 29, 2015
P00407 Principles of Computer Vision 1 1 Department of Computing and Communication Technologies Oxford Brookes University, UK September 29, 2015 September 29, 2015 1 / 73 Outline of the Lecture 1 2 Basics
More informationCameras. Outline. Pinhole camera. Camera trial #1. Pinhole camera Film camera Digital camera Video camera High dynamic range imaging
Outline Cameras Pinhole camera Film camera Digital camera Video camera High dynamic range imaging Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2006/3/1 with slides by Fedro Durand, Brian Curless,
More informationImage Formation. Light from distant things. Geometrical optics. Pinhole camera. Chapter 36
Light from distant things Chapter 36 We learn about a distant thing from the light it generates or redirects. The lenses in our eyes create images of objects our brains can process. This chapter concerns
More informationLecture 7: homogeneous coordinates
Lecture 7: homogeneous Dr. Richard E. Turner (ret26@cam.ac.uk) October 31, 2013 House keeping webpage: http://cbl.eng.cam.ac.uk/public/turner/teaching Recap of last lecture: Pin hole camera image plane
More informationSNC2D PHYSICS 5/25/2013. LIGHT & GEOMETRIC OPTICS L Converging & Diverging Lenses (P ) Curved Lenses. Curved Lenses
SNC2D PHYSICS LIGHT & GEOMETRIC OPTICS L Converging & Diverging Lenses (P.448-450) Curved Lenses We see the world through lenses even if we do not wear glasses or contacts. We all have natural lenses in
More informationImage stitching. Image stitching. Video summarization. Applications of image stitching. Stitching = alignment + blending. geometrical registration
Image stitching Stitching = alignment + blending Image stitching geometrical registration photometric registration Digital Visual Effects, Spring 2006 Yung-Yu Chuang 2005/3/22 with slides by Richard Szeliski,
More informationLecture 2 Camera Models
Lecture 2 Camera Models Professor Silvio Savarese Computational Vision and Geometr Lab Silvio Savarese Lecture 2 - -Jan-8 Lecture 2 Camera Models Pinhole cameras Cameras lenses The geometr of pinhole cameras
More informationNotation for Mirrors and Lenses. Chapter 23. Types of Images for Mirrors and Lenses. More About Images
Notation for Mirrors and Lenses Chapter 23 Mirrors and Lenses Sections: 4, 6 Problems:, 8, 2, 25, 27, 32 The object distance is the distance from the object to the mirror or lens Denoted by p The image
More informationPHYSICS OPTICS. Mr Rishi Gopie
OPTICS Mr Rishi Gopie Ray Optics II Images formed by lens maybe real or virtual and may have different characteristics and locations that depend on: i) The type of lens involved, whether converging or
More informationCourse Syllabus OSE 3200 Geometric Optics
Course Syllabus OSE 3200 Geometric Optics Instructor: Dr. Kyle Renshaw Term: Fall 2016 Email: krenshaw@creol.ucf.edu Class Meeting Days: Monday/Wednesday Phone: 407-823-2807 Class Meeting Time: 10:30-11:45AM
More informationLab 2 Geometrical Optics
Lab 2 Geometrical Optics March 22, 202 This material will span much of 2 lab periods. Get through section 5.4 and time permitting, 5.5 in the first lab. Basic Equations Lensmaker s Equation for a thin
More informationNote on Posted Slides. Fermat s Principle of Least Time. History of Light. Law of Reflection The angle of reflection equals the angle of incidence.
Note on Posted Slides These are the slides that I intended to show in class on Thu. Apr. 3, 2014. They contain important ideas and questions from your reading. Due to time constraints, I was probably not
More informationNote on Posted Slides. Fermat s Principle of Least Time. History of Light. Law of Reflection The angle of reflection equals the angle of incidence.
Note on Posted Slides These are the slides that I intended to show in class on Wed. Apr. 3, 2013. They contain important ideas and questions from your reading. Due to time constraints, I was probably not
More informationComputational Photography and Video. Prof. Marc Pollefeys
Computational Photography and Video Prof. Marc Pollefeys Today s schedule Introduction of Computational Photography Course facts Syllabus Digital Photography What is computational photography Convergence
More informationChapter 28. Reflection and Refraction
Chapter 28 Reflection and Refraction Light takes the path from one point to another that is a. quickest. b. shortest. c. closest to a straight line. d. None of these. Light takes the path from one point
More informationChapter Ray and Wave Optics
109 Chapter Ray and Wave Optics 1. An astronomical telescope has a large aperture to [2002] reduce spherical aberration have high resolution increase span of observation have low dispersion. 2. If two
More informationAverage: Standard Deviation: Max: 99 Min: 40
1 st Midterm Exam Average: 83.1 Standard Deviation: 12.0 Max: 99 Min: 40 Please contact me to fix an appointment, if you took less than 65. Chapter 33 Lenses and Op/cal Instruments Units of Chapter 33
More informationGeneral Physics II. Ray Optics
General Physics II Ray Optics 1 Dispersion White light is a combination of all the wavelengths of the visible part of the electromagnetic spectrum. Red light has the longest wavelengths and violet light
More informationNotes from Lens Lecture with Graham Reed
Notes from Lens Lecture with Graham Reed Light is refracted when in travels between different substances, air to glass for example. Light of different wave lengths are refracted by different amounts. Wave
More informationDigital Image Processing COSC 6380/4393
Digital Image Processing COSC 6380/4393 Lecture 2 Aug 23 rd, 2018 Slides from Dr. Shishir K Shah, Rajesh Rao and Frank (Qingzhong) Liu 1 Instructor Digital Image Processing COSC 6380/4393 Pranav Mantini
More informationConverging and Diverging Surfaces. Lenses. Converging Surface
Lenses Sandy Skoglund 2 Converging and Diverging s AIR Converging If the surface is convex, it is a converging surface in the sense that the parallel rays bend toward each other after passing through the
More informationGEOMETRICAL OPTICS AND OPTICAL DESIGN
GEOMETRICAL OPTICS AND OPTICAL DESIGN Pantazis Mouroulis Associate Professor Center for Imaging Science Rochester Institute of Technology John Macdonald Senior Lecturer Physics Department University of
More informationConverging Lenses. Parallel rays are brought to a focus by a converging lens (one that is thicker in the center than it is at the edge).
Chapter 30: Lenses Types of Lenses Piece of glass or transparent material that bends parallel rays of light so they cross and form an image Two types: Converging Diverging Converging Lenses Parallel rays
More informationLenses Design Basics. Introduction. RONAR-SMITH Laser Optics. Optics for Medical. System. Laser. Semiconductor Spectroscopy.
Introduction Optics Application Lenses Design Basics a) Convex lenses Convex lenses are optical imaging components with positive focus length. After going through the convex lens, parallel beam of light
More informationChapter 23. Geometrical Optics: Mirrors and Lenses and other Instruments
Chapter 23 Geometrical Optics: Mirrors and Lenses and other Instruments HITT 1 You stand two feet away from a plane mirror. How far is it from you to your image? a. 2.0 ft b. 3.0 ft c. 4.0 ft d. 5.0 ft
More informationLecture 8 Camera Models
Lecture 8 Caera Models Professor Silvio Savarese Coputational Vision and Geoetr Lab Silvio Savarese Lecture 8-5-Oct-4 Lecture 8 Caera Models Pinhole caeras Caeras & lenses The geoetr of pinhole caeras
More informationCameras, lenses, and sensors
Cameras, lenses, and sensors Reading: Chapter 1, Forsyth & Ponce Optional: Section 2.1, 2.3, Horn. 6.801/6.866 Profs. Bill Freeman and Trevor Darrell Sept. 10, 2002 Today s lecture How many people would
More informationCriteria for Optical Systems: Optical Path Difference How do we determine the quality of a lens system? Several criteria used in optical design
Criteria for Optical Systems: Optical Path Difference How do we determine the quality of a lens system? Several criteria used in optical design Computer Aided Design Several CAD tools use Ray Tracing (see
More informationTest Review # 8. Physics R: Form TR8.17A. Primary colors of light
Physics R: Form TR8.17A TEST 8 REVIEW Name Date Period Test Review # 8 Light and Color. Color comes from light, an electromagnetic wave that travels in straight lines in all directions from a light source
More informationChapter 29/30. Wave Fronts and Rays. Refraction of Sound. Dispersion in a Prism. Index of Refraction. Refraction and Lenses
Chapter 29/30 Refraction and Lenses Refraction Refraction the bending of waves as they pass from one medium into another. Caused by a change in the average speed of light. Analogy A car that drives off
More informationR.B.V.R.R. WOMEN S COLLEGE (AUTONOMOUS) Narayanaguda, Hyderabad.
R.B.V.R.R. WOMEN S COLLEGE (AUTONOMOUS) Narayanaguda, Hyderabad. DEPARTMENT OF PHYSICS QUESTION BANK FOR SEMESTER III PAPER III OPTICS UNIT I: 1. MATRIX METHODS IN PARAXIAL OPTICS 2. ABERATIONS UNIT II
More information