Light. Computer Vision. James Hays
|
|
- Rodney Martin
- 5 years ago
- Views:
Transcription
1
2
3 Light Computer Vision James Hays
4 Projection: world coordinatesimage coordinates Camera Center (,, ) z y x X... f z y ' ' v u x. v u z f x u * ' z f y v * ' 5 2 ' 2* u 5 2 ' 3* v If X = 2, Y = 3, Z = 5, and f = 2 What are U and V? 3 5-2? z y f v '
5 Interlude: why does this matter?
6 Relating multiple views
7 X x K I z y x f f v u w K Slide Credit: Savarese Projection matrix Intrinsic Assumptions Unit aspect ratio Optical center at (,) No skew Extrinsic Assumptions No rotation Camera at (,,) X x
8 Remove assumption: optical center at origin X x K I z y x v f u f v u w Intrinsic Assumptions Unit aspect ratio No skew Extrinsic Assumptions No rotation Camera at (,,)
9 Remove assumption: square pixels X x K I z y x v u v u w Intrinsic Assumptions No skew Extrinsic Assumptions No rotation Camera at (,,)
10 Remove assumption: non-skewed pixels X x K I z y x v u s v u w Intrinsic Assumptions Extrinsic Assumptions No rotation Camera at (,,) Note: different books use different notation for parameters
11 Oriented and Translated Camera R j w t X k w O w x i w
12 Allow camera translation X t x K I z y x t t t v u v u w z y x Intrinsic Assumptions Extrinsic Assumptions No rotation
13 3D Rotation of Points Rotation around the coordinate axes, counter-clockwise: 1 cos sin sin cos ) ( cos sin 1 sin cos ) ( cos sin sin cos 1 ) ( z y x R R R p p y z Slide Credit: Saverese
14 Allow camera rotation X t x K R z y x t r r r t r r r t r r r v u s v u w z y x
15 Degrees of freedom X t x K R z y x t r r r t r r r t r r r v u s v u w z y x 5 6
16 Reminder: read your book Lectures have assigned readings Szeliski 2.1 and especially cover the geometry of image formation
17 Field of View (Zoom, focal length)
18 Things to remember Vanishing points and vanishing lines Vanishing line Vanishing point Vertical vanishing point (at infinity) Vanishing point Pinhole camera model and camera projection matrix Homogeneous coordinates x K R tx
19 Image Formation Digital Camera Film The Eye
20 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection? λ light source
21 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source
22 A photon s life choices Absorption Diffuse Reflection Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source
23 A photon s life choices Absorption Diffusion Specular Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source
24 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source
25 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source
26 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ 2 λ 1 light source
27 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source
28 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection t=n t=1 light source
29 A photon s life choices Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection (Specular Interreflection) λ light source
30 Lambertian Reflectance In computer vision, surfaces are often assumed to be ideal diffuse reflectors with no dependence on viewing direction.
31 Digital camera A digital camera replaces film with a sensor array Each cell in the array is light-sensitive diode that converts photons to electrons Two common types Charge Coupled Device (CCD) CMOS Slide by Steve Seitz
32 Sensor Array CMOS sensor
33 Sampling and Quantization
34 Interlace vs. progressive scan Slide by Steve Seitz
35 Progressive scan Slide by Steve Seitz
36 Interlace Slide by Steve Seitz
37 Rolling Shutter
38 The Eye The human eye is a camera! Iris - colored annulus with radial muscles Pupil - the hole (aperture) whose size is controlled by the iris What s the film? photoreceptor cells (rods and cones) in the retina Slide by Steve Seitz
39 Aside: why do we care about human vision in this class? We don t, necessarily.
40 Ornithopters
41 Why do we care about human vision? We don t, necessarily. But cameras necessarily imitate the frequency response of the human eye, so we should know that much. Also, computer vision probably wouldn t get as much scrutiny if biological vision (especially human vision) hadn t proved that it was possible to make important judgements from 2d images.
42 Does computer vision understand images? "Can machines fly?" The answer is yes, because airplanes fly. "Can machines swim?" The answer is no, because submarines don't swim. "Can machines think?" Is this question like the first, or like the second? Source: Norvig
43 The Retina Cross-section of eye Cross section of retina Ganglion axons Ganglion cell layer Bipolar cell layer Pigmented epithelium Receptor layer
44 What humans don t have: tapetum lucidum Human eyes can reflect a tiny bit and blood in the retina makes this reflection red.
45 Two types of light-sensitive receptors Cones cone-shaped less sensitive operate in high light color vision Rods rod-shaped highly sensitive operate at night gray-scale vision Stephen E. Palmer, 22
46 Rod / Cone sensitivity
47 . Distribution of Rods and Cones # Receptors/mm2 15, 1, 5, 8 Rods 6 Cones 4 Fovea 2 Blind Spot Rods Cones Visual Angle (degrees from fovea) Night Sky: why are there more stars off-center? Averted vision: Stephen E. Palmer, 22
48 Wait, the blood vessels are in front of the photoreceptors??
49
50 Eye Movements Saccades Can be consciously controlled. Related to perceptual attention. 2ms to initiation, 2 to 2ms to carry out. Large amplitude. Microsaccades Involuntary. Smaller amplitude. Especially evident during prolonged fixation. Function debated. Ocular microtremor (OMT) involuntary. high frequency (up to 8Hz), small amplitude. Smooth pursuit tracking an object
51 Electromagnetic Spectrum Human Luminance Sensitivity Function
52 Visible Light Why do we see light of these wavelengths? because that s where the Sun radiates EM energy Stephen E. Palmer, 22
53 The Physics of Light Any patch of light can be completely described physically by its spectrum: the number of photons (per time unit) at each wavelength 4-7 nm. # Photons (per ms.) Wavelength (nm.) Stephen E. Palmer, 22
54 . The Physics of Light Some examples of the spectra of light sources A. Ruby Laser B. Gallium Phosphide Crystal Wavelength (nm.) D. Normal Daylight # Photons # Photons Wavelength (nm.) C. Tungsten Lightbulb # Photons # Photons Stephen E. Palmer, 22
55 % Photons Reflected The Physics of Light Some examples of the reflectance spectra of surfaces Red Yellow Blue Purple Wavelength (nm) Stephen E. Palmer, 22
56 The Psychophysical Correspondence There is no simple functional description for the perceived color of all lights under all viewing conditions, but... A helpful constraint: Consider only physical spectra with normal distributions mean # Photons area variance Wavelength (nm.) Stephen E. Palmer, 22
57 # Photons The Psychophysical Correspondence Mean Hue blue green yellow Wavelength Stephen E. Palmer, 22
58 # Photons The Psychophysical Correspondence Variance Saturation hi. high med. low medium low Wavelength Stephen E. Palmer, 22
59 # Photons The Psychophysical Correspondence Area Brightness B. Area Lightness bright dark Wavelength Stephen E. Palmer, 22
60 . Physiology of Color Vision Three kinds of cones: nm. RELATIVE ABSORBANCE (%) 1 S M L WAVELENGTH (nm.) Why are M and L cones so close? Why are there 3? Stephen E. Palmer, 22
61 Tetrachromatism Bird cone responses Most birds, and many other animals, have cones for ultraviolet light. Some humans, mostly female, seem to have slight tetrachromatism.
62 More Spectra metamers
63 Practical Color Sensing: Bayer Grid Estimate RGB at G cells from neighboring values Slide by Steve Seitz
64 Color Image R G B
65 Images in Matlab Images represented as a matrix Suppose we have a NxM RGB image called im im(1,1,1) = top-left pixel value in R-channel im(y, x, b) = y pixels down, x pixels to right in the b th channel im(n, M, 3) = bottom-right pixel in B-channel imread(filename) returns a uint8 image (values to 255) Convert to double format (values to 1) with im2double row column G R B
66 Color spaces How can we represent color?
67 Color spaces: RGB Default color space,1, R (G=,B=) 1,, G (R=,B=),,1 Some drawbacks Strongly correlated channels Non-perceptual B (R=,G=) Image from:
68 Color spaces: HSV Intuitive color space H (S=1,V=1) S (H=1,V=1) V (H=1,S=)
69 Color spaces: YCbCr Fast to compute, good for compression, used by TV Y= Y=.5 Y (Cb=.5,Cr=.5) Cr Cb Y=1 Cb (Y=.5,Cr=.5) Cr (Y=.5,Cb=5)
70 Color spaces: L*a*b* Perceptually uniform * color space L (a=,b=) a (L=65,b=) b (L=65,a=)
71 If you had to choose, would you rather go without luminance or chrominance?
72 If you had to choose, would you rather go without luminance or chrominance?
73 Most information in intensity Only color shown constant intensity
74 Most information in intensity Only intensity shown constant color
75 Most information in intensity Original image
76 Back to grayscale intensity
77 Next week Convolution, Filtering, Image Pyramids, Frequencies, Project 1
Lenses: Focus and Defocus
Lenses: 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
More informationComputer Vision Course Lecture 02. Image Formation Light and Color. Ceyhun Burak Akgül, PhD cba-research.com. Spring 2015 Last updated 04/03/2015
Computer Vision Course Lecture 02 Image Formation Light and Color Ceyhun Burak Akgül, PhD cba-research.com Spring 2015 Last updated 04/03/2015 Photo credit: Olivier Teboul vision.mas.ecp.fr/personnel/teboul
More informationPhysics-based Methods in Vision
LIGHT AND COLOR The slides are from several sources through James Hays (Brown); Srinivasa Narasimhan (CMU); Bill Freeman and Antonio Torralba (MIT), including their own slides. Physics-based Methods in
More informationLow-level Vision Processing Algorithms Speaker: Ito, Dang Supporter: Ishii, Toyama and Y. Murakami
Low-level Vision Processing Algorithms Speaker: Ito, Dang Supporter: Ishii, Toyama and Y. Murakami Adaptive Systems Lab The University of Aizu Overview Introduction What is Vision Processing? Basic Knowledge
More information(0, 1, 1) (0, 1, 1) (0, 1, 0) What is light? What is color? Terminology
lecture 23 (0, 1, 1) (0, 0, 0) (0, 0, 1) (0, 1, 1) (1, 1, 1) (1, 1, 0) (0, 1, 0) hue - which ''? saturation - how pure? luminance (value) - intensity What is light? What is? Light consists of electromagnetic
More informationCS4670: Computer Vision
CS4670: Computer Vision Noah Snavely Lecture 30: Light, color, and reflectance Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Properties of light
More informationLinear Algebra Review
CS 1674: Intro to Computer Vision Linear Algebra Review Prof. Adriana Kovashka University of Pittsburgh January 11, 2018 What are images? (in Matlab) Matlab treats images as matrices of numbers To proceed,
More informationImage Formation. Camera trial #1. Pinhole camera. What is an Image? Light and the EM spectrum The H.V.S. and Color Perception
Image Formation Light and the EM spectrum The H.V.S. and Color Perception What is an Image? An image is a projection of a 3D scene into a 2D projection plane. An image can be defined as a 2 variable function
More informationProjective Geometry and Camera Models
Projective Geometry and Camera Models Computer Vision CS 43 Brown James Hays Slides from Derek Hoiem, Alexei Efros, Steve Seitz, and David Forsyth Administrative Stuff My Office hours, CIT 375 Monday and
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 20: Light, reflectance and photometric stereo Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Light by Ted Adelson Readings Szeliski, 2.2, 2.3.2 Properties
More informationCOSC579: Scene Geometry. Jeremy Bolton, PhD Assistant Teaching Professor
COSC579: Scene Geometry Jeremy Bolton, PhD Assistant Teaching Professor Overview Linear Algebra Review Homogeneous vs non-homogeneous representations Projections and Transformations Scene Geometry The
More informationColor. Phillip Otto Runge ( )
Color Phillip Otto Runge (1777-1810) Overview The nature of color Color processing in the human visual system Color spaces Adaptation and constancy White balance Uses of color in computer vision What is
More information1. Final Projects 2. Radiometry 3. Color. Outline
1. Final Projects 2. Radiometry 3. Color Outline Poster presentations http://16720.courses.cs.cmu.edu/project.html ==== Availability form ==== One person from each team (at least) is required to fill out
More informationLecture 1 Image Formation.
Lecture 1 Image Formation peimt@bit.edu.cn 1 Part 3 Color 2 Color v The light coming out of sources or reflected from surfaces has more or less energy at different wavelengths v The visual system responds
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 informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 21: Light, reflectance and photometric stereo Announcements Final projects Midterm reports due November 24 (next Tuesday) by 11:59pm (upload to CMS) State the
More informationProjective Geometry and Camera Models
/2/ Projective Geometry and Camera Models Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Note about HW Out before next Tues Prob: covered today, Tues Prob2: covered next Thurs Prob3:
More informationPinhole Camera Model 10/05/17. Computational Photography Derek Hoiem, University of Illinois
Pinhole Camera Model /5/7 Computational Photography Derek Hoiem, University of Illinois Next classes: Single-view Geometry How tall is this woman? How high is the camera? What is the camera rotation? What
More informationFinal project bits and pieces
Final project bits and pieces The project is expected to take four weeks of time for up to four people. At 12 hours per week per person that comes out to: ~192 hours of work for a four person team. Capstone:
More informationLecture 16 Color. October 20, 2016
Lecture 16 Color October 20, 2016 Where are we? You can intersect rays surfaces You can use RGB triples You can calculate illumination: Ambient, Lambertian and Specular But what about color, is there more
More informationIllumination and Shading
Illumination and Shading Light sources emit intensity: assigns intensity to each wavelength of light Humans perceive as a colour - navy blue, light green, etc. Exeriments show that there are distinct I
More informationOptics of Vision. MATERIAL TO READ Web: 1.
Optics of Vision MATERIAL TO READ Web: 1. www.physics.uoguelph.ca/phys1070/webst.html Text: Chap. 3, pp. 1-39 (NB: pg. 3-37 missing) Chap. 5 pp.1-17 Handbook: 1. study guide 3 2. lab 3 Optics of the eye
More informationLight. Properties of light. What is light? Today What is light? How do we measure it? How does light propagate? How does light interact with matter?
Light Properties of light Today What is light? How do we measure it? How does light propagate? How does light interact with matter? by Ted Adelson Readings Andrew Glassner, Principles of Digital Image
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 informationComputer Vision. The image formation process
Computer Vision The image formation process Filippo Bergamasco (filippo.bergamasco@unive.it) http://www.dais.unive.it/~bergamasco DAIS, Ca Foscari University of Venice Academic year 2016/2017 The image
More informationColor and Shading. Color. Shapiro and Stockman, Chapter 6. Color and Machine Vision. Color and Perception
Color and Shading Color Shapiro and Stockman, Chapter 6 Color is an important factor for for human perception for object and material identification, even time of day. Color perception depends upon both
More informationCapturing Light: Geometry of Image Formation
Capturing Light: Geometry of Image Formation Computer Vision James Hays Slides from Derek Hoiem, Alexei Efros, Steve Seitz, and David Forsyth Administrative Stuff My Office hours, CoC building 35 Monday
More informationThe Display pipeline. The fast forward version. The Display Pipeline The order may vary somewhat. The Graphics Pipeline. To draw images.
View volume The fast forward version The Display pipeline Computer Graphics 1, Fall 2004 Lecture 3 Chapter 1.4, 1.8, 2.5, 8.2, 8.13 Lightsource Hidden surface 3D Projection View plane 2D Rasterization
More informationEXAM SOLUTIONS. Computer Vision Course 2D1420 Thursday, 11 th of march 2003,
Numerical Analysis and Computer Science, KTH Danica Kragic EXAM SOLUTIONS Computer Vision Course 2D1420 Thursday, 11 th of march 2003, 8.00 13.00 Exercise 1 (5*2=10 credits) Answer at most 5 of the following
More informationReading. 2. Color. Emission spectra. The radiant energy spectrum. Watt, Chapter 15.
Reading Watt, Chapter 15. Brian Wandell. Foundations of Vision. Chapter 4. Sinauer Associates, Sunderland, MA, pp. 69-97, 1995. 2. Color 1 2 The radiant energy spectrum We can think of light as waves,
More informationLecture 8: Camera Models
Lecture 8: Camera Models Dr. Juan Carlos Niebles Stanford AI Lab Professor Fei- Fei Li Stanford Vision Lab 1 14- Oct- 15 What we will learn today? Pinhole cameras Cameras & lenses The geometry of pinhole
More informationCSE 167: Lecture #6: Color. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011
CSE 167: Introduction to Computer Graphics Lecture #6: Color Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2011 Announcements Homework project #3 due this Friday, October 14
More informationRay Optics. Lecture 23. Chapter 23. Physics II. Course website:
Lecture 23 Chapter 23 Physics II Ray Optics Course website: http://faculty.uml.edu/andriy_danylov/teaching/physicsii Let s finish talking about a diffraction grating Diffraction Grating Let s improve (more
More informationImage Analysis and Formation (Formation et Analyse d'images)
Image Analysis and Formation (Formation et Analyse d'images) James L. Crowley ENSIMAG 3 - MMIS Option MIRV First Semester 2010/2011 Lesson 4 19 Oct 2010 Lesson Outline: 1 The Physics of Light...2 1.1 Photons
More informationRay Optics. Lecture 23. Chapter 34. Physics II. Course website:
Lecture 23 Chapter 34 Physics II Ray Optics Course website: http://faculty.uml.edu/andriy_danylov/teaching/physicsii Today we are going to discuss: Chapter 34: Section 34.1-3 Ray Optics Ray Optics Wave
More informationComputer Vision I - Algorithms and Applications: Image Formation Process Part 2
Computer Vision I - Algorithms and Applications: Image Formation Process Part 2 Carsten Rother 17/11/2013 Computer Vision I: Image Formation Process Computer Vision I: Image Formation Process 17/11/2013
More informationComputer Vision I - Image Matching and Image Formation
Computer Vision I - Image Matching and Image Formation Carsten Rother 10/12/2014 Computer Vision I: Image Formation Process Computer Vision I: Image Formation Process 10/12/2014 2 Roadmap for next five
More informationSNC 2PI Optics Unit Review /95 Name:
SNC 2PI Optics Unit Review /95 Name: Part 1: True or False Indicate in the space provided if the statement is true (T) or false(f) [15] 1. Light is a form of energy 2. Shadows are proof that light travels
More informationIntroduction to color science
Introduction to color science Trichromacy Spectral matching functions CIE XYZ color system xy-chromaticity diagram Color gamut Color temperature Color balancing algorithms Digital Image Processing: Bernd
More informationThe Elements of Colour
Color science 1 The Elements of Colour Perceived light of different wavelengths is in approximately equal weights achromatic.
More informationAnnouncements. Light. Properties of light. Light. Project status reports on Wednesday. Readings. Today. Readings Szeliski, 2.2, 2.3.
Announcements Project status reports on Wednesday prepare 5 minute ppt presentation should contain: problem statement (1 slide) description of approach (1 slide) some images (1 slide) current status +
More informationCS201 Computer Vision Lect 4 - Image Formation
CS201 Computer Vision Lect 4 - Image Formation John Magee 9 September, 2014 Slides courtesy of Diane H. Theriault Question of the Day: Why is Computer Vision hard? Something to think about from our view
More information(A) Electromagnetic. B) Mechanical. (C) Longitudinal. (D) None of these.
Downloaded from LIGHT 1.Light is a form of radiation. (A) Electromagnetic. B) Mechanical. (C) Longitudinal. 2.The wavelength of visible light is in the range: (A) 4 10-7 m to 8 10-7 m. (B) 4 10 7
More informationPick up Light Packet & Light WS
Pick up Light Packet & Light WS Only sit or stand at a station with a cup. Test or Quiz Make Ups Today/Tomorrow after School Only. Sound Test Corrections/Retakes: Wednesday, Next Tuesday, Wednesday, Thursday
More informationCSE 167: Lecture #6: Color. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2012
CSE 167: Introduction to Computer Graphics Lecture #6: Color Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2012 Announcements Homework project #3 due this Friday, October 19
More informationLecture #2: Color and Linear Algebra pt.1
Lecture #2: Color and Linear Algebra pt.1 John McNelly, Alexander Haigh, Madeline Saviano, Scott Kazmierowicz, Cameron Van de Graaf Department of Computer Science Stanford University Stanford, CA 94305
More informationAnnouncements. Camera Calibration. Thin Lens: Image of Point. Limits for pinhole cameras. f O Z
Announcements Introduction to Computer Vision CSE 152 Lecture 5 Assignment 1 has been posted. See links on web page for reading Irfanview: http://www.irfanview.com/ is a good windows utility for manipulating
More informationLast Lecture. Bayer pattern. Focal Length F-stop Depth of Field Color Capture. Prism. Your eye. Mirror. (flipped for exposure) Film/sensor.
Last Lecture Prism Mirror (flipped for exposure) Your eye Film/sensor Focal Length F-stop Depth of Field Color Capture Light from scene lens Mirror (when viewing) Bayer pattern YungYu Chuang s slide Today
More informationLecture 11. Color. UW CSE vision faculty
Lecture 11 Color UW CSE vision faculty Starting Point: What is light? Electromagnetic radiation (EMR) moving along rays in space R(λ) is EMR, measured in units of power (watts) λ is wavelength Perceiving
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 informationLecture 5 Chapter 1 & 2. Sources of light
We are here Lecture 5 Chapter 1 & 2 Some history of technology How vision works What is light Wavelength and Frequency: c = f λ Scientific notation and metric units Electromagnetic spectrum Transmission
More informationImage Formation. Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico
Image Formation Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico 1 Objectives Fundamental imaging notions Physical basis for image formation
More informationCS635 Spring Department of Computer Science Purdue University
Color and Perception CS635 Spring 2010 Daniel G Aliaga Daniel G. Aliaga Department of Computer Science Purdue University Elements of Color Perception 2 Elements of Color Physics: Illumination Electromagnetic
More informationShading. Brian Curless CSE 557 Autumn 2017
Shading Brian Curless CSE 557 Autumn 2017 1 Reading Optional: Angel and Shreiner: chapter 5. Marschner and Shirley: chapter 10, chapter 17. Further reading: OpenGL red book, chapter 5. 2 Basic 3D graphics
More informationChapter 5 Extraction of color and texture Comunicação Visual Interactiva. image labeled by cluster index
Chapter 5 Extraction of color and texture Comunicação Visual Interactiva image labeled by cluster index Color images Many images obtained with CCD are in color. This issue raises the following issue ->
More informationCameras and Stereo CSE 455. Linda Shapiro
Cameras and Stereo CSE 455 Linda Shapiro 1 Müller-Lyer Illusion http://www.michaelbach.de/ot/sze_muelue/index.html What do you know about perspective projection? Vertical lines? Other lines? 2 Image formation
More information2/26/2016. Chapter 23 Ray Optics. Chapter 23 Preview. Chapter 23 Preview
Chapter 23 Ray Optics Chapter Goal: To understand and apply the ray model of light. Slide 23-2 Chapter 23 Preview Slide 23-3 Chapter 23 Preview Slide 23-4 1 Chapter 23 Preview Slide 23-5 Chapter 23 Preview
More informationComputer Graphics. Bing-Yu Chen National Taiwan University The University of Tokyo
Computer Graphics Bing-Yu Chen National Taiwan University The University of Tokyo Introduction The Graphics Process Color Models Triangle Meshes The Rendering Pipeline 1 What is Computer Graphics? modeling
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 WRI C225 Lecture 02 130124 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Basics Image Formation Image Processing 3 Intelligent
More informationVisualisatie BMT. Rendering. Arjan Kok
Visualisatie BMT Rendering Arjan Kok a.j.f.kok@tue.nl 1 Lecture overview Color Rendering Illumination 2 Visualization pipeline Raw Data Data Enrichment/Enhancement Derived Data Visualization Mapping Abstract
More informationImage Formation. CS418 Computer Graphics Eric Shaffer.
Image Formation CS418 Computer Graphics Eric Shaffer http://graphics.cs.illinois.edu/cs418/fa14 Some stuff about the class Grades probably on usual scale: 97 to 93: A 93 to 90: A- 90 to 87: B+ 87 to 83:
More informationLight and Electromagnetic Waves. Honors Physics
Light and Electromagnetic Waves Honors Physics Electromagnetic Waves EM waves are a result of accelerated charges and disturbances in electric and magnetic fields (Radio wave example here) As electrons
More informationIntroduction to Computer Graphics with WebGL
Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science Laboratory University of New Mexico Image Formation
More informationDD2423 Image Analysis and Computer Vision IMAGE FORMATION. Computational Vision and Active Perception School of Computer Science and Communication
DD2423 Image Analysis and Computer Vision IMAGE FORMATION Mårten Björkman Computational Vision and Active Perception School of Computer Science and Communication November 8, 2013 1 Image formation Goal:
More informationThe topics are listed below not exactly in the same order as they were presented in class but all relevant topics are on the list!
Ph332, Fall 2016 Study guide for the final exam, Part Two: (material lectured before the Nov. 3 midterm test, but not used in that test, and the material lectured after the Nov. 3 midterm test.) The final
More informationMiniature faking. In close-up photo, the depth of field is limited.
Miniature faking In close-up photo, the depth of field is limited. http://en.wikipedia.org/wiki/file:jodhpur_tilt_shift.jpg Miniature faking Miniature faking http://en.wikipedia.org/wiki/file:oregon_state_beavers_tilt-shift_miniature_greg_keene.jpg
More informationUlrik Söderström 17 Jan Image Processing. Introduction
Ulrik Söderström ulrik.soderstrom@tfe.umu.se 17 Jan 2017 Image Processing Introduction Image Processsing Typical goals: Improve images for human interpretation Image processing Processing of images for
More informationBlue Skies Blue Eyes Blue Butterflies
Blue Skies Blue Eyes Blue Butterflies Friday, April 19 Homework #9 due in class Lecture: Blue Skies, Blue Eyes & Blue Butterflies: Interaction of electromagnetic waves with matter. Week of April 22 Lab:
More informationDoes everyone have an override code?
Does everyone have an override code? Project 1 due Friday 9pm Review of Filtering Filtering in frequency domain Can be faster than filtering in spatial domain (for large filters) Can help understand effect
More informationComputer Graphics - Chapter 1 Graphics Systems and Models
Computer Graphics - Chapter 1 Graphics Systems and Models Objectives are to learn about: Applications of Computer Graphics Graphics Systems Images: Physical and Synthetic The Human Visual System The Pinhole
More informationFINDING THE INDEX OF REFRACTION - WebAssign
Name: Book: Period: Due Date: Lab Partners: FINDING THE INDEX OF REFRACTION - WebAssign Purpose: The theme in this lab is the interaction between light and matter. Matter and light seem very different
More informationDigital Image Processing COSC 6380/4393
Digital Image Processing COSC 6380/4393 Lecture 4 Jan. 24 th, 2019 Slides from Dr. Shishir K Shah and Frank (Qingzhong) Liu Digital Image Processing COSC 6380/4393 TA - Office: PGH 231 (Update) Shikha
More informationMech 296: Vision for Robotic Applications
Mech 296: Vision for Robotic Applications Lecture 2: Color Imaging 2. Terminology from Last Week Data Files ASCII (Text): Data file is human readable Ex: 7 9 5 Note: characters may be removed when transferring
More informationGame Programming. Bing-Yu Chen National Taiwan University
Game Programming Bing-Yu Chen National Taiwan University What is Computer Graphics? Definition the pictorial synthesis of real or imaginary objects from their computer-based models descriptions OUTPUT
More informationShading. Reading. Pinhole camera. Basic 3D graphics. Brian Curless CSE 557 Fall Required: Shirley, Chapter 10
Reading Required: Shirley, Chapter 10 Shading Brian Curless CSE 557 Fall 2014 1 2 Basic 3D graphics With affine matrices, we can now transform virtual 3D objects in their local coordinate systems into
More informationIllumination and Reflectance
COMP 546 Lecture 12 Illumination and Reflectance Tues. Feb. 20, 2018 1 Illumination and Reflectance Shading Brightness versus Lightness Color constancy Shading on a sunny day N(x) L N L Lambert s (cosine)
More informationLight Transport Baoquan Chen 2017
Light Transport 1 Physics of Light and Color It s all electromagnetic (EM) radiation Different colors correspond to radiation of different wavelengths Intensity of each wavelength specified by amplitude
More informationS U N G - E U I YO O N, K A I S T R E N D E R I N G F R E E LY A VA I L A B L E O N T H E I N T E R N E T
S U N G - E U I YO O N, K A I S T R E N D E R I N G F R E E LY A VA I L A B L E O N T H E I N T E R N E T Copyright 2018 Sung-eui Yoon, KAIST freely available on the internet http://sglab.kaist.ac.kr/~sungeui/render
More informationComputational Perception. Visual Coding 3
Computational Perception 15-485/785 February 21, 2008 Visual Coding 3 A gap in the theory? - - + - - from Hubel, 1995 2 Eye anatomy from Hubel, 1995 Photoreceptors: rods (night vision) and cones (day vision)
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 informationHomogeneous Coordinates. Lecture18: Camera Models. Representation of Line and Point in 2D. Cross Product. Overall scaling is NOT important.
Homogeneous Coordinates Overall scaling is NOT important. CSED44:Introduction to Computer Vision (207F) Lecture8: Camera Models Bohyung Han CSE, POSTECH bhhan@postech.ac.kr (",, ) ()", ), )) ) 0 It is
More informationModeling Light. On Simulating the Visual Experience
Modeling Light 15-463: Rendering and Image Processing Alexei Efros On Simulating the Visual Experience Just feed the eyes the right data No one will know the difference! Philosophy: Ancient question: Does
More informationLecture 12 Color model and color image processing
Lecture 12 Color model and color image processing Color fundamentals Color models Pseudo color image Full color image processing Color fundamental The color that humans perceived in an object are determined
More informationCSE152a Computer Vision Assignment 1 WI14 Instructor: Prof. David Kriegman. Revision 0
CSE152a Computer Vision Assignment 1 WI14 Instructor: Prof. David Kriegman. Revision Instructions: This assignment should be solved, and written up in groups of 2. Work alone only if you can not find a
More informationColour computer vision: fundamentals, applications and challenges. Dr. Ignacio Molina-Conde Depto. Tecnología Electrónica Univ.
Colour computer vision: fundamentals, applications and challenges Dr. Ignacio Molina-Conde Depto. Tecnología Electrónica Univ. of Málaga (Spain) Outline Part 1: colorimetry and colour perception: What
More informationVision Review: Image Formation. Course web page:
Vision Review: Image Formation Course web page: www.cis.udel.edu/~cer/arv September 10, 2002 Announcements Lecture on Thursday will be about Matlab; next Tuesday will be Image Processing The dates some
More informationUnit 3: Optics Chapter 4
Unit 3: Optics Chapter 4 History of Light https://www.youtube.com/watch?v=j1yiapztlos History of Light Early philosophers (Pythagoras) believed light was made up of tiny particles Later scientist found
More informationINFOGR Computer Graphics. J. Bikker - April-July Lecture 10: Shading Models. Welcome!
INFOGR Computer Graphics J. Bikker - April-July 2016 - Lecture 10: Shading Models Welcome! Today s Agenda: Introduction Light Transport Materials Sensors Shading INFOGR Lecture 10 Shading Models 3 Introduction
More informationECE-161C Color. Nuno Vasconcelos ECE Department, UCSD (with thanks to David Forsyth)
ECE-6C Color Nuno Vasconcelos ECE Department, UCSD (with thanks to David Forsyth) Color so far we have talked about geometry where is a 3D point map mapped into, in terms of image coordinates? perspective
More information11/28/17. Midterm Review. Magritte, Homesickness. Computational Photography Derek Hoiem, University of Illinois
Midterm Review 11/28/17 Computational Photography Derek Hoiem, University of Illinois Magritte, Homesickness Major Topics Linear Filtering How it works Template and Frequency interpretations Image pyramids
More informationMichelson Interferometer
Michelson Interferometer The Michelson interferometer uses the interference of two reflected waves The third, beamsplitting, mirror is partially reflecting ( half silvered, except it s a thin Aluminum
More informationINTRODUCTION. Slides modified from Angel book 6e
INTRODUCTION Slides modified from Angel book 6e Fall 2012 COSC4328/5327 Computer Graphics 2 Objectives Historical introduction to computer graphics Fundamental imaging notions Physical basis for image
More informationw Foley, Section16.1 Reading
Shading w Foley, Section16.1 Reading Introduction So far, we ve talked exclusively about geometry. w What is the shape of an object? w How do I place it in a virtual 3D space? w How do I know which pixels
More informationShading. Brian Curless CSE 457 Spring 2017
Shading Brian Curless CSE 457 Spring 2017 1 Reading Optional: Angel and Shreiner: chapter 5. Marschner and Shirley: chapter 10, chapter 17. Further reading: OpenGL red book, chapter 5. 2 Basic 3D graphics
More informationFeature Tracking and Optical Flow
Feature Tracking and Optical Flow Prof. D. Stricker Doz. G. Bleser Many slides adapted from James Hays, Derek Hoeim, Lana Lazebnik, Silvio Saverse, who 1 in turn adapted slides from Steve Seitz, Rick Szeliski,
More informationDigital Image Processing COSC 6380/4393. Lecture 19 Mar 26 th, 2019 Pranav Mantini
Digital Image Processing COSC 6380/4393 Lecture 19 Mar 26 th, 2019 Pranav Mantini What is color? Color is a psychological property of our visual experiences when we look at objects and lights, not a physical
More informationChapter 24. Geometric optics. Assignment No. 11, due April 27th before class: Problems 24.4, 24.11, 24.13, 24.15, 24.24
Chapter 24 Geometric optics Assignment No. 11, due April 27th before class: Problems 24.4, 24.11, 24.13, 24.15, 24.24 A Brief History of Light 1000 AD It was proposed that light consisted of tiny particles
More informationIntroduction. Lighting model Light reflection model Local illumination model Reflectance model BRDF
Shading Introduction Affine transformations help us to place objects into a scene. Before creating images of these objects, we ll look at models for how light interacts with their surfaces. Such a model
More informationAnnouncements. Image Formation: Outline. Homogenous coordinates. Image Formation and Cameras (cont.)
Announcements Image Formation and Cameras (cont.) HW1 due, InitialProject topics due today CSE 190 Lecture 6 Image Formation: Outline Factors in producing images Projection Perspective Vanishing points
More informationImage Formation I Chapter 1 (Forsyth&Ponce) Cameras
Image Formation I Chapter 1 (Forsyth&Ponce) Cameras Guido Gerig CS 632 Spring 215 cknowledgements: Slides used from Prof. Trevor Darrell, (http://www.eecs.berkeley.edu/~trevor/cs28.html) Some slides modified
More information