Introduction to Computer Vision. Introduction CMPSCI 591A/691A CMPSCI 570/670. Image Formation

Similar documents
Understanding Variability

dq dt I = Irradiance or Light Intensity is Flux Φ per area A (W/m 2 ) Φ =

Reflectance & Lighting

Cameras and Radiometry. Last lecture in a nutshell. Conversion Euclidean -> Homogenous -> Euclidean. Affine Camera Model. Simplified Camera Models

Capturing light. Source: A. Efros

dq dt I = Irradiance or Light Intensity is Flux Φ per area A (W/m 2 ) Φ =

INFOGR Computer Graphics. J. Bikker - April-July Lecture 10: Shading Models. Welcome!

Measuring Light: Radiometry and Cameras

How to achieve this goal? (1) Cameras

DD2423 Image Analysis and Computer Vision IMAGE FORMATION. Computational Vision and Active Perception School of Computer Science and Communication

Introduction to Computer Vision. Week 8, Fall 2010 Instructor: Prof. Ko Nishino

Image formation. Thanks to Peter Corke and Chuck Dyer for the use of some slides

Ray Optics. Physics 11. Sources of Light Rays: Self-Luminous Objects. The Ray Model of Light

Image Formation. Antonino Furnari. Image Processing Lab Dipartimento di Matematica e Informatica Università degli Studi di Catania

Ray Optics I. Last time, finished EM theory Looked at complex boundary problems TIR: Snell s law complex Metal mirrors: index complex

CS201 Computer Vision Lect 4 - Image Formation

Computer Vision. The image formation process

Lecture 22: Basic Image Formation CAP 5415

COSC579: Scene Geometry. Jeremy Bolton, PhD Assistant Teaching Professor

782 Schedule & Notes

PHY 171 Lecture 6 (January 18, 2012)

Lecture Outline Chapter 26. Physics, 4 th Edition James S. Walker. Copyright 2010 Pearson Education, Inc.

AP Physics: Curved Mirrors and Lenses

Radiometry (From Intro to Optics, Pedrotti 1-4) Radiometry is measurement of Emag radiation (light) Consider a small spherical source Assume a black

Optics Part 1. Vern Lindberg. March 5, 2013

Engineered Diffusers Intensity vs Irradiance

Announcements. Lighting. Camera s sensor. HW1 has been posted See links on web page for readings on color. Intro Computer Vision.

Optics II. Reflection and Mirrors

Chapter 26 Geometrical Optics

Optics and Images. Lenses and Mirrors. Matthew W. Milligan

Light: Geometric Optics

Module 5: Video Modeling Lecture 28: Illumination model. The Lecture Contains: Diffuse and Specular Reflection. Objectives_template

Mosaics, Plenoptic Function, and Light Field Rendering. Last Lecture

Vision Review: Image Formation. Course web page:

CMPSCI 670: Computer Vision! Image formation. University of Massachusetts, Amherst September 8, 2014 Instructor: Subhransu Maji

LIGHT. Speed of light Law of Reflection Refraction Snell s Law Mirrors Lenses

The Light Field. Last lecture: Radiometry and photometry

Spectral Color and Radiometry

Chapter 36. Image Formation

Computer Vision CS 776 Fall 2018

Algebra Based Physics

Radiometry & BRDFs CS295, Spring 2017 Shuang Zhao

Light: Geometric Optics (Chapter 23)

Chapter 23. Geometrical Optics (lecture 1: mirrors) Dr. Armen Kocharian

COMP30019 Graphics and Interaction Perspective Geometry

Conceptual Physics 11 th Edition

Physics 11 Chapter 18: Ray Optics

Refraction of Light. This bending of the ray is called refraction

Global Illumination The Game of Light Transport. Jian Huang

CENG 477 Introduction to Computer Graphics. Ray Tracing: Shading

Conceptual Physics Fundamentals

Chapter 32 Light: Reflection and Refraction. Copyright 2009 Pearson Education, Inc.

4. A bulb has a luminous flux of 2400 lm. What is the luminous intensity of the bulb?

Global Illumination. CMPT 361 Introduction to Computer Graphics Torsten Möller. Machiraju/Zhang/Möller

Ray Optics. Ray model Reflection Refraction, total internal reflection Color dispersion Lenses Image formation Magnification Spherical mirrors

CS6670: Computer Vision

Computer Vision Project-1

Image Formation: Light and Shading. Introduction to Computer Vision CSE 152 Lecture 3

CMSC427 Shading Intro. Credit: slides from Dr. Zwicker

Radiance. Radiance properties. Radiance properties. Computer Graphics (Fall 2008)

Photometric Stereo. Lighting and Photometric Stereo. Computer Vision I. Last lecture in a nutshell BRDF. CSE252A Lecture 7

The branch of physics which studies light

CS6670: Computer Vision

specular diffuse reflection.

Light: Geometric Optics

index of refraction-light speed

Part Images Formed by Flat Mirrors. This Chapter. Phys. 281B Geometric Optics. Chapter 2 : Image Formation. Chapter 2: Image Formation

Photometric Stereo.

Laser sensors. Transmitter. Receiver. Basilio Bona ROBOTICA 03CFIOR

Overview. Radiometry and Photometry. Foundations of Computer Graphics (Spring 2012)

Assignment #2. (Due date: 11/6/2012)

Realistic Camera Model

Measuring Light: Radiometry and Photometry

Light Field = Radiance(Ray)

CS667 Lecture Notes: Radiometry

Robotics - Projective Geometry and Camera model. Marcello Restelli

OPPA European Social Fund Prague & EU: We invest in your future.

Physics 11. Unit 8 Geometric Optics Part 1

Representing the World

ECE-161C Cameras. Nuno Vasconcelos ECE Department, UCSD

Announcements. Rotation. Camera Calibration

Announcements. Image Formation: Light and Shading. Photometric image formation. Geometric image formation

Topic 9: Lighting & Reflection models 9/10/2016. Spot the differences. Terminology. Two Components of Illumination. Ambient Light Source

Electromagnetic waves and power spectrum. Rays. Rays. CS348B Lecture 4 Pat Hanrahan, Spring 2002

Introduction to Computer Vision

Topic 9: Lighting & Reflection models. Lighting & reflection The Phong reflection model diffuse component ambient component specular component

The Law of Reflection

Chapter 12 Notes: Optics

Radiometry. Computer Graphics CMU /15-662, Fall 2015

CS-184: Computer Graphics. Today. Lecture 22: Radiometry! James O Brien University of California, Berkeley! V2014-S

Reflection & Mirrors

EE Light & Image Formation

Phys102 Lecture 21/22 Light: Reflection and Refraction

CS354 Computer Graphics Ray Tracing. Qixing Huang Januray 24th 2017

Radiometry. Radiometry. Measuring Angle. Solid Angle. Radiance

Announcements. Image Formation: Outline. Homogenous coordinates. Image Formation and Cameras (cont.)

General Physics II. Mirrors & Lenses

HW Chapter 20 Q 2,3,4,5,6,10,13 P 1,2,3. Chapter 20. Classic and Modern Optics. Dr. Armen Kocharian

Chapter 26 Geometrical Optics

Illumination. Courtesy of Adam Finkelstein, Princeton University

Transcription:

Introduction CMPSCI 591A/691A CMPSCI 570/670 Image Formation

Lecture Outline Light and Optics Pinhole camera model Perspective projection Thin lens model Fundamental equation Distortion: spherical & chromatic aberration, radial distortion Reflection and Illumination: color, Lambertian and specular surfaces, Phong, BDRF Sensing Light Conversion to Digital Images Sampling Theorem Other Sensors: frequency, type,.

Abstract Image An image can be represented by an image function whose general form is f(x,y). f(x,y) is a vector-valued function whose argument represents a pixel location. The value of f(x,y) can have different interpretations in different kinds of images. Examples Intensity Image Range Image Color Image Video - f(x,y) = intensity of the scene - f(x,y) = depth of the scene from imaging system - f(x,y) = {f r (x,y), f g (x,y), f b (x,y)} - f(x,y,t) = temporal image sequence

Basic Radiometry Radiometry is the part of image formation concerned with the relation among the amounts of light energy emitted from light sources, reflected from surfaces, and registered by sensors. Light Source CCD Array Optics p i n e P L(P,d) Surface

Light and Matter The interaction between light and matter can take many forms: Reflection Refraction Diffraction Absorption Scattering

Lecture Assumptions Typical imaging scenario: visible light ideal lenses standard sensor (e.g. TV camera) opaque objects Goal To create 'digital' images which can be processed to recover some of the characteristics of the 3D world which was imaged.

Steps World Optics Sensor Signal Digitizer Digital Representation World Optics Sensor Signal Digitizer Digital Rep. reality focus {light} from world on sensor converts {light} to {electrical energy} representation of incident light as continuous electrical energy converts continuous signal to discrete signal final representation of reality in computer memory

Factors in Image Formation Geometry concerned with the relationship between points in the three-dimensional world and their images Radiometry concerned with the relationship between the amount of light radiating from a surface and the amount incident at its image Photometry concerned with ways of measuring the intensity of light Digitization concerned with ways of converting continuous signals (in both space and time) to digital approximations

Image Formation Light (Energy) Source Surface Imaging Plane Pinhole Lens World Optics Sensor Signal B&W Film Color Film TV Camera Silver Density Silver density in three color layers Electrical

Geometry Geometry describes the projection of: three-dimensional (3D) world two-dimensional (2D) image plane. Typical Assumptions Light travels in a straight line Optical Axis: the perpendicular from the image plane through the pinhole (also called the central projection ray) Each point in the image corresponds to a particular direction defined by a ray from that point through the pinhole. Various kinds of projections: - perspective - oblique - orthographic - isometric - spherical

Basic Optics Two models are commonly used: Pin-hole camera Optical system composed of lenses Pin-hole is the basis for most graphics and vision Derived from physical construction of early cameras Mathematics is very straightforward Thin lens model is first of the lens models Mathematical model for a physical lens Lens gathers light over area and focuses on image plane.

Pinhole Camera Model Image Plane Optical Axis Pinhole lens f World projected to 2D Image Image inverted Size reduced Image is dim No direct depth information f called the focal length of the lens Known as perspective projection

Pinhole camera image Amsterdam Photo by Robert Kosara, robert@kosara.net http://www.kosara.net/gallery/pinholeamsterdam/pic01.html

Equivalent Geometry Consider case with object on the optical axis: z f More convenient with upright image: z - f Projection plane z = 0 Equivalent mathematically

Thin Lens Model Rays entering parallel on one side converge at focal point. Rays diverging from the focal point become parallel. f i o IMAGE PLANE LENS OPTIC AXIS 1 = 1 1 + THIN LENS LAW f i o

Coordinate System Simplified Case: Origin of world and image coordinate systems coincide Y-axis aligned with y-axis X-axis aligned with x-axis Z-axis along the central projection ray Image Coordinate System p(x,y) y (0,0,0) X Y Y World Coordinate System Z P(X,Y,Z) x (0,0) X Z

Perspective Projection Compute the image coordinates of p in terms of the world coordinates of P. p(x, y) y P(X,Y,Z) Z=-f x Z = 0 Z Look at projections in x-z and y-z planes

X-Z Projection - f Z x X By similar triangles: x f = X Z+f x = fx Z+f

Y-Z Projection y Y - f Z y By similar triangles: = f Y Z+f y = fy Z+f

Perspective Equations Given point P(X,Y,Z) in the 3D world The two equations: x = fx Z+f y = fy Z+f transform world coordinates (X,Y,Z) into image coordinates (x,y)

Reverse Projection Given a center of projection and image coordinates of a point, it is not possible to recover the 3D depth of the point from a single image. P(X,Y,Z) can be anywhere along this line p(x,y) All points on this line have image coordinates (x,y). In general, at least two images of the same point taken from two different locations are required to recover depth.

Stereo Geometry P(X,Y,Z) Object point p l Central Projection Rays p r Vergence Angle Depth obtained by triangulation Correspondence problem: p l and p r must correspond to the left and right projections of P, respectively.

Radiometry Image: two-dimensional array of 'brightness' values. Geometry: where in an image a point will project. Radiometry: what the brightness of the point will be. Brightness: informal notion used to describe both scene and image brightness. Image brightness: related to energy flux incident on the image plane: IRRADIANCE Scene brightness: brightness related to energy flux emitted (radiated) from a surface. RADIANCE

Light Electromagnetic energy Wave model Light sources typically radiate over a frequency spectrum Φ watts radiated into 4π radians Φ watts r Φ = dφ sphere dω R = Radiant Intensity = (of source) dφ dω Watts/unit solid angle (steradian)

Irradiance Light falling on a surface from all directions. How much? dφ da Irradiance: power per unit area falling on a surface. dφ Irradiance E = dα watts/m 2