LENSES. INEL 6088 Computer Vision

Size: px
Start display at page:

Download "LENSES. INEL 6088 Computer Vision"

Transcription

1 LENSES INEL 6088 Computer Vision

2 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 other variants exist: CMOS is becoming more popular

3 Cameras CCD: most common, low cost, good performance CMOS: inexpensive, low performance now Color: 1 CCD+color filters 3 CCD+prism Line-scan: row-by-row RS-170 Standard CCTV standard (used in USA, Canada; in Europe, CCIR) Interlaced, single-channel (serial) vertical and horizontal sync pulses + analog video signal

4 Issues with digital cameras Noise big difference between consumer vs. SLR-style cameras low light is where you most notice noise Compression creates artifacts except in uncompressed formats (tiff, raw) Color color fringing artifacts from Bayer patterns Blooming charge overflowing into neighboring pixels In-camera processing oversharpening can produce halos Interlaced vs. progressive scan video even/odd rows from different exposures Are more megapixels better? requires higher quality lens noise issues Stabilization compensate for camera shake (mechanical vs. electronic) More info online, e.g.,

5 Parameters for Digital Images Physical properties of the photosensitive matrix Discrete nature of photo-sensors Quantization of the intensity scale Copyright 2002, Edmund Industrial Optics. All rights reserved.

6

7 !7

8 Ansel Adams's large format photograph The Tetons and the Snake River (1942).!8

9 RS170 Signals

10 Analog Video Signals

11 LENSES The function of the lens is to collect more light Image sensor size determines camera format Lens should be chosen so that all features to be measured are covered in the image sensor, plus 10% for extra margin Features must be at least 3 pixels across If there are more than 100 features use a second camera 11

12 Magnification = W camera /W FOV FOV: Field of view = object area that is imaged by the lens onto the image sensor W FOV =width of the FOV W camera = width of the camera sensor Working distance = distance from lens to object Thin-lens approximation: lens thickness is neglected Pinhole camera: no lens; images through a small hole 12

13 Camera Obscura The first camera Known to Aristotle How does the aperture size affect the image?

14 PINHOLE CAMERAS n Pinhole cameras work in practice Abstract camera model - box with a small hole 14

15 Pinhole too big - many directions are averaged, blurring the image Pinhole too smalldiffraction effects blur the image Generally, pinhole cameras are dark, because a very small set of rays from a particular point hits the screen. 15

16 Shrinking the aperture

17 THE REASON FOR LENSES 17

18 Adding a lens 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

19 Lenses F optical center (Center Of Projection) focal point A lens focuses parallel rays onto a single focal point focal point at a distance f beyond the plane of the lens f is a function of the shape and index of refraction of the lens Aperture of diameter D restricts the range of rays aperture may be on either side of the lens Lenses are typically spherical (easier to produce)

20 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 )

21 THE THIN LENS Basic Properties: (1) Any ray entering the lens parallel to the axis goes through the focus on the other side; (2) any ray entering the lens from the focus in one side emerges parallel to the axis on the other side β β α α Focal length: distance between lens and camera plane when the object is at infinity Lens maker s formula: Magnification: m = z/z 21

22 (ignoring signs so -z z and -y y) tan = P z f = P 0 f tan = P f = P 0 z 0 f P P 0 = z f = f f z 0 f (z f)(z 0 f)=z 2 z 0 f zf + f 2 = f 2 zz 0 = z 0 f + zf 1 f = 1 z + 1 z 0

23 Image Geometry

24 Perspective Projection

25 SPHERICAL ABERRATION 25

26 LENS SYSTEMS 26

27 VIGNETTING 27

28 Chromatic aberration OTHER (POSSIBLY ANNOYING) PHENOMENA q Light at different wavelengths follows different paths; hence, some wavelengths are defocussed q Machines: coat the lens q Humans: live with it Scattering at the lens surface q Some light entering the lens system is reflected off each surface it encounters (Fresnel s law gives details) q Machines: coat the lens, interior q Humans: live with it (various scattering phenomena are visible in the human eye) Geometric phenomena (Barrel distortion, etc.) 28

29 Chromatic aberration

30 Light scattering (image flare)

31 Barrel distortion

32 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

33 Modeling distortion Project to normalized image coordinates Apply radial distortion Apply focal length translate image center To model lens distortion Use above projection operation instead of standard projection matrix multiplication

34 Correcting radial distortion from Helmut Dersch

35 RESOLUTION Listed as resolving power in units of lines per inch/millimeter RP=1/2d lines/mm d = spacing between pixels in the image plane This equation neglects lens distortion usually not an issue in M.V. 35

36 F/NUMBER Cone angle of the rays that form an image Determines Brightness of image Depth of field Resolution of the lens In MV the f/number can be taken as the ratio of the focal lens to the diameter of the aperture; large aperture -> small f/number 36

37 DEPTH OF FIELD Larger aperture more light reduced depth of field Depth of field: range of scene (object) distances with scene points that are in focus to an acceptable degree Out of focus points are imaged to circles. If the diameter of the circle b is below the resolution of the sensor then defocusing is not significant 37

38 Depth of field f / 5.6 f / 32 Changing the aperture size affects depth of field A smaller aperture increases the range in which the object is approximately in focus Flower images from Wikipedia

39 DEPTH OF FOCUS D b: maximum acceptable blur diameter d: lens diameter f: focal length z: scene distance (nominal plane of focus) Near plane distance Far plane distance 39

40 EXPOSURE E = Et E : amount of light collected by the camera E : image irradiance; intensity of light falling on the image plane t : duration of exposure (shutter speed) 40

41 POLARIZATION Some objects have certain features that are extremely bright, reflective or objects may be illuminated from an angle that produces intense reflection. Polarize filters are a solution. 41

42 POLARIZATION Normally light is linearly polarized: 42

43 POLARIZATION A linear polarizer filter absorb E along some directions and transmit orthogonal to the direction of absorption. 43

44 POLARIZERS 44

45 POLARIZERS 45

46 MORE INFO For videos on different subjects related to MV, search for "EO Imaging Lab" in youtube Edmund Optics application notes Lumera Corporation white paper: Design a Vision System

Projection. Readings. Szeliski 2.1. Wednesday, October 23, 13

Projection. 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 information

Cameras. CSE 455, Winter 2010 January 25, 2010

Cameras. 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 information

Projection. Projection. Image formation. Müller-Lyer Illusion. Readings. Readings. Let s design a camera. Szeliski 2.1. Szeliski 2.

Projection. 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 information

Unit 1: Image Formation

Unit 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 information

Projection. Announcements. Müller-Lyer Illusion. Image formation. Readings Nalwa 2.1

Projection. 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 information

Building a Real Camera. Slides Credit: Svetlana Lazebnik

Building 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 information

Building a Real Camera

Building 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 information

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. 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 information

The Camera : Computational Photography Alexei Efros, CMU, Fall 2008

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 information

Cameras. Shrinking the aperture. Camera trial #1. Pinhole camera. Digital Visual Effects Yung-Yu Chuang. Put a piece of film in front of an object.

Cameras. 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 information

CSE 473/573 Computer Vision and Image Processing (CVIP)

CSE 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 information

Announcement 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 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 information

Image Formation and Capture

Image 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 information

IMAGE FORMATION. Light source properties. Sensor characteristics Surface. Surface reflectance properties. Optics

IMAGE 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 information

Two strategies for realistic rendering capture real world data synthesize from bottom up

Two strategies for realistic rendering capture real world data synthesize from bottom up 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

More information

Cameras. Outline. Pinhole camera. Camera trial #1. Pinhole camera Film camera Digital camera Video camera

Cameras. 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 information

Cameras. 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 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 information

CS6670: Computer Vision

CS6670: 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 information

Acquisition. 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 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 information

Overview. Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image

Overview. 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 information

Image Formation. Dr. Gerhard Roth. COMP 4102A Winter 2015 Version 3

Image 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 information

Lenses. Overview. Terminology. The pinhole camera. Pinhole camera Lenses Principles of operation Limitations

Lenses. 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 information

CS 443: Imaging and Multimedia Cameras and Lenses

CS 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 information

CS6670: Computer Vision

CS6670: 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 information

The Camera : Computational Photography Alexei Efros, CMU, Fall 2005

The 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 information

CSE 527: Introduction to Computer Vision

CSE 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 information

Chapters 1 & 2. Definitions and applications Conceptual basis of photogrammetric processing

Chapters 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 information

Cameras. Outline. Pinhole camera. Camera trial #1. Pinhole camera Film camera Digital camera Video camera High dynamic range imaging

Cameras. 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 information

Cameras, lenses, and sensors

Cameras, 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 information

Lecture 7: Camera Models

Lecture 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 information

Dr F. Cuzzolin 1. September 29, 2015

Dr 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 information

Image Formation: Camera Model

Image 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 information

Lenses, exposure, and (de)focus

Lenses, 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 information

CS559: 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 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 information

Image Formation III Chapter 1 (Forsyth&Ponce) Cameras Lenses & Sensors

Image 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 information

Image Formation. Dr. Gerhard Roth. COMP 4102A Winter 2014 Version 1

Image 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 information

How do we see the world?

How 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 information

VC 14/15 TP2 Image Formation

VC 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 information

VC 11/12 T2 Image Formation

VC 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 information

Optics: An Introduction

Optics: An Introduction It is easy to overlook the contribution that optics make to a system; beyond basic lens parameters such as focal distance, the details can seem confusing. This Tech Tip presents a basic guide to optics

More information

Computer Vision. The Pinhole Camera Model

Computer 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 information

Cameras, lenses and sensors

Cameras, lenses and sensors Cameras, lenses and sensors Marc Pollefeys COMP 256 Cameras, lenses and sensors Camera Models Pinhole Perspective Projection Affine Projection Camera with Lenses Sensing The Human Eye Reading: Chapter.

More information

Image and Multidimensional Signal Processing

Image and Multidimensional Signal Processing Image and Multidimensional Signal Processing Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ Digital Image Fundamentals 2 Digital Image Fundamentals

More information

Cameras and Sensors. Today. Today. It receives light from all directions. BIL721: Computational Photography! Spring 2015, Lecture 2!

Cameras 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 information

TSBB09 Image Sensors 2018-HT2. Image Formation Part 1

TSBB09 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 information

Cameras. Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017

Cameras. 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 information

Applied Optics. , Physics Department (Room #36-401) , ,

Applied 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 information

Basic principles of photography. David Capel 346B IST

Basic principles of photography. David Capel 346B IST Basic principles of photography David Capel 346B IST Latin Camera Obscura = Dark Room Light passing through a small hole produces an inverted image on the opposite wall Safely observing the solar eclipse

More information

What will be on the midterm?

What will be on the midterm? What will be on the midterm? CS 178, Spring 2014 Marc Levoy Computer Science Department Stanford University General information 2 Monday, 7-9pm, Cubberly Auditorium (School of Edu) closed book, no notes

More information

VC 16/17 TP2 Image Formation

VC 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 information

6.098 Digital and Computational Photography Advanced Computational Photography. Bill Freeman Frédo Durand MIT - EECS

6.098 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 information

INTRODUCTION THIN LENSES. Introduction. given by the paraxial refraction equation derived last lecture: Thin lenses (19.1) = 1. Double-lens systems

INTRODUCTION THIN LENSES. Introduction. given by the paraxial refraction equation derived last lecture: Thin lenses (19.1) = 1. Double-lens systems Chapter 9 OPTICAL INSTRUMENTS Introduction Thin lenses Double-lens systems Aberrations Camera Human eye Compound microscope Summary INTRODUCTION Knowledge of geometrical optics, diffraction and interference,

More information

Image Formation and Camera Design

Image Formation and Camera Design Image Formation and Camera Design Spring 2003 CMSC 426 Jan Neumann 2/20/03 Light is all around us! From London & Upton, Photography Conventional camera design... Ken Kay, 1969 in Light & Film, TimeLife

More information

IMAGE SENSOR SOLUTIONS. KAC-96-1/5" Lens Kit. KODAK KAC-96-1/5" Lens Kit. for use with the KODAK CMOS Image Sensors. November 2004 Revision 2

IMAGE SENSOR SOLUTIONS. KAC-96-1/5 Lens Kit. KODAK KAC-96-1/5 Lens Kit. for use with the KODAK CMOS Image Sensors. November 2004 Revision 2 KODAK for use with the KODAK CMOS Image Sensors November 2004 Revision 2 1.1 Introduction Choosing the right lens is a critical aspect of designing an imaging system. Typically the trade off between image

More information

Optical basics for machine vision systems. Lars Fermum Chief instructor STEMMER IMAGING GmbH

Optical basics for machine vision systems. Lars Fermum Chief instructor STEMMER IMAGING GmbH Optical basics for machine vision systems Lars Fermum Chief instructor STEMMER IMAGING GmbH www.stemmer-imaging.de AN INTERNATIONAL CONCEPT STEMMER IMAGING customers in UK Germany France Switzerland Sweden

More information

Chapter 25 Optical Instruments

Chapter 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 information

Virtual and Digital Cameras

Virtual and Digital Cameras CS148: Introduction to Computer Graphics and Imaging Virtual and Digital Cameras Ansel Adams Topics Effect Cause Field of view Film size, focal length Perspective Lens, focal length Focus Dist. of lens

More information

Colorado School of Mines. Computer Vision. Professor William Hoff Dept of Electrical Engineering &Computer Science.

Colorado School of Mines. Computer Vision. Professor William Hoff Dept of Electrical Engineering &Computer Science. Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ 1 Sensors and Image Formation Imaging sensors and models of image formation Coordinate systems Digital

More information

This experiment is under development and thus we appreciate any and all comments as we design an interesting and achievable set of goals.

This experiment is under development and thus we appreciate any and all comments as we design an interesting and achievable set of goals. Experiment 7 Geometrical Optics You will be introduced to ray optics and image formation in this experiment. We will use the optical rail, lenses, and the camera body to quantify image formation and magnification;

More information

The Xiris Glossary of Machine Vision Terminology

The Xiris Glossary of Machine Vision Terminology X The Xiris Glossary of Machine Vision Terminology 2 Introduction Automated welding, camera technology, and digital image processing are all complex subjects. When you combine them in a system featuring

More information

Image Formation. Light from distant things. Geometrical optics. Pinhole camera. Chapter 36

Image 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 information

CPSC 425: Computer Vision

CPSC 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 information

Laboratory experiment aberrations

Laboratory 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 information

Lecture 2: Geometrical Optics. Geometrical Approximation. Lenses. Mirrors. Optical Systems. Images and Pupils. Aberrations.

Lecture 2: Geometrical Optics. Geometrical Approximation. Lenses. Mirrors. Optical Systems. Images and Pupils. Aberrations. Lecture 2: Geometrical Optics Outline 1 Geometrical Approximation 2 Lenses 3 Mirrors 4 Optical Systems 5 Images and Pupils 6 Aberrations Christoph U. Keller, Leiden Observatory, keller@strw.leidenuniv.nl

More information

To Do. Advanced Computer Graphics. Outline. Computational Imaging. How do we see the world? Pinhole camera

To 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 information

Applications of Optics

Applications of Optics Nicholas J. Giordano www.cengage.com/physics/giordano Chapter 26 Applications of Optics Marilyn Akins, PhD Broome Community College Applications of Optics Many devices are based on the principles of optics

More information

Waves & Oscillations

Waves & 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 information

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor Image acquisition Digital images are acquired by direct digital acquisition (digital still/video cameras), or scanning material acquired as analog signals (slides, photographs, etc.). In both cases, the

More information

Name: Date: Math in Special Effects: Try Other Challenges. Student Handout

Name: Date: Math in Special Effects: Try Other Challenges. Student Handout Name: Date: Math in Special Effects: Try Other Challenges When filming special effects, a high-speed photographer needs to control the duration and impact of light by adjusting a number of settings, including

More information

Point Spread Function. Confocal Laser Scanning Microscopy. Confocal Aperture. Optical aberrations. Alternative Scanning Microscopy

Point Spread Function. Confocal Laser Scanning Microscopy. Confocal Aperture. Optical aberrations. Alternative Scanning Microscopy Bi177 Lecture 5 Adding the Third Dimension Wide-field Imaging Point Spread Function Deconvolution Confocal Laser Scanning Microscopy Confocal Aperture Optical aberrations Alternative Scanning Microscopy

More information

OPTICAL SYSTEMS OBJECTIVES

OPTICAL SYSTEMS OBJECTIVES 101 L7 OPTICAL SYSTEMS OBJECTIVES Aims Your aim here should be to acquire a working knowledge of the basic components of optical systems and understand their purpose, function and limitations in terms

More information

Image formation - Cameras. Grading & Project. About the course. Tentative Schedule. Course Content. Students introduction

Image 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 information

Lecture 02 Image Formation 1

Lecture 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 information

Lecture 2: Geometrical Optics. Geometrical Approximation. Lenses. Mirrors. Optical Systems. Images and Pupils. Aberrations.

Lecture 2: Geometrical Optics. Geometrical Approximation. Lenses. Mirrors. Optical Systems. Images and Pupils. Aberrations. Lecture 2: Geometrical Optics Outline 1 Geometrical Approximation 2 Lenses 3 Mirrors 4 Optical Systems 5 Images and Pupils 6 Aberrations Christoph U. Keller, Leiden Observatory, keller@strw.leidenuniv.nl

More information

ECEN 4606, UNDERGRADUATE OPTICS LAB

ECEN 4606, UNDERGRADUATE OPTICS LAB ECEN 4606, UNDERGRADUATE OPTICS LAB Lab 2: Imaging 1 the Telescope Original Version: Prof. McLeod SUMMARY: In this lab you will become familiar with the use of one or more lenses to create images of distant

More information

Aperture and Digi scoping. Thoughts on the value of the aperture of a scope digital camera combination.

Aperture and Digi scoping. Thoughts on the value of the aperture of a scope digital camera combination. Aperture and Digi scoping. Thoughts on the value of the aperture of a scope digital camera combination. Before entering the heart of the matter, let s do a few reminders. 1. Entrance pupil. It is the image

More information

Camera Simulation. References. Photography, B. London and J. Upton Optics in Photography, R. Kingslake The Camera, The Negative, The Print, A.

Camera Simulation. References. Photography, B. London and J. Upton Optics in Photography, R. Kingslake The Camera, The Negative, The Print, A. Camera Simulation Effect Cause Field of view Film size, focal length Depth of field Aperture, focal length Exposure Film speed, aperture, shutter Motion blur Shutter References Photography, B. London and

More information

Mirrors 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. 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 information

Vision 1. Physical Properties of Light. Overview of Topics. Light, Optics, & The Eye Chaudhuri, Chapter 8

Vision 1. Physical Properties of Light. Overview of Topics. Light, Optics, & The Eye Chaudhuri, Chapter 8 Vision 1 Light, Optics, & The Eye Chaudhuri, Chapter 8 1 1 Overview of Topics Physical Properties of Light Physical properties of light Interaction of light with objects Anatomy of the eye 2 3 Light A

More information

Converging and Diverging Surfaces. Lenses. Converging Surface

Converging 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 information

How to Choose a Machine Vision Camera for Your Application.

How to Choose a Machine Vision Camera for Your Application. Vision Systems Design Webinar 9 September 2015 How to Choose a Machine Vision Camera for Your Application. Andrew Bodkin Bodkin Design & Engineering, LLC Newton, MA 02464 617-795-1968 wab@bodkindesign.com

More information

A Simple Camera Model

A Simple Camera Model A Simple Camera Model Carlo Tomasi The images we process in computer vision are formed by light bouncing off surfaces in the world and into the lens of the camera. The light then hits an array of sensors

More information

Capturing Light in man and machine. Some figures from Steve Seitz, Steve Palmer, Paul Debevec, and Gonzalez et al.

Capturing Light in man and machine. Some figures from Steve Seitz, Steve Palmer, Paul Debevec, and Gonzalez et al. Capturing Light in man and machine Some figures from Steve Seitz, Steve Palmer, Paul Debevec, and Gonzalez et al. 15-463: Computational Photography Alexei Efros, CMU, Fall 2005 Image Formation Digital

More information

Lecture 2 Camera Models

Lecture 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 information

Optical Components for Laser Applications. Günter Toesko - Laserseminar BLZ im Dezember

Optical Components for Laser Applications. Günter Toesko - Laserseminar BLZ im Dezember Günter Toesko - Laserseminar BLZ im Dezember 2009 1 Aberrations An optical aberration is a distortion in the image formed by an optical system compared to the original. It can arise for a number of reasons

More information

Optical 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 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 information

Imaging Optics Fundamentals

Imaging Optics Fundamentals Imaging Optics Fundamentals Gregory Hollows Director, Machine Vision Solutions Edmund Optics Why Are We Here? Topics for Discussion Fundamental Parameters of your system Field of View Working Distance

More information

Reflectors vs. Refractors

Reflectors vs. Refractors 1 Telescope Types - Telescopes collect and concentrate light (which can then be magnified, dispersed as a spectrum, etc). - In the end it is the collecting area that counts. - There are two primary telescope

More information

Announcements. Image Formation: Outline. The course. How Cameras Produce Images. Earliest Surviving Photograph. Image Formation and Cameras

Announcements. Image Formation: Outline. The course. How Cameras Produce Images. Earliest Surviving Photograph. Image Formation and Cameras Announcements Image ormation and Cameras CSE 252A Lecture 3 Assignment 0: Getting Started with Matlab is posted to web page, due Tuesday, ctober 4. Reading: Szeliski, Chapter 2 ptional Chapters 1 & 2 of

More information

Chapter 25. Optical Instruments

Chapter 25. Optical Instruments Chapter 25 Optical Instruments Optical Instruments Analysis generally involves the laws of reflection and refraction Analysis uses the procedures of geometric optics To explain certain phenomena, the wave

More information

PHYSICS. Chapter 35 Lecture FOR SCIENTISTS AND ENGINEERS A STRATEGIC APPROACH 4/E RANDALL D. KNIGHT

PHYSICS. Chapter 35 Lecture FOR SCIENTISTS AND ENGINEERS A STRATEGIC APPROACH 4/E RANDALL D. KNIGHT PHYSICS FOR SCIENTISTS AND ENGINEERS A STRATEGIC APPROACH 4/E Chapter 35 Lecture RANDALL D. KNIGHT Chapter 35 Optical Instruments IN THIS CHAPTER, you will learn about some common optical instruments and

More information

Chapter 18 Optical Elements

Chapter 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 information

brief history of photography foveon X3 imager technology description

brief history of photography foveon X3 imager technology description brief history of photography foveon X3 imager technology description imaging technology 30,000 BC chauvet-pont-d arc pinhole camera principle first described by Aristotle fourth century B.C. oldest known

More information

Image Formation. World Optics Sensor Signal. Computer Vision. Introduction to. Light (Energy) Source. Surface Imaging Plane. Pinhole Lens.

Image Formation. World Optics Sensor Signal. Computer Vision. Introduction to. Light (Energy) Source. Surface Imaging Plane. Pinhole Lens. 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 Today Optics:

More information

EE119 Introduction to Optical Engineering Spring 2003 Final Exam. Name:

EE119 Introduction to Optical Engineering Spring 2003 Final Exam. Name: EE119 Introduction to Optical Engineering Spring 2003 Final Exam Name: SID: CLOSED BOOK. THREE 8 1/2 X 11 SHEETS OF NOTES, AND SCIENTIFIC POCKET CALCULATOR PERMITTED. TIME ALLOTTED: 180 MINUTES Fundamental

More information

Lecture 22: Cameras & Lenses III. Computer Graphics and Imaging UC Berkeley CS184/284A, Spring 2017

Lecture 22: Cameras & Lenses III. Computer Graphics and Imaging UC Berkeley CS184/284A, Spring 2017 Lecture 22: Cameras & Lenses III Computer Graphics and Imaging UC Berkeley, Spring 2017 F-Number For Lens vs. Photo A lens s F-Number is the maximum for that lens E.g. 50 mm F/1.4 is a high-quality telephoto

More information

Geometry of Aerial Photographs

Geometry of Aerial Photographs Geometry of Aerial Photographs Aerial Cameras Aerial cameras must be (details in lectures): Geometrically stable Have fast and efficient shutters Have high geometric and optical quality lenses They can

More information

Opto Engineering S.r.l.

Opto Engineering S.r.l. TUTORIAL #1 Telecentric Lenses: basic information and working principles On line dimensional control is one of the most challenging and difficult applications of vision systems. On the other hand, besides

More information

Using Optics to Optimize Your Machine Vision Application

Using Optics to Optimize Your Machine Vision Application Expert Guide Using Optics to Optimize Your Machine Vision Application Introduction The lens is responsible for creating sufficient image quality to enable the vision system to extract the desired information

More information

Lecture Outline Chapter 27. Physics, 4 th Edition James S. Walker. Copyright 2010 Pearson Education, Inc.

Lecture Outline Chapter 27. Physics, 4 th Edition James S. Walker. Copyright 2010 Pearson Education, Inc. Lecture Outline Chapter 27 Physics, 4 th Edition James S. Walker Chapter 27 Optical Instruments Units of Chapter 27 The Human Eye and the Camera Lenses in Combination and Corrective Optics The Magnifying

More information