Computer Vision Spring 2006 15-385,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm.

Slides:



Advertisements
Similar presentations
© 2005 Pearson Prentice Hall This work is protected by United States copyright laws and is provided solely for the use of instructors in teaching their.
Advertisements

Human Eye  A human eyeball is like a simple camera! Sclera: outer walls, hard, like a light-tight box. Cornea and crystalline lens (eyelens): the two.
16421: Vision Sensors Lecture 6: Radiometry and Radiometric Calibration Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 2:50pm.
Advanced Computer Vision Cameras, Lenses and Sensors.
Cameras (Reading: Chapter 1) Goal: understand how images are formed Camera obscura dates from 15 th century Basic abstraction is the pinhole camera Perspective.
Lecture 22 Wave Optics-3 Chapter 22 PHYSICS 270 Dennis Papadopoulos April 2, 2010.
Image Formation and Optics
Lecture 30: Light, color, and reflectance CS4670: Computer Vision Noah Snavely.
Announcements. Projection Today’s Readings Nalwa 2.1.
Announcements Mailing list (you should have received messages) Project 1 additional test sequences online Talk today on “Lightfield photography” by Ren.
Modeling the imaging system Why? If a customer gives you specification of what they wish to see, in what environment the system should perform, you as.
Announcements Mailing list Project 1 test the turnin procedure *this week* (make sure it works) vote on best artifacts in next week’s class Project 2 groups.
Optical Instruments Chapter 25.
Compound lenses --- what’s the purpose? Correct bad optics with additional optics --- Hubble telescope, eyeglasses, etc. Correct or minimize aberations:
LENSES.
7. Optical instruments 1) Cameras
Announcements Office hours: My office hours today 2 -3 pm
CPSC 643 Robot Vision Introduction to Computer Vision Dezhen Song,
The Human Eye and Vision The structure of the eye –Iris –Cornea –Lens Focusing –Cornea –Accommodation The Retina –Photoreceptors –Processing time –Sensitivity.
1 Chapter 5: The Human Eye and Vision - I: Producing the Image Using what we have learned about lenses and cameras to understand how the eye works The.
Basic Principles of Imaging and Photometry Lecture #2 Thanks to Shree Nayar, Ravi Ramamoorthi, Pat Hanrahan.
Building a Real Camera.
Image formation & Geometrical Transforms Francisco Gómez J MMS U. Central y UJTL.
Magnifying Glass. Can a Diverging Lens used as magnifying glass?
Copyright © 2009 Pearson Education, Inc. Chapter 33 Lenses and Optical Instruments.
ECE 472/572 – Digital Image Processing Lecture 2 – Elements of Visual Perception and Image Formation 08/25/11.
Image Formation CS418 Computer Graphics John C. Hart.
Design of photographic lens Shinsaku Hiura Osaka University.
Lenses Chapter 30.
Physics 213 General Physics Lecture Last Meeting: Diffraction Today: Optical Instruments.
Lenses in Combination The analysis of multi-lens systems requires only one new rule: The image of the first lens acts as the object for the second lens.
Human Eye  A human eyeball is like a simple camera! Sclera: White part of the eye, outer walls, hard, like a light-tight box. Cornea and crystalline lens.
Chapter 30 Key Terms June 4 – June 10 Mr. Gaydos.
Lens Aberrations The departures of actual images from the ideal predicted by our simplified model are called aberrations. Using Snell’s law at each refracting.
Digital Photography A tool for Graphic Design Graphic Design: Digital Photography.
Optical Instruments, Camera A single lens camera consists basically of an opaque box, converging lens and film. Focusing depends on the object distance.
Fundamental of Optical Engineering Lecture 3.  Aberration happens when the rays do not converge to a point where it should be. We may distinguish the.
Camera Geometry and Calibration Thanks to Martial Hebert.
Chapter 6 Human Vision can be corrected and extended using optical systems.
16421: Vision Sensors Lecture 7: High Dynamic Range Imaging Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 3:00pm.
MIT 2.71/ /22/04 wk3-b-1 Imaging Instruments (part I) Principal Planes and Focal Lengths (Effective, Back, Front) Multi-element systems Pupils &
Copyright © 2010 Pearson Education, Inc. Lecture Outline Chapter 27 Physics, 4 th Edition James S. Walker.
Chapter 23 Properties of Light. Section 1: Objectives Use ray diagrams to show how light is reflected or refracted. Compare plane mirrors, concave mirrors,
PHYS 1442 – Section 004 Lecture #22-23 MW April 14-16, 2014 Dr. Andrew Brandt 1 Cameras, Film, and Digital The Human Eye; Corrective Lenses Magnifying.
Sebastian Thrun CS223B Computer Vision, Winter Stanford CS223B Computer Vision, Winter 2005 Lecture 2 Lenses and Camera Calibration Sebastian Thrun,
Chapter 34 Lecture Eight: Images: II. Image Formed by a Thin Lens A thin lens is one whose thickness is small compared to the radii of curvature For a.
1 Computer Vision 一目 了然 一目 了然 一看 便知 一看 便知 眼睛 頭腦 眼睛 頭腦 Computer = Image + Artificial Vision Processing Intelligence Vision Processing Intelligence.
Background Research. material selection for launching of telescope; must be soft, able to absorb vibration, fit within the appropriate temperature range.
16421: Vision Sensors Lecture 7: High Dynamic Range Imaging Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 3:00pm.
Geometrical Optics Chapter 24 + Other Tidbits 1. On and on and on …  This is a short week.  Schedule follows  So far, no room available for problem.
Image Formation CS418 Computer Graphics John C. Hart.
Lenses Chapter 30. Converging and Diverging Lenses  Lens – a piece of glass which bends parallel rays so that they cross and form an image  Converging.
Intelligent Vision Systems Image Geometry and Acquisition ENT 496 Ms. HEMA C.R. Lecture 2.
Lenses. Diverging and Converging Lenses Double Convex lenses focus light rays to a point on the opposite side of the lens. Double Concave lenses diverge.
EECS 274 Computer Vision Cameras.
Miguel Tavares Coimbra
CSE 185 Introduction to Computer Vision Cameras. Camera models –Pinhole Perspective Projection –Affine Projection –Spherical Perspective Projection Camera.
Lecture Outlines Chapter 27 Physics, 3rd Edition James S. Walker
Intelligent Vision Systems Image Geometry and Acquisition ENT 496 Ms. HEMA C.R. Lecture 2.
Instructor: Mircea Nicolescu Lecture 3 CS 485 / 685 Computer Vision.
Lens Applications.
Chapter 20 Mirrors and Lenses
Psychophysical theories Signal detection theory: A psychophysical theory that quantifies the response of an observer to the presentation of a signal in.
PL3020/FM2101/PL2033 Physiology Vision 1.
CSE 185 Introduction to Computer Vision
6.2 Extending Human Vision
Magnifying Glass.
CSE 185 Introduction to Computer Vision
EECS 274 Computer Vision Cameras.
Photographic Image Formation I
Presentation transcript:

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm

Image Sensing Lecture #3

Recap: Pinhole and the Perspective Projection (x,y) screen scene Is an image being formed on the screen? YES! But, not a “clear” one. image plane effective focal length, f’ optical axis y x z pinhole

Vanishing Points

Exposure 4 secondsExposure 96 minutes Images copyright © 2000 Zero Image Co. Pinhole Images

Image Formation using Lenses Lenses are used to avoid problems with pinholes. Ideal Lens: Same projection as pinhole but gathers more light! i o Gaussian Thin Lens Formula: f is the focal length of the lens – determines the lens’s ability to refract light f different from the effective focal length f’ discussed before! P P’ f

Common Lens Related Issues - Summary Compound (Thick) Lens Vignetting Chromatic AbberationRadial and Tangential Distortion thickness principal planes nodal points B A more light from A than B ! Lens has different refractive indices for different wavelengths. image plane ideal actual ideal actual

Lens Glare Stray interreflections of light within the optical lens system. Happens when very bright sources are present in the scene. Reading:

Vignetting B A L3 L1 L2 More light passes through lens L3 for scene point A than scene point B Results in spatially non-uniform brightness (in the periphery of the image)

Vignetting photo by Robert Johnes

Chromatic Aberration longitudinal chromatic aberration transverse chromatic aberration (axial)(lateral)

Chromatic Aberrations longitudinal chromatic aberration transverse chromatic aberration (axial)(lateral)

Geometric Lens Distortions Radial distortionTangential distortion Both due to lens imperfection Rectify with geometric camera calibration Photo by Helmut Dersch

Radial Lens Distortions No DistortionBarrel DistortionPincushion Distortion r u = r d + k 1 r d 3 Radial distance from Image Center:

Correcting Radial Lens Distortions Before After

Topics to be Covered Image Sensors Sensing Brightness Sensing Color Our Eyes

Image Sensors Considerations Speed Resolution Signal / Noise Ratio Cost

Image Sensors Convert light into electric charge CCD (charge coupled device) Higher dynamic range High uniformity Lower noise CMOS (complementary metal Oxide semiconductor) Lower voltage Higher speed Lower system complexity

Sensor Readout CCD Bucket Brigade

Sensor Readout CCD Bucket Brigade Images Copyright © 2000 TWI Press, Inc.

CCD Performance Characteristics Resolution: 1k x 1k packed in 1-2 cm No space between Pixels No Photons wasted 2

CCD Performance Characteristics Pixels must have same area Only 3 tessellations possible:

CCD Performance Characteristics Linearity Principle: Incoming photon flux vs. Output Signal Sometimes cameras are made non-linear on purpose. Calibration must be done (using reflectance charts)---covered later Dark Current Noise: Non-zero output signal when incoming light is zero Sensitivity: Minimum detectable signal produced by camera

Sensing Brightness pixel intensity light (photons) However, incoming light can vary in wavelength Quantum Efficiency Pixel intensity: For monochromatic light with flux :

Sensing Brightness Incoming light has a spectral distribution So the pixel intensity becomes

Sensing Color Assume we have an image  We know the pixel value  We know our camera parameters Can we tell the color of the scene? (Can we recover the spectral distribution ) Use a filter Where then

Rods and Cones RodsCones Achromatic: one type of pigment Chromatic: three types of pigment Slow response (long integration time) Fast response (short integration time) High amplification High sensitivity Less amplification Lower absolute sensitivity Low acuityHigh acuity

How do we sense color? Do we have infinite number of filters? rod cones Three filters of different spectral responses

Sensing Color Tristimulus (trichromatic) values Camera’s spectral response functions:

Sensing Color beam splitter light 3 CCD Bayer pattern Foveon X3 TM

Color Chart Calibration Important preprocessing step for many vision and graphics algorithms Use a color chart with precisely known reflectances. Irradiance = const * Reflectance Pixel Values 3.1%9.0%19.8%36.2%59.1%90% Use more camera exposures to fill up the curve. Method assumes constant lighting on all patches and works best when source is far away (example sunlight). Unique inverse exists because g is monotonic and smooth for all cameras g ? ?

Measured Response Curves of Cameras [Grossberg, Nayar]

Dark Current Noise Subtraction Dark current noise is high for long exposure shots To remove (some) of it: Calibrate the camera (make response linear) Capture the image of the scene as usual Cover the lens with the lens cap and take another picture Subtract the second image from the first image

Dark Current Noise Subtraction Original image + Dark Current Noise Image with lens cap on Result of subtraction Copyright Timo Autiokari,

Our Eyes  Index of refraction: cornea 1.376, aqueous 1.336, lens  Iris is the diaphragm that changes the aperture (pupil)  Retina is the sensor where the fovea has the highest resolution Cornea Sclera Iris Pupil

Accommodation Changes the focal length of the lens shorter focal length

Myopia and Hyperopia (myopia)

Astigmatism The cornea is distorted causing images to be un-focused on the retina.

Blind Spot in Eye Close your right eye and look directly at the “+”

Eyes in Nature Mosquito Mosquitos have microscopic vision, but to focus at large distances their would need to be 1 m!

Curved Mirrors in Scallop Eyes (by Mike Land, Sussex) Telescopic Eye … More in the last part of the course

Next Class Binary Image Processing Horn, Chapter 3