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.

Slides:



Advertisements
Similar presentations
Last 4 lectures Camera Structure HDR Image Filtering Image Transform.
Advertisements

The Camera : Computational Photography Alexei Efros, CMU, Fall 2006.
CS 691 Computational Photography Instructor: Gianfranco Doretto 3D to 2D Projections.
Three-Dimensional Viewing Sang Il Park Sejong University Lots of slides are stolen from Jehee Lee’s.
Computer Vision CS 776 Spring 2014 Cameras & Photogrammetry 1 Prof. Alex Berg (Slide credits to many folks on individual slides)
The Camera CS194: Image Manipulation & Computational Photography Alexei Efros, UC Berkeley, Fall 2014.
Projection Readings Szeliski 2.1. Projection Readings Szeliski 2.1.
Image formation and cameras CSE P 576 Larry Zitnick Many slides courtesy of Steve Seitz.
Modeling Light : Rendering and Image Processing Alexei Efros.
Announcements. Projection Today’s Readings Nalwa 2.1.
Lecture 5: Projection CS6670: Computer Vision Noah Snavely.
Announcements Mailing list (you should have received messages) Project 1 additional test sequences online Talk today on “Lightfield photography” by Ren.
CS485/685 Computer Vision Prof. George Bebis
Lecture 5: Cameras and Projection CS6670: Computer Vision Noah Snavely.
Camera model Relation between pixels and rays in space ?
CSCE641: Computer Graphics Image Formation Jinxiang Chai.
Lecture 13: Projection, Part 2
Lecture 4a: Cameras CS6670: Computer Vision Noah Snavely Source: S. Lazebnik.
Lecture 12: Projection CS4670: Computer Vision Noah Snavely “The School of Athens,” Raphael.
Lecture 6: Image Warping and Projection CS6670: Computer Vision Noah Snavely.
Panoramas and Calibration : Rendering and Image Processing Alexei Efros …with a lot of slides stolen from Steve Seitz and Rick Szeliski.
CS223b, Jana Kosecka Rigid Body Motion and Image Formation.
Single-view Metrology and Camera Calibration Computer Vision Derek Hoiem, University of Illinois 02/26/15 1.
CS4670 / 5670: Computer Vision KavitaBala Lecture 15: Projection “The School of Athens,” Raphael.
The Camera : Computational Photography Alexei Efros, CMU, Fall 2008.
CSCE 641: Computer Graphics Image Formation & Plenoptic Function Jinxiang Chai.
Cameras, lenses, and calibration
Image formation and cameras
Perspective projection
Cameras CSE 455, Winter 2010 January 25, 2010.
EEM 561 Machine Vision Week 10 :Image Formation and Cameras
Building a Real Camera.
Image formation & Geometrical Transforms Francisco Gómez J MMS U. Central y UJTL.
Northeastern University, Fall 2005 CSG242: Computational Photography Ramesh Raskar Mitsubishi Electric Research Labs Northeastern University September.
Lecture 14: Projection CS4670 / 5670: Computer Vision Noah Snavely “The School of Athens,” Raphael.
776 Computer Vision Jan-Michael Frahm Fall Camera.
CPSC 641: Computer Graphics Image Formation Jinxiang Chai.
Image Formation Fundamentals Basic Concepts (Continued…)
Recap from Wednesday Two strategies for realistic rendering capture real world data synthesize from bottom up Both have existed for 500 years. Both are.
776 Computer Vision Jan-Michael Frahm, Enrique Dunn Spring 2013.
Single-view Metrology and Camera Calibration Computer Vision Derek Hoiem, University of Illinois 01/25/11 1.
Geometric Camera Models
Peripheral drift illusion. Multiple views Hartley and Zisserman Lowe stereo vision structure from motion optical flow.
EECS 274 Computer Vision Cameras.
CS-498 Computer Vision Week 7, Day 2 Camera Parameters Intrinsic Calibration  Linear  Radial Distortion (Extrinsic Calibration?) 1.
CSE 185 Introduction to Computer Vision Cameras. Camera models –Pinhole Perspective Projection –Affine Projection –Spherical Perspective Projection Camera.
Projection Readings Nalwa 2.1 Szeliski, Ch , 2.1
More with Pinhole + Single-view Metrology
Image Formation and Cameras CSE 455 Linda Shapiro 1.
Announcements Project 1 grading session this Thursday 2:30-5pm, Sieg 327 –signup ASAP:signup –10 minute slot to demo your project for a TA »have your program.
Example: warping triangles Given two triangles: ABC and A’B’C’ in 2D (12 numbers) Need to find transform T to transfer all pixels from one to the other.
Lecture 14: Projection CS4670 / 5670: Computer Vision Noah Snavely “The School of Athens,” Raphael.
Lecture 18: Cameras CS4670 / 5670: Computer Vision KavitaBala Source: S. Lazebnik.
CSE 185 Introduction to Computer Vision
PNU Machine Vision Lecture 2a: Cameras Source: S. Lazebnik.
CS5670: Computer Vision Lecture 9: Cameras Noah Snavely
The Camera : Computational Photography
Announcements Project 1 Project 2
CSE 185 Introduction to Computer Vision
CSCE 441 Computer Graphics 3-D Viewing
Announcements Project 1 Project 2 Due Wednesday at 11:59pm
Lecture 13: Cameras and geometry
Geometric Camera Models
The Camera : Computational Photography
Announcements Midterm out today Project 1 demos.
Projection Readings Nalwa 2.1.
Credit: CS231a, Stanford, Silvio Savarese
Projection Readings Szeliski 2.1.
Announcements Midterm out today Project 1 demos.
Presentation transcript:

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 next week signup for panorama kits find group of 3-4 people

Projection Readings Nalwa 2.1 (supplemental): Forsyth Chaps 1-2

Müller-Lyer Illusion by Pravin Bhat

Image formation Let’s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable image?

Pinhole camera Add a barrier to block off most of the rays This reduces blurring The opening known as the aperture How does this transform the image?

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

Shrinking the aperture Why not make the aperture as small as possible? Less light gets through Diffraction effects...

Shrinking the aperture

Adding a lens 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 “circle of confusion”

Lenses 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) focal point F optical center (Center Of Projection)

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 )

Depth of field Changing the aperture size affects depth of field A smaller aperture increases the range in which the object is approximately in focus

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

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 –

Modeling projection The coordinate system We will use the pin-hole model as an approximation Put the optical center (Center Of Projection) at the origin Put the image plane (Projection Plane) in front of the COP –Why? The camera looks down the negative z axis –we need this if we want right-handed-coordinates –

Modeling projection Projection equations Compute intersection with PP of ray from (x,y,z) to COP Derived using similar triangles (on board) We get the projection by throwing out the last coordinate:

Homogeneous coordinates Is this a linear transformation? Trick: add one more coordinate: homogeneous image coordinates homogeneous scene coordinates Converting from homogeneous coordinates no—division by z is nonlinear

Perspective Projection Projection is a matrix multiply using homogeneous coordinates: divide by third coordinate This is known as perspective projection The matrix is the projection matrix Can also formulate as a 4x4 (today’s reading does this) divide by fourth coordinate

Perspective Projection How does scaling the projection matrix change the transformation?

Orthographic projection Special case of perspective projection Distance from the COP to the PP is infinite Also called “parallel projection”: (x, y, z) → (x, y) What’s the projection matrix? Image World

Other types of projection Scaled orthographic Also called “weak perspective” Affine projection Also called “paraperspective”

Projection equation The projection matrix models the cumulative effect of all parameters Useful to decompose into a series of operations projectionintrinsicsrotationtranslation identity matrix Camera parameters A camera is described by several parameters Translation T of the optical center from the origin of world coords Rotation R of the image plane focal length f, principle point (x’ c, y’ c ), pixel size (s x, s y ) blue parameters are called “extrinsics,” red are “intrinsics” The definitions of these parameters are not completely standardized –especially intrinsics—varies from one book to another

Distortion Radial distortion of the image Caused by imperfect lenses Deviations are most noticeable for rays that pass through the edge of the lens No distortionPin cushionBarrel

Correcting radial distortion from Helmut DerschHelmut Dersch

Distortion

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