Image formation and cameras

Slides:



Advertisements
Similar presentations
The Camera : Computational Photography Alexei Efros, CMU, Fall 2006.
Advertisements

CS 691 Computational Photography Instructor: Gianfranco Doretto 3D to 2D Projections.
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.
Cameras and Projections
Cameras. Overview The pinhole projection model Qualitative properties Perspective projection matrix Cameras with lenses Depth of focus Field of view Lens.
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.
Lecture 6: Image transformations and alignment CS6670: Computer Vision Noah Snavely.
Camera model Relation between pixels and rays in space ?
CSCE641: Computer Graphics Image Formation Jinxiang Chai.
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.
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.
Today: The Camera. Overview The pinhole projection model Qualitative properties Perspective projection matrix Cameras with lenses Depth of focus Field.
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.
Cameras, lenses, and calibration
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.
Image formation How are objects in the world captured in an image?
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.
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
776 Computer Vision Jan-Michael Frahm Fall Last class.
Peripheral drift illusion. Multiple views Hartley and Zisserman Lowe stereo vision structure from motion optical flow.
EECS 274 Computer Vision Cameras.
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.
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.
Computer vision: models, learning and inference
CS5670: Computer Vision Lecture 9: Cameras Noah Snavely
Jan-Michael Frahm Fall 2016
The Camera : Computational Photography
Announcements Project 1 Project 2
CSE 185 Introduction to Computer Vision
Announcements Project 1 Project 2 Due Wednesday at 11:59pm
CS4670 / 5670: Computer Vision Lecture 12: Cameras Noah Snavely
Lecture 13: Cameras and geometry
Lecturer: Dr. A.H. Abdul Hafez
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:

Image formation and cameras CSE P 576 Ali Farhadi Many slides from Larry Zitnick, Steve Seitz, Neeraj Kumar

Projection http://www.julianbeever.net/pave.htm

Projection http://www.julianbeever.net/pave.htm

Müller-Lyer Illusion http://www.michaelbach.de/ot/sze_muelue/index.html

Image formation Let’s design a camera Object Film 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 Object Barrier Film 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?

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 is known as the aperture How does this transform the image?

Camera Obscura Basic principle known to Mozi (470-390 BC), Aristotle (384-322 BC) Drawing aid for artists: described by Leonardo da Vinci (1452-1519) Gemma Frisius, 1558

Camera Obscura The first camera 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...

Pinhole cameras everywhere Sun “shadows” during a solar eclipse by Henrik von Wendt http://www.flickr.com/photos/hvw/2724969199/

Pinhole cameras everywhere Sun “shadows” during a solar eclipse http://www.flickr.com/photos/73860948@N08/6678331997/

Pinhole cameras everywhere Tree shadow during a solar eclipse photo credit: Nils van der Burg http://www.physicstogo.org/index.cfm

Shrinking the aperture

Adding a lens A lens focuses light onto the film “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

(Center Of Projection) Lenses F focal point optical center (Center Of Projection) 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) Real cameras use many lenses together (to correct for aberrations)

Thin lenses Thin lens equation: manipulate eq to show shape of focus region 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 assumption The thin lens assumption assumes the lens has no thickness, but this isn’t true… Object Lens Film Focal point By adding more elements to the lens, the distance at which a scene is in focus can be made roughly planar.

Depth of field Changing the aperture size affects depth of field Film 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 http://en.wikipedia.org/wiki/Depth_of_field

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 How do we refocus? Change the shape of the lens

Digital camera A digital camera replaces film with a sensor array Each cell in the array is a Charge Coupled Device (CCD) light-sensitive diode that converts photons to electrons CMOS is becoming more popular (esp. in cell phones) http://electronics.howstuffworks.com/digital-camera.htm

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., http://electronics.howstuffworks.com/digital-camera.htm http://www.dpreview.com/

Projection Mapping from the world (3d) to an image (2d) Can we have a 1-to-1 mapping? How many possible mappings are there? An optical system defines a particular projection. We’ll talk about 2: Perspective projection (how we see “normally”) Orthographic projection (e.g., telephoto lenses)

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 We get the projection by throwing out the last coordinate:

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

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

Perspective Projection Example 1. Object point at (10, 6, 4), d=2 2. Object point at (25, 15, 10) Perspective projection is not 1-to-1!

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

Perspective Projection What happens to parallel lines? What happens to angles? What happens to distances?

Perspective Projection What happens when d∞?

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

Orthographic (“telecentric”) lenses Navitar telecentric zoom lens http://www.lhup.edu/~dsimanek/3d/telecent.htm

Orthographic Projection What happens to parallel lines? What happens to angles? What happens to distances?

Camera parameters How many numbers do we need to describe a camera? We need to describe its pose in the world We need to describe its internal parameters

A Tale of Two Coordinate Systems v w u x y z COP Camera o Two important coordinate systems: 1. World coordinate system 2. Camera coordinate system “The World”

Camera parameters To project a point (x,y,z) in world coordinates into a camera First transform (x,y,z) into camera coordinates Need to know Camera position (in world coordinates) Camera orientation (in world coordinates) Then project into the image plane Need to know camera intrinsics These can all be described with matrices

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 (sx, sy) blue parameters are called “extrinsics,” red are “intrinsics” Projection equation The projection matrix models the cumulative effect of all parameters Useful to decompose into a series of operations projection intrinsics rotation translation identity matrix The definitions of these parameters are not completely standardized especially intrinsics—varies from one book to another

Extrinsics How do we get the camera to “canonical form”? (Center of projection at the origin, x-axis points right, y-axis points up, z-axis points backwards) Step 1: Translate by -c

Extrinsics How do we get the camera to “canonical form”? (Center of projection at the origin, x-axis points right, y-axis points up, z-axis points backwards) Step 1: Translate by -c How do we represent translation as a matrix multiplication?

Extrinsics How do we get the camera to “canonical form”? (Center of projection at the origin, x-axis points right, y-axis points up, z-axis points backwards) Step 1: Translate by -c Step 2: Rotate by R 3x3 rotation matrix

Extrinsics How do we get the camera to “canonical form”? (Center of projection at the origin, x-axis points right, y-axis points up, z-axis points backwards) Step 1: Translate by -c Step 2: Rotate by R

Perspective projection (converts from 3D rays in camera coordinate system to pixel coordinates) (intrinsics) in general, (upper triangular matrix) : aspect ratio (1 unless pixels are not square) : skew (0 unless pixels are shaped like rhombi/parallelograms) : principal point ((0,0) unless optical axis doesn’t intersect projection plane at origin)

Focal length Can think of as “zoom” Related to field of view 24mm 50mm

Projection matrix intrinsics projection rotation translation

Projection matrix = (in homogeneous image coordinates)

Distortion Radial distortion of the image Caused by imperfect lenses No distortion Pin cushion Barrel Many wide –angle lenses have radial distortion – visible curvature in the projection of straight lines. To create good reconstructions / panoramas etc. this distortion shouldbe taken into account. Barrel usually a product of wide angle lenses and pincushion usually in telephoto lenses. Radial distortion of the image Caused by imperfect lenses Deviations are most noticeable for rays that pass through the edge of the lens

Correcting radial distortion from Helmut Dersch

http://blog.photoshopcreative.co.uk/general/fix-barrel-distortion/

Radial Distortion

Modeling Radial Distortion Project to “normalized” image coordinates Apply radial distortion K1, k2 are radial distortion parameters. For a given lens. Apply focal length & translate image center To model lens distortion Use above projection operation instead of standard projection matrix multiplication

Other Types of Distortion Lens Vignetting Chromatic Aberrations Lens Glare [Slide adapted from Srinivasa Narasimhan]

Many other types of projection exist...

360 degree field of view… Basic approach Take a photo of a parabolic mirror with an orthographic lens (Nayar) http://www.cs.columbia.edu/CAVE/projects/cat_cam_360/gallery1/index.html Or buy one a lens from a variety of omnicam manufacturers… See http://www.cis.upenn.edu/~kostas/omni.html

Tilt-shift images from Olivo Barbieri and Photoshop imitations Simulates a miniature scene http://www.northlight-images.co.uk/article_pages/tilt_and_shift_ts-e.html Tilt-shift images from Olivo Barbieri and Photoshop imitations

Rotating sensor (or object) Rollout Photographs © Justin Kerr http://research.famsi.org/kerrmaya.html Also known as “cyclographs”, “peripheral images”

Photofinish

Human eye

Colors What colors do humans see? RGB tristimulus values, 1931 RGB CIE

Colors Plot of all visible colors (Hue and saturation):

Bayer pattern Some high end video cameras have 3 CCD chips.

Demosaicing How can we compute an R, G, and B value for every pixel?

Spectral response 3 chip CCD Bayer CMOS http://www.definitionmagazine.com/journal/2010/5/7/capturing-colour.html