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

Similar documents
Unit 1: Image Formation

Cameras. CSE 455, Winter 2010 January 25, 2010

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

TSBB09 Image Sensors 2018-HT2. Image Formation Part 1

Computer Vision. The Pinhole Camera Model

LENSES. INEL 6088 Computer Vision

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

Dr F. Cuzzolin 1. September 29, 2015

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

CSE 527: Introduction to Computer Vision

Image Formation and Capture. Acknowledgment: some figures by B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, and A. Theuwissen

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

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

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

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

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

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

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

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

Acquisition. Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros

Image Formation and Capture

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

Image Formation and Camera Design

CPSC 425: Computer Vision

How do we see the world?

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

VC 11/12 T2 Image Formation

Laboratory experiment aberrations

Building a Real Camera. Slides Credit: Svetlana Lazebnik

VC 14/15 TP2 Image Formation

ECEN 4606, UNDERGRADUATE OPTICS LAB

Overview. Image formation - 1

Lecture 02 Image Formation 1

Sensors and Sensing Cameras and Camera Calibration

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

Building a Real Camera

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

Chapter 25 Optical Instruments

Single-view Metrology and Cameras

A Simple Camera Model

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

ME 6406 MACHINE VISION. Georgia Institute of Technology

CS6670: Computer Vision

CS 443: Imaging and Multimedia Cameras and Lenses

Modeling and Synthesis of Aperture Effects in Cameras

Basic principles of photography. David Capel 346B IST

Astronomical Cameras

Lecture 9. Lecture 9. t (min)

OPTICAL SYSTEMS OBJECTIVES

Be aware that there is no universal notation for the various quantities.

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

1 Image Formation. 1.1 Optics 1.1

Lecture 7: Camera Models

Announcement A total of 5 (five) late days are allowed for projects. Office hours

Chapter 18 Optical Elements

Midterm Examination CS 534: Computational Photography

Virtual and Digital Cameras

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

VC 16/17 TP2 Image Formation

Lenses, exposure, and (de)focus

Image Formation: Camera Model

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

Lecture 4: Geometrical Optics 2. Optical Systems. Images and Pupils. Rays. Wavefronts. Aberrations. Outline

Reflectors vs. Refractors

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

Cameras, lenses and sensors

Intorduction to light sources, pinhole cameras, and lenses

Image Acquisition Hardware. Image Acquisition and Representation. CCD Camera. Camera. how digital images are produced

Image Acquisition and Representation. Camera. CCD Camera. Image Acquisition Hardware

Image Acquisition and Representation

Optical Systems: Pinhole Camera Pinhole camera: simple hole in a box: Called Camera Obscura Aristotle discussed, Al-Hazen analyzed in Book of Optics

Big League Cryogenics and Vacuum The LHC at CERN

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

Ch 24. Geometric Optics

Opto Engineering S.r.l.

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

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

Imaging Optics Fundamentals

Waves & Oscillations

Cameras, lenses, and sensors

Lecture 2 Camera Models

Breaking Down The Cosine Fourth Power Law

Applications of Optics

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

Image Acquisition and Representation. Image Acquisition Hardware. Camera. how digital images are produced how digital images are represented

LENSES. a. To study the nature of image formed by spherical lenses. b. To study the defects of spherical lenses.

Chapter 36. Image Formation

Notes from Lens Lecture with Graham Reed

The diffraction of light

Chapter 36. Image Formation

6.A44 Computational Photography

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

Mirrors and Lenses. Images can be formed by reflection from mirrors. Images can be formed by refraction through lenses.

E X P E R I M E N T 12

Digital Image Processing COSC 6380/4393

Test procedures Page: 1 of 5

Image Processing & Projective geometry

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

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

Lenses. A lens is any glass, plastic or transparent refractive medium with two opposite faces, and at least one of the faces must be curved.

Transcription:

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) where: x and y are spatial coordinates The value of f at any point (x, y) is proportional to the brightness or gray value of the image at that point.! Cannot be stored as such on a digital computer.

DIGITAL IMAGES A digitized image is one in which: Spatial and grayscale values have been made discrete. Intensities measured across a regularly spaced grid in x and y directions are sampled to 8 bits (256 values) per point for black and white, 3x8 bits per point for color images. They are stored as a two dimensional arrays of gray-level values. The array elements are called pixels and identified by their x, y coordinates.

PIXELS

IMAGE FORMATION Projection from surfaces to 2-D sensor. Where: Geometry How bright: Radiometry Stored how: Sensing

PINHOLE CAMERA MODEL Idealized model of the perspective projection: All rays go through a hole and form a pencil of lines. The hole acts as a ray selector that allows an i nverted i m a g e t o form.

MAGNET LIKE SLOPES

VIRTUAL IMAGE

CAMERA GEOMETRY Pinhole geometry without image reversal

COORDINATE SYSTEMS World, Camera, Image Coordinate Systems

CAMERA COORDINATE SYSTEM The center of the projection coincides with the origin of the world. The camera axis (optical axis) is aligned with the world s z-axis. To avoid image inversion, the image plane is in front of the center of projection.

1D IMAGE

2D IMAGE

DISTANT OBJECTS APPEAR SMALLER

PARALLEL LINES MEET

VANISHING POINTS The projections of parallel lines all meet at one point, called the vanishing point. As focal length and distance to camera increase, the image remains the same size but perspective effects diminish.

ROAD FOLLOWING Rasmussen et al., BMVC 04

PROJECTION IS NON LINEAR!Reformulate it as a linear operation.

HOMOGENEOUS COORDINATES Homogeneous representation of 2D point: Homogeneous representation of 3D point:! Projections become linear transformations.

SIMPLE PROJECTION MATRIX

INTRINSIC AND EXTRINSIC PARAMETERS Camera may not be at the origin, looking down the z-axis! Extrinsic parameters One unit in camera coordinates may not be the same as one unit in world coordinates! Intrinsic parameters

LINEAR CAMERA MODEL

<latexit sha1_base64="vcki0xckjcpbd39difrszkiyccm=">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 PRINCIPAL POINT u = X i + p u = fx/z + p u v = Y i + p v = fy/z + p v 2 3 f 0 p u K = 4 0 f p v 5 0 0 1

<latexit sha1_base64="7s3+g0bjnvs8dleehmwhxicgowq=">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</latexit> INHOMOGENEOUS SCALING u = u X i + p u = u X/Z + p u v = v Y i + p v = v Y/Z + p v K = 2 4 u 0 p u 0 v p v 0 0 1 3 5 The pixels are not necessarily square.

<latexit sha1_base64="l0b1q5kwgxjhoffsqjpju2jrxiw=">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</latexit> s encodes the non-orthogonality of the u and v directions. Very close to zero in modern cameras. AXIS SKEW K = 2 4 u s p u 0 v p v 0 0 1 3 5

ROTATION / TRANSLATION!Rotations and translations also expressed in terms of matrix multiplications in projective space.

FULL PROJECTION MATRIX Hartley, Chap 6.

CAMERA CALIBRATION Internal Parameters: Horizontal and vertical scaling (2) Principal points (2) Skew of the axis (1) External Parameters: Rotations (3) Translations (3)! 11 free parameters.

LIMITATIONS Idealization because the hole cannot be infinitely small Image would be infinitely dim Diffraction effects! Use of Lenses.

IMAGING WITH A LENS P: point emitting light in all directions. Image plane An ideal lens realizes the same projection as a pinhole but gathers much more light!

THIN LENS PROPERTIES Any incident ray traveling parallel to the optical axis, will refract and travel through the focal point on the opposite side of the lens. Any incident ray traveling through the focal point on the way to the lens will be refracted and travel parallel to the principal axis. An incident ray which passes through the center of the lens will in effect continue in the same direction that it had when it entered the lens. All rays emanating from P and entering the lens will converge at P optical axis Image plane

CAMERA OBSCURA Used by painters since the Renaissance to produce perspective projections. Direct ancestors to the first film cameras.

DURER 1471-1528 He clearly knew all about the perspective transform!

SHIFTING PERSPECTIVE China, 8th century: The focal point moves from one part of the image to the other. The characters are always seen at eye-level as the picture is unrolled.

THIN LENS EQUATION! Lens with focal distance f equivalent to pinhole camera with similar focal distance but larger aperture.

DEPTH OF FIELD vs APERTURE Large Aperture: Large blur circles Shallow depth of field Small Aperture: Low intensity Long exposure time

DEPTH OF FIELD Range of object distances (d-d ) over which the image is s u f f i c i e n t l y w e l l focused. Range for which blur circle is less than the resolution of the sensor. Small focal length > Large depth of field.

APERTURE aperture iris diaphragm Diameter d of the lens that is exposed to light.

BLUR CIRCLE blur circle (circle of confusion) Sensor Plane Out -of- Focus Point In Focus Point aperture Simple geometry: Thin lens equation:

<latexit sha1_base64="niudt8/qosbzdhlsmdbp5ss8ut0=">aaacwxicbvbnb9naffybfgrtinajl6fgvcobymkfocbvcofybkgvyjd6xj8nq67xzncnilz/ss4iib+cxcbxgx48avejmxma3ukrkyynot+e/2bnd2+/9zb4dpd4ydp+s+dftvlrtlneyljfpghickvtk6yki0otfqmk8/tqw1o//07aifj9saukkgixsusco3xuvp8n4lkaep7co4hzjbzbtjenlcsscnvdcfnlpg2g2cmr/cumm3e3sralpb0oie6cmjoypivabj8llbjcdchcoldr8kcihe9ywnl/ei2izcbdmo7aghvznu//jlos14ul4 CHANGING APERTURE f/11 1/30sec f/2.8 1/500sec Small aperture, long exposure. Small aperture, long exposure. r b = a s 0 f 2 (d 0 f)(d f) Small a! Small r b

<latexit sha1_base64="/fhj0k6p0jt4k8+bcifvgghuhk0=">aaacwxicbvbnj9mwfhqc7jbwvdgjlycatovalfychjbwcog4cmqu1itqxxlurxwcydugkps/yquh8veqcnscyjcn2rrnznpyk9dkwpckp4pwxs1bb4ed29gdu/fupxg+fptjvo3hnoevqsx5jpau1dr30ik6rw1hmss6yy/ebvwzr2ssrprht6kpk3glpzacnaewwy+qliiwyxxeqyom8ha71h53kcos7rlnxodz146l4+figywlf3epkau1u4wgzakuk3zkpf5fh0pucmirq7x3odhvtxh63ufey+eomss7getg2omr6+d0ofyefhvvsh CHANGING FOCAL LENGTH Wide field of view (small f) r b = a s 0 f 2 (d 0 f)(d f) Small f! Small r b Narrow field of view (large f)

DISTORTIONS The lens is not exactly a thin lens: Different wave lengths refracted differently Barrel Distortion

CHROMATIC ABERRATION Different wavelengths are refracted differently.

RADIAL LENS DISTORTIONS No Distortion Barrel Distortion Pincushion Distortion Radial distance from Image Center: r u = r d + k 1 r d 3

LENS SYSTEMS Aberrations can be minimized by aligning several lenses with well chosen Shapes, Refraction indices.

UNDISTORTING

UNDISTORTING Once the image is undistorted, the camera projection can be formulated as a projective transform.! The pinhole camera model applies.

RADIOMETRY Scene Radiance: Amount of light radiation from a surface point (Watt / m2 / Steradian) Image Irradiance: Amount of light incident at the image of the surface point. (Watt / m2) Fundamental Radiometric Equation:

VIGNETTING Images can get darker towards their edges because some of the light does not go through all the lenses.

DE VIGNETTING Y. Zheng, S. Lin, and S.B. Kang, CVPR 08

SENSOR ARRAY Photons free up electrons that are then captured by a potential well. Charges are transferred row by row wise to a register. Pixel values are read from the register.

SENSING Conversion of the optical image into an electrical image :! Quantization in Time Space

IN SHORT Camera geometry can be modeled in terms of the pinhole camera model, which is linear in projective space. Image radiance is roughly proportional to surface radiance and the two can be used interchangeably for our purposes.