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CSCE 452 ROBOT VISION Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s EE4780

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Page 1: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

CSCE 452 ROBOT VISION

Introduction to Computer VisionDezhen Song,

Department Computer Science and EngineeringTexas A&M University

Part of slides are from Bahadir K. Gunturk’s EE4780

Page 2: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Digital Image Acquisition

Page 3: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Imaging Sensors Charge-Coupled Device (CCD)

Page 4: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Imaging Sensors Complementary Metal Oxide

Semiconductor (CMOS)

Page 5: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

CCD Vs. CMOS

Responsivity: CMOS >CCD Dynamic range: CCD is 2 times better Uniformity: CCD > CMOS Shuttering

CCD: synchronous shutter (better) CMOS: rolling shutter

Speed: CMOS >> CCD Reliability: CMOS >>CCD Cost: CMOS < CCD

Page 6: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Bahadir K. Gunturk 6

Matrix Representation of Images

A digital image can be written as a matrix

1 2

[0,0] [0,1] [0, 1]

[1,0] [1,1] [1, 1][ , ]

[ 1,0] [ 1, 1]MxN

x x x N

x x x Nx n n

x M x M N

35 45 20

43 64 52

10 29 39

Page 7: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

RGB Color Model

Page 8: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Dynamic range (Contrast Ratio)

Nature light: 1010:1 Human eye: 109:1 CMOS Sensor: 11000-6000:1 LCD panel: 1000-10000:1

Page 9: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Measured Dynamic Range

Bit Precisionof A/D Converter

Contrast Ratio

8 256:110 1024:112 4096:114 16384:116 65536:1

Page 10: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

10

ExposuresLong exposure time

Short exposure time

Page 11: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Cameras

2D camera (i.e. surveillance camera) Pin-hole camera Surveillance camera Robotic pan-tilt-zoom camera Wide angle camera –

fisheye, omni, etc 1D camera (satellite camera, scanner) Photo cell

Page 12: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

12

Perspective Projection Perspective projection equations

' ' 'x y z

x y z

Page 13: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

13

Pinhole Camera Model

Page 14: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

14

Cameras With Lenses Most cameras are equipped with lenses. There are two main reasons for this:

To gather light. To keep the picture in sharp focus while gathering

light from a large area.

Page 15: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

15

Real Lenses Rays may not focus at a single point.

Spherical aberration

Spherical aberration can be eliminated completely by designing aspherical lenses.

Page 16: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Bahadir K. Gunturk 16

Real Lenses

Chromatic Aberration

Page 17: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Bahadir K. Gunturk 17

Real Lenses Special lens systems using two or more pieces of glass with

different refractive indeces can reduce or eliminate this problem. However, not even these lens systems are completely perfect and still can lead to visible chromatic aberrations.

Page 18: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Finite projective camera

1yx

xx

p

ps

K

C~|IKRP

11 dof (5+3+3)

Page 19: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Camera Calibration

Page 20: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Bahadir K. Gunturk 20

Compound Lens Systems

Page 21: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

Lens modelling Thin lens Thick lens Lens with mirrors Radial Distortion

Page 22: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s

22

Real Lenses Barrel Distortion & Pincushion Distortion

Stop (Aperture)

Causes of distortion

(normal)

Chief ray

Page 23: Introduction to Computer Vision Dezhen Song, Department Computer Science and Engineering Texas A&M University Part of slides are from Bahadir K. Gunturk’s