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1 Computer Vision - Introduction Computer Vision Image Formation Andrea Torsello [email protected]

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Page 1: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

1 Computer Vision - Introduction

Computer Vision

Image Formation

Andrea [email protected]

Page 2: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

2 Computer Vision - Introduction

Page 3: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

3 Computer Vision - Introduction

Page 4: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

4 Computer Vision - Introduction

What would you see looking at a perfectly specular sphere in a perfectly dark room? (completely absorbing)

What would you see if the sphere was painted white?

Page 5: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

5 Computer Vision - Introduction

Page 6: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

6 Computer Vision - Introduction

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7 Computer Vision - Introduction

RadiometryRadiometry: set of techniques for measuring electromagnetic radiation, including visible light

Radiant flux ( , Watt): radiant energy passing through a surface per unit of time

Irradiance ( , Watt/m2): radiant flux per surface element

Radiance ( , Watt/m2sr): Irradiance per emission angle

Measures the electromagnetic flux going through the infinitesimal area dA in the infinitesimal range of directions dw

Physical quantity related to luminosity

Page 8: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

8 Computer Vision - Introduction

BSSRDF and BRDF

Bidirectional Scattering Surface Reflectance Distribution Function (BSSRDF)

Functions that characterizes the local interaction between light and a surface

Bidirectional Reflectance Distribution Function (BRDF)

No internal scattering → xr=xi=x

Page 9: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

9 Computer Vision - Introduction

Lambertian Surfaces

Radiance depends on incident and observation angles

For some materials (e.g., chalk) the dependency to the viewing direction is weak or inexistent

Micro facets diffuse light randomly → radiance is uniform in all directions

Diffuse reflection follows Lambert's law

kx is called albedo

Page 10: Computer Vision Image Formation - Università Ca' Foscari ...atorsell/Visione/02-Image Formation.pdf · Computer Vision - Introduction 9 Lambertian Surfaces Radiance depends on incident

10 Computer Vision - Introduction

There are other effects and complex interactions between light and matter

Refraction Scattering Fluorescence Color bleeding

We will (mostly) ingore them!

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11 Computer Vision - Introduction

Photography 1826 Niepce made first photograph

Exposed paper covered with silver cloride in a camera obscura and then fixed the image with nitric acid

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12 Computer Vision - Introduction

Camera Obscura

CanalettoCampo San Giovanni e PaoloAccademia

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13 Computer Vision - Introduction

What happens if you place the film in front of an object?

What happens if you put a barrier with a small hole in front of it?

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14 Computer Vision - Introduction

Problems with camera obscura Hole too large → out of focus

Hole too small → image too dark

Hole size comparable to wave length → out of focus

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15 Computer Vision - Introduction

Pinhole Camera Model

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17 Computer Vision - Introduction

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18 Computer Vision - Introduction

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20 Computer Vision - Introduction

Sensor matrix An image can be modelled a sa

function

The domain is a (usully rectangular) subset of the real plane

A continuous image is converted into a digital one through a process of sampling and quantization

Sampling reduces the image to a finite set of spatial coordinates

Quantization reduces the sensor response (function value) to a finite set of values

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21 Computer Vision - Introduction

The result of the sampling process is a MxN matrix Each cell is called pixel