computer vision image formation - università ca' foscari ...atorsell/visione/02-image...
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2 Computer Vision - Introduction
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3 Computer Vision - Introduction
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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?
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5 Computer Vision - Introduction
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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
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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
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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
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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