light (energy) source surface imaging plane pinhole …elm/teaching/ppt/370/370_2.pptx.pdf · light...
TRANSCRIPT
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Introduction to Computer Vision Image Formation
Light (Energy) Source
Surface
Pinhole Lens
Imaging Plane
World Optics Sensor Signal
B&W Film
Color Film
TV Camera
Silver Density
Silver densityin three colorlayers
Electrical
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Introduction to Computer Vision Today and Next Lecture
■ EM spectrum and visible light ■ Distribution of light wavelengths ■ Linearity ■ Percent of light reflected off a surface.
● Linearity of reflected light. ■ Efficiency of a solar panel as a function of wavelength
● Linearity of solar panel power. ■ Photoreceptor response as a function of wavelength
● Linearity of photoreceptor output.
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Introduction to Computer Vision If a tree falls in a forest
■ If a tree falls in a forest, and there is no one there to hear it, does it make a sound?
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Introduction to Computer Vision If a tree falls in a forest
■ If a tree falls in a forest, and there is no one there to hear it, does it make a sound? ● Definition 1: Sound as a disturbance in a medium.
■ Answer: yes, there is a disturbance, and we can measure it.
● Definition 2: Sound as a perception by people (or other creatures). ■ Answer: no, there is no perception.
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Introduction to Computer Vision Light
■ Two very different, but related, subjects: ● The physics of light. ● The perception of light.
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Introduction to Computer Vision Simplifying Assumptions
■ Typical imaging scenario: ● visible light ● ideal lenses ● standard sensor (e.g. TV camera) ● opaque objects
■ Goal
To create 'digital' images which can be processed to recover some of the characteristics of the 3D world which was imaged.
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Introduction to Computer Vision Steps
World Optics Sensor Signal Digitizer Digital Representation
World reality Optics focus {light} from world on sensor Sensor converts {light} to {electrical energy} Signal representation of incident light as continuous electrical energy Digitizer converts continuous signal to discrete signal Digital Rep. final representation of reality in computer memory
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Introduction to Computer Vision Light and Absorption
■ What is the structure of light? ■ What kind of light is relevant for
● human vision? ● animal vision? ● computer vision?
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Introduction to Computer Vision Light: EM Spectrum
Electromagnetic Spectrum
http://www.lbl.gov/MicroWorlds/ALSTool/EMSpec/EMSpec2.html
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Introduction to Computer Vision EM Spectrum
http://commons.wikimedia.org/wiki/File:EM_Spectrum_Properties_edit.svg
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Introduction to Computer Vision Interaction of Light and Matter
■ When light strikes an object, ● It will be wholly or partly transmitted. ● It will be wholly or partly reflected. ● It will be wholly or partly absorbed. ● Physical surface properties dictate
what happens
■ When we see an object as blue or red or purple, ● what we're really seeing is a partial
reflection of light from that object. ● The color we see is what's left of the
spectrum after part of it is absorbed by the object.
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Introduction to Computer Vision Facts
■ Visible wavelengths: 380-780 nanometers. ■ Nanometer: 10^-9 meters. ■ From shortest to longest:
● gamma, X-ray, ultraviolet, visible, infrared, radar, FM radio, TV, shortwave (radio), AM radio
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Introduction to Computer Vision Visible Light
■ Why do we see the visible spectrum and not other frequencies of light? ● Rhodopsins, photopsins, melanopsins the biological
chemicals that transduce light in humans, only respond at these wavelengths.
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Introduction to Computer Vision Spectrum Facts
■ Higher energies (e.g. X-rays) harder to refract ● Can’t be practically used with lenses.
■ Not all animals are sensitive to the same spectrum: ● Example: Bees see some ultraviolet
■ More than one way to sense light: ● Feel it instead of seeing it.
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Introduction to Computer Vision The visible spectrum
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Introduction to Computer Vision The visible spectrum
Where’s white? Where’s brown? Where’s pink?
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Introduction to Computer Vision Newton 1666
From Voltaire's Eléments de la Philosophie de Newton, published in 1738
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Introduction to Computer Vision Composition of Natural Lights
■ Most light that you see has a large distribution of frequencies. ● These multiple frequencies may appear together to
form a single color of the spectrum: ■ red, blue, green, yellow
● Or, they may appear to form a color that is not in the spectrum ■ White, brown, pink, magenta
■ Why? ● Before we get to that, we will explore the distribution of
light in the world, and how it is transmitted, absorbed, etc.
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Introduction to Computer Vision Physics and Perception
■ Usually, there is a large spectrum of wavelengths present
■ Perception: ● We perceive a single color of light (for each pixel).
■ Perception and physics are not the same thing!
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Introduction to Computer Vision Spectral Distributions
■ Spectral distributions show the ‘amount’ of energy at each wavelength for a light source; e.g.
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Introduction to Computer Vision Spectral Reflectance Curves
■ Reflectance curves for objects that appear to be:
The wavelengths reflected or transmitted from or through an object determine the stimulus to the retina that provokes the optical nerve into sending responses to our brains that indicate color.
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Introduction to Computer Vision A Word about Units
■ What are the units of relative power? ■ For now, think of a standard setup:
● One square meter light detector. ● How many watts of power are landing on the light
detector for each wavelength of the spectrum? ● (watts per square meter per unit wavelength)
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Introduction to Computer Vision Light Bulb Spectra
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Introduction to Computer Vision Light bulbs
http://housecraft.ca/2012/09/30/the-diy-decorator’s-eco-friendly-lighting-dilemma/
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Introduction to Computer Vision Linearity
■ A function f(x) is linear if and only if: ● f(a)+f(b) = f(a+b), for all a and b.
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Introduction to Computer Vision Linearity
■ A function f(x) is linear if and only if: ● f(a)+f(b) = f(a+b), for all a and b.
■ Corollary: for a linear function, ● 2*f(a) = f(a)+f(a) = f(a+a) = f(2*a)
■ “If you double the input, you double the output.”
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Introduction to Computer Vision Linearity of Light
■ Double the intensity of sunlight ● Double the output of a solar panel
■ Double the duration of sunlight collection ● Double the output of a solar panel
■ Double the number of lightbulbs in a room ● Double the number of photons coming off of each
surface ■ Double the amount of light coming into the eye.
● Double the response of the rod and cone cells (the eye’s photoreceptors)
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Introduction to Computer Vision Linearity
■ Are these functions linear? ● f(x) = 2x+3 ● f(z) = 45z ● f(y) = 3y^2
● f(x) = Ax, where A is a matrix and x is a vector ● g(x) = Ax+y, where A is a matrix, x and y are vectors.
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Introduction to Computer Vision Light reflection
■ Surface absorbs a percentage of light for each wavelength
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Introduction to Computer Vision Solar panels
■ Linearity of solar panel responses: ● Because the response of solar panels to light is
approximately linear, we can calculate their total response as a sum of the responses to “individual” wavelengths:
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Introduction to Computer Vision Sensitivity of receptors
■ Light receptors (biological and artificial) have different sensitivity to different wavelengths
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Introduction to Computer Vision
■ End