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# 08-28: Capturing Light… in man and machine #

• Etymology
• Photo means light
• Graphy means drawing/writing
• We take many samples of an image, typically in [0, 255]
• Rolling shutter occurs when some object is moving noticeably faster than the sampling rate
• Pictures are not a single instance

## The Eye #

• Saccadic eye movement: Unlike an image, we see everything except what we’re looking at is blurred
• Iris: colored annulus with radial muscles
• Pupil: the hole (aperture) whose size is controlled by the iris
• Photoreceptor cells (rods and cones) in the retina act as the ‘film’
• Inside-out retina (in humans, and most other animals) is messy:
• Light has to pass through various layers of ‘wiring’
• Optical nerve produces blind spot (no rods or cones)
• Two types of light-sensitive receptors:
1. Cones
• cone-shaped
• less sensitive
• operate in high light
• color vision
• concentrated in the fovea (center of the retina)
2. Rods
• rod-shaped
• highly sensitive
• operate at night
• gray-scale vision
• concentrated in the periphery of the retina

When looking at the night sky, you will be able to see more out of the corner of your eye because that’s where there are more rods– cones do very little to help you see the light from the stars.

## Physics of Light #

• We can see 400-700 nanometers (visible light on the electromagnetic spectrum)
• This is the range that the Sun radiates
• Color is psychophysical: it’s a property of the brain, not the light
• Any patch of light can be completely described physically by its spectrum: the number of photons (per time unit) at each wavelength from 400 to 700 nm.
• The perceived color of an object corresponds to the spectrum of light that it reflects.
• Small (blue), Medium (green), Large (red) cones then ‘parse’ the spectrum into color
• M and L (green and red) are close to one another

## Color Vision #

• Colorblind (not trichromatic) people have a different set of cones
• Deuteranopia: green-blind, missing M cones
• Protanopia: red-blind, missing L cones
• Tritanopia: blue-blind, missing S cones
• “M” and “L” on the X-chromosome
• Why men are more likely to be color blind
• “L” has high variation, so some women are tetrachromatic
• Some animals have
• 1 (night animals)
• 2 (e.g., dogs)
• 4 (fish, birds)
• 5 (pigeons, some reptiles/amphibians)
• 12 (mantis shrimp)

• Rods an cones act as a filter on the spectrum:

The “photometer metaphor” of color perception: Color perception is determined by the spectrum of light on each retinal receptor (as measured by a photometer

• We can approximate the spectrum with a linear combination of the three cone responses
• Most of the information is lost
• As a result, two different spectra may appear indistinguishable
• such spectra are known as metamers
• Distribution of color can be interpreted as…
• Mean corresponds to the hue
• Variance corresponds to the saturation
• Area corresponds to the brightness
• Color Constancy: the ability to perceive the invariant color of a surface despite ecological Variations in the conditions of observation.
• Another of these hard inverse problems: Physics of light emission and surface reflection underdetermine perception of surface color

## Cameras #

• White balancing
• Manual
• Choose color-neutral object to normalize
• Automatic (AWB)
• Grey world: force average color of scene to grey
• White world: force brightest object to white
• Our eyes are most sensitive to green light, so we often have more green sensors (ex. Bayer Filter)
• Storing values in a matrix; x corresponds to the columns and y corresponds to the rows

## Color Spaces #

• Easy for devices
• But not perceptually uniform
• Where do the grays live?
• Where is hue and saturation?`
• CMYK (Cyan, Magenta, Yellow, Key) – subtractive (ink)
• Used in printing/painting
• White is the background; black is the additive value of all colors
• HSV (Hue, Saturation, Value) – cylindrical
• Hue (kind of color) is the angle
• Red: 0
• Green: 120
• Blue: 240
• Saturation (purity) is the distance from the center
• Value (lightness) is the total amount of light
• Lab (Luminance, a, b) – perceptually uniform
• Luminance is the amount of light
• Humans are much more sensitive to changes L
• a corresponds to red to green
• b corresponds to blue to yellow

# 09-07: Convolution and Image Derivatives #

• When calculating the moving average, it’s smart to apply non-uniform weights (e.x gaussian) to account for outliers
• $\sigma$ (std) determines the extent of smoothing
• $\sigma \to 0$: more concentrated, single pixel (no smoothing)
• $\sigma \to \infty$: box filter (blurry image)
• Kernel (size of rectangle) should be $\approx 3 \sigma$
• Too big: extra 1-weight around edges
• Too small: doesn’t cover full range

## Convolution #

• Cross-correlation: $G = H \otimes F$
• Signal $F$ and filter $H$
• Not commutative (order matters)
• Convolution: $G = H \star F$
• Commutative, associative, distributive over addition, scalars factor out
• Associative shows us that applying multiple filters is equal to applying a single combined filter: $(((a\star b_1)\star b_2) \star b_3 \equiv a \star (b_1 \star b_2 \star b_3)$
• Convolving with self is another Gaussian with $\sigma \sqrt 2$

## Downsampling #

• Subsampling: lazy way of downsampling
• Removes every other row/cols of pixels
• Undersampling: Occurs when too few samples taken
• Gaussian (lowpass) pre-filtering
• Solution: filter (blur) the image then resample
• Filter size should double for each 1/2 size reduction
• Applying a blur filter is also smart when taking the gradient of an image
• Image pyramids can also be implement to speed up process

# 9-12: The Frequency Domain #

• Humans have a wacky non-linear/predictable perception of spatial frequencies
• Campbell-Robson contrast sensitivity curve
• Humans have innate antialiasing
• Cats have a left-shifted curve (more sensitive at lower frequencies) for hunting in the dark
• Eagles have a right-shifted curve (more sensitive at higher frequencies) for seeing prey at a distance
• We can decompose an image into it’s spatial domain ($n^2$ vectors)
• We can transform this into a series of $sin/cos$ waves composing it’s own basis
• Jean Baptiste Joseph Fourier (1768-1830) said “Any univariate function can be rewritten as a weighted sum of sines and cosines of different frequencies”
• We can compose any signal as a sum of sines and cosines
• $f(x) = A\sin(\omega x + \phi)$
• $\omega$ is the frequency (change in $x$ per cycle)
• $A$ is the amplitude
• $\phi$ is the phase, captures spatial information – what we discard when going to ‘fourier-basis’
• We take $f(x) \to_\text{FFT} F(\omega)$
• For every $\omega$ from 0 to inf, $F(\omega)$ holds the amplitude $A$ and phase $\phi$ of the corresponding sine:
• $F(\omega) = R(\omega) + i l(\omega)$
• $A = \pm \sqrt{R^2 + l^2}$
• $\phi = \tan ^{-1}\left(\dfrac{l(\omega)}{R(\omega)}\right)$

• Sufficient, finite $\omega$ can be stored in a lookup table $\implies$ fast + low memory and very little loss of information
• Technically drops some information that’s overlapping, but this is okay because the approximation of the range of $\omega$ is very accurate
• So basis/representation is not necessarily unique (but it is in most cases in the wild)
• Storing/encoding image: $M \cdot f(x) = F(\omega)$
• $f(x)$ is the input vector of the image
• $M$ is the matrix of sines and cosines
• $F(\omega)$ is the representation in new $\omega$ basis
• Decoding image: $M^{-1} \cdot F(\omega) = \hat f(x)$
• $M^{-1}$ is the inverse of the matrix of sines and cosines
• $F(\omega)$ is the representation in new $\omega$ basis
• $\hat f(x)$ is the output vector of the image
• Now store basis of $F \times N$ vectors $\implies$ space complexity is $\mathcal O (FN)$ where $N$ is the number of $\omega$ values
• $\mathcal O (FN) \ll \mathcal O(N^2)$
• Inverse $M^{-1}$ is easy to compute when knowing $M$

Local change in one domain, courses global change in the other

Low (top) and High (bottom) Pass filtering

• The Convolution Theorem

• The Fourier transform of the convolution of two functions is the product of their Fourier transforms $$F[g \star h] = F[g]F[h]$$

• The inverse Fourier transform of the product of two Fourier transforms is the convolution of the two inverse Fourier transforms $$F^{-1}[gh] = F[g]^{-1}\star F[h]^{-1}$$

• We can transform our FFT basis into a series of gaussian

# 09-14: Pyramid Blending, Templates, NL Filters #

## Hybrid Images #

• We can get the details from an image by subtracting the smoothed version of the image from the original
• Then to sharpen an image we can add a scalar of the details back in
• This is called an unsharp mask and results in a Laplacian of Gaussian (LoG) filter
• Laplacian Pyramid: We can take multiple samples of the difference between the original and increasingly smoothed version os the image (called the octaves)
• We can then reconstruct the original image by adding the smoothed version of the image back to the details

## Blending #

• Alpha blending: We can blend two images by linearly interpolating between the two images
• $I_{blend} = \alpha I_1 + (1 - \alpha) I_2$
• Total sums to 1 so we have full opacity
• Window Size
• Ideal size is equal to the largest prominent feature, $\phi$
• The larger the window, the more ghosting
• Minimal ghosting when window <= 2$\cdot \phi$
• Too small of a window is a regular crop
• Casting in Fourier domain
• Largest frequency <= 2*smallest frequency
• Image frequency content should occupy one ‘octave’ (power of two)
• Laplacian Pyramid: Blending
1. Build Laplacian pyramids LA and LB from images A and B
2. Build a Gaussian pyramid GR from selected region R
• High frequency (first level) has fine details, small blend window
• Low frequency (last level) has blurry figures, large blend window
3. Form a combined pyramid LS from LA and LB using nodes of GR as weights:
• $LS(i,j) = GR(I,j,)\cdot LA(I,j) + (1-GR(I,j))\cdot LB(I,j)$
4. Collapse the LS pyramid to get the final blended image
• Two-band blending:
• Alternatively to Laplacian Pyramid, you can use high and low frequencies w/o downsmampling
• Blends low freq smoothly
• Blends high freq with no smoothing; use binary $\alpha$
• Huffman coding: Lossless compression
• Generate pixel histogram
• Then generate pixel code based how often each pixel in the histogram occurs
• Most common colors have fewer bits
• Maximally 2x compression – sounds good but not significant
• JPEG compression
• Lossy compression – takes advantage of human vision not being able to notice high frequencies
• Colors layers are downsampled since people have bad resolution for colors
• Cut into 8x8 blocks (standard) and subtract 128
• Small block: faster; correlation exists between neighboring pixels
• Large block; better compression in smooth regions
• For each block…
• Compute DCT (discrete cosine transform)
• Quantize
• More coarsely for high frequencies (tend to have smaller values anyways)
• Many high frequency values will be 0
• jpeg standard specifies the quantization table
• Encode
• i.e. with Huffman coding
• Can decode with inverse DCT
• Spatial dimension of color channels are reduced by 2

## Filters #

• Smoothing filters
• Gaussian: remove “high-frequency” components; “low-pass” filter
• Values are non-negative and sum to 1
• Constant regions are not affected by the filter
• Weighted average of neighboring pixels
• Derivative filters
• Derivatives of Gaussian
• Values can be negative and sum to 0
• No response in constant regions
• High absolute value at points of high contrast

### Application: template matching #

• Find some object (sub-image) in a larger image
• What is a good similarity or distance measure between two patches?
• Correlation
• $h[m,n] = \sum_{i,j} g[i,j]f[m+i,n+j]$
• $f$ is the image, $g$ is the template
• Results in smoothing filter
• Zero-mean correlation
• $h[m,n] \sum_{i,j} (g[i,j] - \bar g)f([m+i,n+j])$
• Even if we subtract the mean, we have false-positives in brighter reasons
• Sum Square Difference
• $h[m,n] = \sum_{i,j} (g[i,j] - f[m+i,n+j])^2$
• Doesn’t scale well with varying brightness/intensity
• Normalized Cross Correlation
• $h[m,n] = \dfrac{\sum_{i,j}(g[i,j] - \bar g)(f[m+i,n+j] - \bar f_{m,n})}{\sqrt{\sum_{i,j} (g[i,j] - \bar g)^2 \sum_{i,j} (f[m+i, n+j] - \bar f_{m,n})}}$
• Slowest, invariant to local, invariant to local average intensity/contrast
• Denoising
• With gaussian, still preserves ‘salt and pepper’ noise
• Median filter
• Replace each pixel with median of its neighbors
• Robust to outliers