The Frequency Domain without tears Somewhere in Cinque
- Slides: 46
The Frequency Domain, without tears Somewhere in Cinque Terre, May 2005 CS 194: Intro to Computer Vision and Comp. Photo Many slides borrowed Alexei Efros, UC Berkeley, Fall 2020 from Steve Seitz
Salvador Dali “Gala Contemplating the Mediterranean Sea, which at 30 meters becomes the portrait of Abraham Lincoln”, 1976
A nice set of basis Teases away fast vs. slow changes in the image. This change of basis has a special name…
Jean Baptiste Joseph Fourier (1768 -1830). . . the manner in which the author arrives at these had crazy idea (1807): equations is not exempt of difficulties and. . . his analysis Any univariate function can be rewritten as a weighted sum of sines and cosines of different frequencies. to integrate them still leaves something to be desired on the score of generality and even rigour. Don’t believe it? • Neither did Lagrange, Laplace, Poisson and other big wigs • Not translated into English until 1878! Laplace But it’s (mostly) true! • called Fourier Series Lagrange Legendre
A sum of sines Our building block: Add enough of them to get any signal f(x) you want! How many degrees of freedom? What does each control? Which one encodes the coarse vs. fine structure of the signal?
Fourier Transform We want to understand the frequency w of our signal. So, let’s reparametrize the signal by w instead of x: f(x) Fourier Transform F(w) For every w from 0 to inf, F(w) holds the amplitude A and phase f of the corresponding sine • How can F hold both? We can always go back: F(w) Inverse Fourier Transform f(x)
Time and Frequency example : g(t) = sin(2 pf t) + (1/3)sin(2 p(3 f) t)
Time and Frequency example : g(t) = sin(2 pf t) + (1/3)sin(2 p(3 f) t) = +
Frequency Spectra example : g(t) = sin(2 pf t) + (1/3)sin(2 p(3 f) t) = +
Frequency Spectra Usually, frequency is more interesting than the phase
Frequency Spectra = = +
Frequency Spectra = = +
Frequency Spectra = = +
Frequency Spectra = = +
Frequency Spectra = = +
Frequency Spectra =
Frequency Spectra
FT: Just a change of basis M * f(x) = F(w) * . . . =
IFT: Just a change of basis M-1 * F(w) = f(x) * . . . =
Finally: Scary Math
Finally: Scary Math …not really scary: is hiding our old friend: phase can be encoded by sin/cos pair So it’s just our signal f(x) times sine at frequency w
Extension to 2 D = Image as a sum of basis images
Extension to 2 D in Matlab, check out: imagesc(log(abs(fftshift(fft 2(im)))));
Fourier analysis in images Intensity Image Fourier Image http: //sharp. bu. edu/~slehar/fourier. html#filtering
Signals can be composed + = http: //sharp. bu. edu/~slehar/fourier. html#filtering More: http: //www. cs. unm. edu/~brayer/vision/fourier. html
Man-made Scene
Can change spectrum, then reconstruct Local change in one domain, courses global change in the other
Low and High Pass filtering
The Convolution Theorem The greatest thing since sliced (banana) bread! • The Fourier transform of the convolution of two functions is the product of their Fourier transforms • The inverse Fourier transform of the product of two Fourier transforms is the convolution of the two inverse Fourier transforms • Convolution in spatial domain is equivalent to multiplication in frequency domain!
2 D convolution theorem example |F(sx, sy)| f(x, y) * h(x, y) |H(sx, sy)| g(x, y) |G(sx, sy)|
Filtering Why does the Gaussian give a nice smooth image, but the square filter give edgy artifacts? Gaussian Box filter
Fourier Transform pairs
Gaussian
Box Filter
Low-pass, Band-pass, High-pass filters low-pass: High-pass / band-pass:
Edges in images
What does blurring take away? original
What does blurring take away? smoothed (5 x 5 Gaussian)
High-Pass filter smoothed – original
Image “Sharpening” What does blurring take away? – = detail smoothed (5 x 5) original Let’s add it back: +α original = detail sharpened
Unsharp mask filter image unit impulse (identity) blurred image Gaussian Laplacian of Gaussian
application: Hybrid Images Aude Oliva & Antonio Torralba & Philippe G Schyns, SIGGRAPH 2006
Application: Hybrid Images Gaussian Filter A. Oliva, A. Torralba, P. G. Schyns, “Hybrid Images, ” SIGGRAPH 2006 Laplacian Filter unit impulse Gaussian Laplacian of Gaussian
Yestaryear’s homework (CS 194 -26: Riyaz Faizullabhoy) Prof. Jitendros Papadimalik
- Tears, idle tears
- Tears idle tears 해석
- Tears idle tears 해석
- Tears idle tears
- Tears idle tears themes
- No tears in the writer no tears in the reader
- Table of z transforms
- Z domain to frequency domain
- Data domain fundamentals
- Roc in z transform
- Tiers without tears
- Handwriting without tears occupational therapy
- Mat man occupational therapy
- Cinque vie firenze
- I regni dei viventi
- Czanne
- Xxvi canto inferno parafrasi
- Il cinque maggio analisi
- Cinquitere
- Osservo l'acqua con i cinque sensi
- I cinque regni degli esseri viventi
- Cinque tesori da scoprire testo
- Che cos'è la metacomunicazione
- Tre fari sono visibili dal lungomare
- Cinque sola
- Joint frequency
- How is linear frequency related to angular frequency?
- How to find conditional relative frequency
- Relative frequency bar chart
- How to calculate relative frequency
- Marginal frequency table
- Form factor and crest factor
- The furthest distance ive travelled poem
- Somewhere somebody something
- Till we meet again vera lynn
- Somewhere over the rainbow judy garland
- Something anything nothing etc
- Wpa rainbow tables
- Somewhere i have never travelled gladly beyond analysis
- Maksim mrvica exodus
- But we gotta start somewhere
- Somewhere deep in the woods
- There's always a bull market somewhere
- If u want to go fast go alone
- Harold arlen somewhere over the rainbow
- High pass filter radiology
- Circular convolution meaning