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Lecture 5: Fourier and Pyramids
CS4670/5670: Computer Vision Kavita Bala Lecture 5: Fourier and Pyramids
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Announcements PA1-A will be out tonight, due in a week
Written part alone Coding part in pairs Monday: numPy lecture
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Fourier Transform Given a signal compute (co)sine waves that contribute to signal For each frequency k, contribution of that frequency to signal: X[k] W (angular frequency) = 2 pi f
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Fourier Transform Fourier transform stores the magnitude and phase at each frequency Magnitude encodes how much signal there is at a particular frequency Phase encodes spatial information (indirectly) For mathematical convenience, this is often notated in terms of real and complex numbers Amplitude: Phase:
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Derivation for Gaussian
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Mandrill © Kavita Bala, Computer Science, Cornell University
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Process Convert to frequency domain Multiply by low pass filter Sample
Eliminate high frequencies Sample Reconstruct
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Filtering Lost some detail in this process
But ensure that you eliminate objectionable high frequency artifacts Have to transform to frequency domain? Expensive Not necessary because of relationship between spatial and frequency domain
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The Convolution Theorem
The Fourier transform of the convolution of two functions is the product of their Fourier transforms Convolution in spatial domain is equivalent to multiplication in frequency domain!
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Don’t multiply in frequency domain
Just convolve in spatial domain
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Types of Filters Spatial Sine Gaussian Box Sinc Frequency Impulse
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Pre-Filtering Ideal is box/pulse in frequency space
Sinc in spatial domain But infinite spread
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Truncated sinc © Kavita Bala, Computer Science, Cornell University
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Gaussian Filter Gaussian is preferred because it is same in frequency and spatial domain Not perfect: attenuates
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Mean vs. Gaussian filtering
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Gaussian filter Removes “high-frequency” components from the image (low-pass filter) Convolution with self is another Gaussian Convolving twice with Gaussian kernel of width = convolving once with kernel of width * = Linear vs. quadratic in mask size Source: K. Grauman
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Gaussian filters = 1 pixel = 5 pixels = 10 pixels = 30 pixels
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Image Resampling
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Image Scaling This image is too big to fit on the screen. How can we generate a half-sized version? Source: S. Seitz
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Image sub-sampling 1/16 1/4 Throw away every other row and column to create a 1/2 size image - called image sub-sampling Source: S. Seitz
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Image sub-sampling 1/2 1/4 (2x zoom) 1/16 (4x zoom)
Why does this look so crufty? Source: S. Seitz
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Image sub-sampling Source: F. Durand
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Wagon-wheel effect (See Source: L. Zhang
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Aliasing Occurs when your sampling rate is not high enough to capture the amount of detail in your image Can give you the wrong signal/image—an alias To do sampling right, need to understand the structure of your signal/image Enter Monsieur Fourier… Source: L. Zhang
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Nyquist-Shannon Sampling Theorem
When sampling a signal at discrete intervals, the sampling frequency must be 2 fmax fmax = max frequency of the input signal This will allows to reconstruct the original perfectly from the sampled version v v v good bad
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Nyquist limit – 2D example
Good sampling Bad sampling
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Aliasing When downsampling by a factor of two How can we fix this?
Original image has frequencies that are too high How can we fix this?
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Gaussian pre-filtering
Solution: filter the image, then subsample Source: S. Seitz
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Subsampling with Gaussian pre-filtering
Solution: filter the image, then subsample Source: S. Seitz
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Compare with... 1/2 1/4 (2x zoom) 1/8 (4x zoom) Source: S. Seitz
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Gaussian pre-filtering
Solution: filter the image, then subsample F1 F2 … blur subsample blur subsample F0 H * F1 H *
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{ … F0 F0 H * Gaussian pyramid F1 F1 H * F2 blur subsample blur
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Gaussian pyramids [Burt and Adelson, 1983]
In computer graphics, a mip map [Williams, 1983] Gaussian Pyramids have all sorts of applications in computer vision Source: S. Seitz
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The Gaussian Pyramid Low resolution sub-sample blur sub-sample blur
High resolution
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Gaussian pyramid and stack
Source: Forsyth
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Memory Usage What is the size of the pyramid? 4/3
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The Laplacian Pyramid Gaussian Pyramid Laplacian Pyramid - = - = - =
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Laplacian pyramid Source: Forsyth
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A. Oliva, A. Torralba, P.G. Schyns, “Hybrid Images,” SIGGRAPH 2006
PA1 (A): Hybrid Images A. Oliva, A. Torralba, P.G. Schyns, “Hybrid Images,” SIGGRAPH 2006 Gaussian Filter Laplacian Filter Source: Hays
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Source: Hays Salvador Dali invented Hybrid Images? Salvador Dali
“Gala Contemplating the Mediterranean Sea, which at 30 meters becomes the portrait of Abraham Lincoln”, 1976 Source: Hays
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Source: Hays Salvador Dali invented Hybrid Images? Salvador Dali
“Gala Contemplating the Mediterranean Sea, which at 30 meters becomes the portrait of Abraham Lincoln”, 1976 Source: Hays
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Major uses of image pyramids
Compression Object detection Scale search Features Registration Course-to-fine
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