Download presentation

Presentation is loading. Please wait.

Published byKayli Cornfield Modified over 3 years ago

1
Linear Filtering – Part II Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr

2
CS 484, Fall 2012©2012, Selim Aksoy2 Fourier theory Jean Baptiste Joseph Fourier had a crazy idea: Any periodic function can be written as a weighted sum of sines and cosines of different frequencies (1807). Fourier series Even functions that are not periodic (but whose area under the curve is finite) can be expressed as the integral of sines and cosines multiplied by a weighing function. Fourier transform

3
CS 484, Fall 2012©2012, Selim Aksoy3 Fourier theory The Fourier theory shows how most real functions can be represented in terms of a basis of sinusoids. The building block: A sin( ωx + Φ ) Add enough of them to get any signal you want. Adapted from Alexei Efros, CMU

4
CS 484, Fall 2012©2012, Selim Aksoy4 Fourier transform

5
CS 484, Fall 2012©2012, Selim Aksoy5 Fourier transform

6
CS 484, Fall 2012©2012, Selim Aksoy6 Fourier transform

7
CS 484, Fall 2012©2012, Selim Aksoy7 Fourier transform

8
CS 484, Fall 2012©2012, Selim Aksoy8 Fourier transform

9
CS 484, Fall 2012©2012, Selim Aksoy9 Fourier transform

10
CS 484, Fall 2012©2012, Selim Aksoy10 To get some sense of what basis elements look like, we plot a basis element --- or rather, its real part --- as a function of x,y for some fixed u, v. We get a function that is constant when (ux+vy) is constant. The magnitude of the vector (u, v) gives a frequency, and its direction gives an orientation. The function is a sinusoid with this frequency along the direction, and constant perpendicular to the direction. u v Adapted from Antonio Torralba

11
Fourier transform CS 484, Fall 2012©2012, Selim Aksoy11 Adapted from Antonio Torralba Here u and v are larger than in the previous slide. u v

12
Fourier transform CS 484, Fall 2012©2012, Selim Aksoy12 Adapted from Antonio Torralba And larger still... u v

13
CS 484, Fall 2012©2012, Selim Aksoy13 Fourier transform Adapted from Alexei Efros, CMU

14
CS 484, Fall 2012©2012, Selim Aksoy14 Fourier transform Adapted from Gonzales and Woods

15
CS 484, Fall 2012©2012, Selim Aksoy15 Fourier transform Adapted from Gonzales and Woods

16
CS 484, Fall 2012©2012, Selim Aksoy16 Fourier transform

17
CS 484, Fall 2012©2012, Selim Aksoy17 Fourier transform

18
CS 484, Fall 2012©2012, Selim Aksoy18 Adapted from Antonio Torralba Horizontal orientation Vertical orientation 45 deg. 0f max 0 fx in cycles/image Low spatial frequencies High spatial frequencies Log power spectrum How to interpret a Fourier spectrum:

19
Fourier transform CS 484, Fall 2012©2012, Selim Aksoy19 Adapted from Antonio Torralba AB C 12 3

20
CS 484, Fall 2012©2012, Selim Aksoy20 Fourier transform Adapted from Shapiro and Stockman

21
CS 484, Fall 2012©2012, Selim Aksoy21 Fourier transform Example building patterns in a satellite image and their Fourier spectrum.

22
CS 484, Fall 2012©2012, Selim Aksoy22 Convolution theorem

23
CS 484, Fall 2012©2012, Selim Aksoy23 Frequency domain filtering Adapted from Shapiro and Stockman, and Gonzales and Woods

24
CS 484, Fall 2012©2012, Selim Aksoy24 Frequency domain filtering Since the discrete Fourier transform is periodic, padding is needed in the implementation to avoid aliasing (see section 4.6 in the Gonzales-Woods book for implementation details).

25
CS 484, Fall 2012©2012, Selim Aksoy25 Frequency domain filtering f(x,y) h(x,y) g(x,y) |F(u,v)| |H(u,v)| |G(u,v)| Adapted from Alexei Efros, CMU

26
CS 484, Fall 2012©2012, Selim Aksoy26 Smoothing frequency domain filters

27
CS 484, Fall 2012©2012, Selim Aksoy27 Smoothing frequency domain filters The blurring and ringing caused by the ideal low- pass filter can be explained using the convolution theorem where the spatial representation of a filter is given below.

28
CS 484, Fall 2012©2012, Selim Aksoy28 Sharpening frequency domain filters

29
CS 484, Fall 2012©2012, Selim Aksoy29 Sharpening frequency domain filters Adapted from Gonzales and Woods

30
CS 484, Fall 2012©2012, Selim Aksoy30 Sharpening frequency domain filters Adapted from Gonzales and Woods

31
CS 484, Fall 2012©2012, Selim Aksoy31 Template matching Correlation can also be used for matching. If we want to determine whether an image f contains a particular object, we let h be that object (also called a template) and compute the correlation between f and h. If there is a match, the correlation will be maximum at the location where h finds a correspondence in f. Preprocessing such as scaling and alignment is necessary in most practical applications.

32
CS 484, Fall 2012©2012, Selim Aksoy32 Template matching Adapted from Gonzales and Woods

33
CS 484, Fall 2012©2012, Selim Aksoy33 Template matching Face detection using template matching: face templates.

34
CS 484, Fall 2012©2012, Selim Aksoy34 Template matching Face detection using template matching: detected faces.

35
CS 484, Fall 2012©2012, Selim Aksoy35 Resizing images How can we generate a half-sized version of a large image? Adapted from Steve Seitz, U of Washington

36
CS 484, Fall 2012©2012, Selim Aksoy36 Resizing images Throw away every other row and column to create a 1/2 size image (also called sub-sampling). 1/4 1/8 Adapted from Steve Seitz, U of Washington

37
CS 484, Fall 2012©2012, Selim Aksoy37 Resizing images Does this look nice? 1/4 (2x zoom)1/8 (4x zoom)1/2 Adapted from Steve Seitz, U of Washington

38
CS 484, Fall 2012©2012, Selim Aksoy38 Resizing images We cannot shrink an image by simply taking every k’th pixel. Solution: smooth the image, then sub-sample. Gaussian 1/4 Gaussian 1/8 Gaussian 1/2 Adapted from Steve Seitz, U of Washington

39
CS 484, Fall 2012©2012, Selim Aksoy39 Resizing images Gaussian 1/4 (2x zoom) Gaussian 1/8 (4x zoom) Gaussian 1/2 Adapted from Steve Seitz, U of Washington

40
CS 484, Fall 2012©2012, Selim Aksoy40 Sampling and aliasing Adapted from Steve Seitz, U of Washington

41
CS 484, Fall 2012©2012, Selim Aksoy41 Sampling and aliasing Errors appear if we do not sample properly. Common phenomenon: High spatial frequency components of the image appear as low spatial frequency components. Examples: Wagon wheels rolling the wrong way in movies. Checkerboards misrepresented in ray tracing. Striped shirts look funny on color television.

42
CS 484, Fall 2012©2012, Selim Aksoy42 Gaussian pyramids Adapted from Gonzales and Woods

43
CS 484, Fall 2012©2012, Selim Aksoy43 Gaussian pyramids Adapted from Michael Black, Brown University

44
CS 484, Fall 2012©2012, Selim Aksoy44 Gaussian pyramids Adapted from Michael Black, Brown University

Similar presentations

Presentation is loading. Please wait....

OK

Computer Vision University of Illinois Derek Hoiem

Computer Vision University of Illinois Derek Hoiem

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on bio and non biodegradable waste Ppt on disk formatting pdf Ppt on adjectives for class 2 Download ppt on teachers day in india Ppt on river ganga pollution Viewer ppt online shopping Ppt on point contact diode application Ppt on types of production process Ppt on pi in maths what does mode Ppt on latest technology in electronics and communication free download