Signal Processing COS 323

Size: px

Start display at page:

Download "Signal Processing COS 323"

Peregrine Abner Hubbard
5 years ago
Views:

1 Signal Processing COS 323

2 Digital Signals D: functions of space or time e.g., sound 2D: often functions of 2 spatial dimensions e.g. images 3D: functions of 3 spatial dimensions CAT, MRI scans or 2 space, time video

3 Digital Signal Processing. Understand analogues of filters 2. Understand nature of sampling

4 Filtering Consider a noisy D signal ft One basic operation: smooth the signal Output = new function ht Filter ft ht

5 Filtering Consider a noisy D signal ft One basic operation: smooth the signal Output = new function ht Linear Shift-Invariant Filters If you double input, double output If you shift input by t, shift output by t

6 Simplest smoothing filter Take average of nearby points: Filter

7 Convolution Output signal at each point = weighted average of local region of input signal Depends on input signal, pattern of weights Filter g = function containing weights for linear combination Basic operation = move filter to some position, add up f times g g: c Filter 0

8 Convolution f g f g = f t g t dt Try for yourself:

9 Convolution f is called signal and g is filter or kernel, but the operation is symmetric *for real functions But: usually desirable to leave a constant signal unchanged: choose g such that g t dt =

10 Filter Choices Simple filters: bo, triangle

11 Gaussian Filter Commonly used filter G 2σ = e σ 2π 2 2

12 2D Gaussian Filter

13 Eample: Smoothing Original image Smoothed with 2D Gaussian kernel

14 Consider magnitude of gradient st derivative Eample: Edge Detection

taking derivative Differentiation and convolution

15 Smoothed Derivative Derivative of noisy signal = more noisy Solution: smooth with a Gaussian before taking derivative Differentiation and convolution both linear operators: they commute d d f g = df d g = f dg d

16 Smoothed Derivative Result: good way of finding derivative = convolution with derivative of Gaussian

17 Results in D: Peak appears at edge

18 Smoothed Derivative in 2D What is derivative in 2D? Gradient: Gaussian is separable! Combine smoothing, differentiation: = y f f y f,, = =,,,,,, 2 y G G y f y G G y f y G G y f y G G y f y G y f, 2 y G G y G =

19 Smoothed Derivative in 2D = =,,,,,, 2 y G G y f y G G y f y G G y f y G G y f y G y f

20 Edge Detection using Derivative of Gaussian Original Image Smoothed Gradient Magnitude

21 Canny Edge Detector Smooth Find derivative Find maima Threshold

22 Canny Edge Detector Original Image Edges

23 Sampling

24 Sampled Signals Analog domain: Continuous signals Digital domain: Can t store continuous signal: instead store samples Usually evenly sampled: f 0 =f 0, f =f 0 +, f 2 =f 0 +2, f 3 =f 0 +3,

25 3 Hz sine wave

26 3Hz sine sampled at 5Hz

27 3Hz sine sampled at 5Hz

28 5Hz sine sampled at 27 Hz

29 5Hz sine sampled at 5Hz

30 5Hz sine sampled at 6Hz

31 5Hz sine sampled at 6Hz Aliasing!

32 Aliasing Need to sample at least twice per period to capture the signal unambiguously Nyquist s theorem: Highest allowed signal frequency is half the sampling frequency = Nyquist frequency E.g., CD quality audio is 44,00 Hz Highest frequency representable is 22,050 Hz Limit of human hearing: ~7kHz to 20kHz

33 Aliasing in D Frequencies above Nyquist get reflected back below Nyquist 0 f N

34 Aliasing strikes!

36 Preventing aliasing Use a sample rate high enough to capture frequencies of interest i.e., > twice the highest frequency of interest Apply a low-pass filter to remove frequencies above the Nyquist frequency, before sampling.

37 Discrete Convolution Integral becomes sum over samples f g = fi g i i Normalization condition is i g i =

38 Computing Discrete Convolutions f g = fi g for = to n for i = to m out = out + fi * g-i i i Does not handle boundaries! If f has n samples and g has m nonzero samples, straightforward computation takes time Onm OK for small filter kernels, bad for large ones

39 Fourier Analysis

40 Frequency Domain Any signal can be represented as a sum of sinusoids at discrete frequencies, each with a given magnitude and phase

43 Frequency content in audio Frequency related to pitch: A 440 : 440 Hz 880Hz: one octave above Frequency also related to timbre: Real sounds contain many frequencies Higher frequency content can make sounds brighter In speech, higher frequencies are related to vowels, consonants independent of spoken/sung pitch

44 Fourier Transform Transform applied to function to analyze a signal s frequency content Several versions: Continuous Time Discrete Time Aperiodic / unbounded time, continuous frequency Fourier Transform Discrete-time Fourier Transform DTFT Periodic or bounded time, discrete frequency Fourier Series Discrete Fourier Transform DFT FFT used here

45 Fourier Series f Periodic function f defined over [ π.. π ] = 2 a0 + a + n cos n bn sin n n= n= where a n = π π π f cos n d b n = π π π f sin n d

46 Applying Euler s Formula Euler s formula: e i = cos + isin Apply: f = 2 a0 + a + n cos n bn sin n n= n= becomes where f = c n = 2π n= π π c n e in f e in d

47 Fourier Transform [Continuous] Fourier transform: Fk = F f 2πik = f e d Discrete Fourier transform: F k = n = 0 2πi F is a function of frequency describes how much of each frequency f contains Fourier transform is invertible f e k n

48 The Convolution Theorem Fourier transform turns convolution into multiplication: F f * g = F f F g and vice versa: F f g = F f * F g

49 Fourier Transform and Convolution Useful application #: Use frequency space to understand effects of filters Eample: Fourier transform of a Gaussian is a Gaussian Thus: attenuates high frequencies Amplitude Amplitude = Amplitude Frequency Frequency Frequency

50 Fourier Transform and Convolution Bo function? In frequency space: sinc function sinc = sin / Not as good at attenuating high frequencies

51 Fourier Transform and Convolution Fourier transform of derivative: d d Blows up for high frequencies! f = 2 k f F πi F After Gaussian smoothing, doesn t blow up

52 Fourier Transform and Convolution Useful application #2: Efficient computation Fast Fourier Transform FFT takes time On log n Thus, convolution can be performed in time On log n + m log m Greatest efficiency gains for large filters

Filtering and Edge Detection

Filtering and Edge Detection Local Neighborhoods Hard to tell anything from a single pixel Example: you see a reddish pixel. Is this the object s color? Illumination? Noise? The next step in order of complexity