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# Basic ideas of Image Transforms are derived from those showed earlier - PowerPoint PPT Presentation

Basic ideas of Image Transforms are derived from those showed earlier. Image Transforms. Fast Fourier 2-D Discrete Fourier Transform Fast Cosine 2-D Discrete Cosine Transform Radon Transform Slant Walsh, Hadamard, Paley, Karczmarz Haar Chrestenson Reed-Muller.

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Basic ideas of Image Transforms are derived from those showed earlier

• Fast Fourier

• 2-D Discrete Fourier Transform

• Fast Cosine

• 2-D Discrete Cosine Transform

• Slant

• Haar

• Chrestenson

• Reed-Muller

Spatial FrequencyorFourier Transform

Fourier face in Fourier Transform Domain

Jean Baptiste Joseph Fourier

Image is function of x and y

A cos(x2i/N) B cos(y2j/M)

fx = u = i/N, fy = v =j/M

Lines in the figure correspond to real value 1

Now we need two cosinusoids for each point, one for x and one for y

Now we have waves in two directions and they have frequencies and amplitudes

Original image

Fourier Transform

image

transform

spectrum

… will be covered in a separate lecture on spectral approaches…..

<

image

..and its spectrum

Let g(u,v) be the kernel

Let h(u,v) be the image

G(k,l) = DFT[g(u,v)]

H(k,l) = DFT[h(u,v)]

Then

This is a very important result

where means multiplication

and means convolution.

This means that an image can be filtered in the Spatial Domain or the Frequency Domain.

Let g(u,v) be the kernel

Let h(u,v) be the image

G(k,l) = DFT[g(u,v)]

H(k,l) = DFT[h(u,v)]

Then

in spatial domain we can do multiplication

In frequency domain

Multiplication in spectral domain

Convolution in spatial domain

where means multiplication

and means convolution.

Image

u

Spectrum

Noise and its spectrum

Noise filtering

Image

v

u

Image x(u,v)

v

u

Spectrum log(X(k,l))

l

k

Image x(u,v)

v

u

Spectrum log(X(k,l))

l

k

Image of cow with noise

white noise spectrum

kernel spectrum (low pass filter)

red noise

red noise spectrum

Discrete Cosine Transform (DCT) complicated

• Used in JPEG and MPEG

• Another Frequency Transform, with Different Set of Basis Functions

Discrete Cosine Transform in Matlab complicated

trucks

Two-dimensional Discrete Cosine Transform

Two dimensional spectrum of tracks. Nearly all information in left top corner

absolute

“Statistical” Filters complicated

• Median Filter also eliminates noise

• preserves edges better than blurring

• Sorts values in a region and finds the median

• region size and shape

• how define the median for color values?

“Statistical” Filters Continued complicated

• Minimum Filter (Thinning)

• Maximum Filter (Growing)

• “Pixellate” Functions

Now we can do this quickly in spectral domain

thinning complicated

growing

• Thinning

• Growing

Pixellate Examples complicated

Original image

After pixellate

DCT used in compression and recognition complicated

1 2 3 4 5

1

2

3

4

5

Can be used for face recognition, tell my story from Japan.

Fringe Pattern

DCT Coefficients

DCT

(1,1)

(1,2)

(2,1)

(2,2)

.

.

.

Artificial

Neural

Network

Feature

Vector

Noise Removal complicated

Transforms for Noise Removal

Image with Noise Transform been removed

Image reconstructed as the noise has been removed

Image Segmentation Recall: Edge Detection complicated

-1

-1

-2

0

-1

1

f(x,y)

fe(x,y)

0

0

0

2

0

-2

0

2

1

1

1

-1

Now we do this in spectral domain!!

Image Moments complicated

2-D continuous function f(x,y), the moment of order (p+q) is:

Moments were found by convolutions

Central moment of order (p+q) is:

Image Moments (contd.) complicated

Normalized central moment of order (p+q) is:

convolutions are now done in spectral domain

A set of seven invariant moments can be derived from gpq

Now we do this in spectral domain!!

Image Textures complicated

Grass Sand Brick wall

Now we do texture analysis like this in spectral domain!!

The USC-SIPI Image Database

http://sipi.usc.edu/

Problems complicated

• There is a lot of Fourier and Cosine Transform software on the web, find one and apply it to remove some kind of noise from robot images from FAB building.

Sources complicated

• Howard Schultz, Umass

• Herculano De Biasi

• Shreekanth Mandayam

• ECE Department, Rowan University

• http://engineering.rowan.edu/~shreek/fall01/dip/

http://engineering.rowan.edu/~shreek/fall01/dip/lab4.html