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A Seminar on Fundamentals of Image Processing By Alok K. Watve. Applications of image processing. Gamma ray imaging X-ray imaging Multimedia systems Satellite imagery Flaw detection and quality control And many more……. Fundamental Steps in digital image processing. Image acquisition

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Presentation Transcript
slide1

A Seminar on

Fundamentals of

Image Processing

By

Alok K. Watve

slide2

Applications of image processing

  • Gamma ray imaging
  • X-ray imaging
  • Multimedia systems
  • Satellite imagery
  • Flaw detection and quality control
  • And many more…….
slide3

Fundamental Steps in digital image processing

  • Image acquisition
  • Image enhancement(gray or color images)
  • Wavelet and multi-resolution processing
  • Compression
  • Morphological processing
  • Segmentation
  • Representation & description
  • Object recognition

Low level processing

Medium level processing

High level processing

slide4

Image enhancement in spatial domain

  • Binary images
  • Only two colors
  • Gray images
  • A range of colors(not more than 256) from black to white
  • Color images
  • Contain several colors(as many as 224)
slide5

Definitions

  • Image : A 2D function represented by
        • I = f(x,y)
        • where I = intensity of the point(x,y)
  • Foreground : Objects of interest in an image
  • Background : Everything that’s not in foreground
slide6

Definitions

  • Histogram : A graph of frequency(of an intensity) versus intensity. Frequency is expressed as count of pixels
  • freq(I) = # pixels with intensity I
  • Spatial resolution : Smallest discernible detail in the image. Depends on the sampling.
  • Gray-level resolution : smallest discernible change in the gray level change.
slide7

Basic gray level transformations

Image negatives

s = (L – 1) – r

Where,

s = output intensity*

r = input intensity*

(L – 1) = Maximum intensity*

*These notations will be used throughout the seminar

slide8

Basic gray level transformations

Original image Negative image

All images: courtesy : www.imageprocessingplace.com

slide9

Basic gray level transformations

Power law transformation

s = c. rγ

Here,

c is a constant

slide10

Basic gray level transformations

Original image Transformed image (c = 1, γ = 0.3)

slide11

Basic gray level transformations

Contrast stretching : increases dynamic range

L-1

S2 Output intensity S1

0, 0 L1 L2 L-1

Input intensity

slide12

Basic gray level transformations

Original image Image obtained by contrast stretching

slide16

Basic gray level transformations

Histogram equalization

Image of mars’ moon histogram

slide17

Basic gray level transformations

Histogram equalization transformation can be expressed as a monotonically increasing function with domain and range = [0, 1]**

Assuming the intensities are normalized in the range [0,1]

slide18

Basic gray level transformations

A low contrast image and its histogram

slide19

Basic gray level transformations

Result of histogram equalization

slide20

Filtering in spatial domain

  • Concept of frequency
  • Modeling filters using convolution in spatial domain
  • Implementing filters using masks
slide22

Filtering in spatial domain

w(-1,-1) w(-1, 0) w(-1, 1)

w(0,-1) w(0, 0) w(0, 1)

f(x-1,y-1) f(x-1, y) f(x-1, y+1)

w(1,-1) w(1, 0) w(1, 1)

f(x,y-1) f(x, y) f(x, y+1)

f(x+1,y-1) f(x+1, y) f(x+1, y+1)

slide23

Low pass filter

Averaging filter Weighted average filter

slide25

Median filter

A noisy image Filtered image

slide26

High pass filter

  • Computing gradients in spatial domain
  • Laplacian filter
  • Other masks (operators)
    • Roberts
    • Sobel
slide27

High pass filter

  • Designing high pass filters
  • Method 1
    • g(x, y) = f(x, y) + fhp(x, y)
  • Method 2
    • g(x, y) = f(x, y) – flp(x, y)
slide28

Laplacian operators

0 -1 0

-1 -1 -1

-1 5 -1

-1 9 -1

0 -1 0

-1 -1 -1

slide29

Sobel Operators

-1 -2 -1

-1 0 1

0 0 0

-2 0 2

1 2 1

-1 0 1

slide33

References

  • Digital image processing, second edition - R. C. Gonzalez, R. E. Woods
  • Fundamental of digital image processing – A. K. Jain
  • www.imageprocessingplace.com