Image Processing
Introduction and Special Techniques
Harshit Srivastava
Department of Electrical and Electronics
Engineeri...
Introduction
This presentation is an overview of some of the ideas and techniques of image
processing.
 Image processing ...
Topics
Topics

19 March 2010

1. Image formation
2. Point processing and equalization
3. Colour correction
4. Image sampli...
Tom and Bolt
Tom

Bolt

Tom and Bolt will be subjects of some of the imagery in this introduction.

19 March 2010

HARSHIT...
rc
e
ou
ts
lig
h
ob
j

ec

t

lens

Image Formation
Image Formation

image

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

p...
Image Formation
Image Formation

projection
projection
through lens
through lens

19 March 2010

image of object
image of ...
Image Formation
Image Formation

projection onto
projection onto
discrete sensor
discrete sensor
array.
array.

19 March 2...
Image Formation
Image Formation

sensors register
sensors register
average colour.
average colour.

19 March 2010

HARSHIT...
Image Formation
Image Formation

continuous
continuous
colours, discrete
colours, discrete
locations.
locations.

19 March...
discrete colour output

Digital Image Formation: Quantization

continuous colours
continuous colours
mapped to a finite,
m...
Sampling and Quantization
pixel grid

real image

19 March 2010

sampled

quantized

HARSHIT SRIVASTAVA 2009-2010

sampled...
Digital Image

Colour images have 3 values
per pixel; monochrome images
have 1 value per pixel.

a grid of squares,
a grid...
Colour Processing
requires some
requires some
knowledge of
knowledge of
how we see
how we see
colors
colors

Eye’s Light S...
Colour Sensing / Colour Perception
These are approximations
These are approximations
of the responses to the
of the respon...
Colour Images
• Are constructed from three
intensity maps.
• Each intensity map is pro-jected
through a colour filter (e.g...
Colour
Colour
Images
Images
On a
On a
CRT
CRT

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

16
Point Processing
Point Processing

- gamma

- brightness

original

+ brightness

+ gamma

histogram mod

- contrast

orig...
Colour Balance
and Saturation
Uniform changes in
colour components result
in change of tint.
E.g., if all G pixel values a...
Resampling
nearest neighbor

nearest neighbor

8×

16×

bicubic interpolation

bicubic interpolation

(resizing)

19 March...
ROTATION
In geometry and linear
algebra, a rotation is a
transformation in a plane
or in space that describes
the motion o...
Motion Blur
regional

vertical

original
zoom
19 March 2010

rotational

HARSHIT SRIVASTAVA 2009-2010

21
Image Warping
 Image warping is an special type of
affect which changes the function of
an image…to next level..
In imag...
Noise Reduction

blurred image

colour noise

colour-only blur

Next level of image

blurred image
19 March 2010

colour n...
Morphology

Nonlinear Processing: Binary Reconstruction
• Used after opening to grow back pieces of the original image
tha...
Nonlinear Processing: Grayscale Reconstruction
original

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

reconstructed openin...
Image Compression
Original image is
Original image is
5244w xx4716h
5244w 4716h
@ 1200 ppi:
@ 1200 ppi:
127MBytes
127MByte...
19 March 2010

File size in bytes

JPEG quality level

Image Compression: JPEG

HARSHIT SRIVASTAVA 2009-2010

27
Image Compositing
• Combine parts from separate images to form a new image.
• It’s difficult to do well.
• Requires relati...
Image Compositing Example

This man in his home office. Needs a better shirt.
19 March 2010

HARSHIT SRIVASTAVA 2009-2010
...
Image Compositing Example

This shirt demands a monogram.
19 March 2010

HARSHIT SRIVASTAVA 2009-2010

30
Image Compositing Example

He needs some more color.
19 March 2010

HARSHIT SRIVASTAVA 2009-2010

31
Image Compositing Example

Nice. Now for the way he’d wear his hair if he had any.
19 March 2010

HARSHIT SRIVASTAVA 2009-...
Image Compositing Example

He can’t stay in the office like this.
19 March 2010

HARSHIT SRIVASTAVA 2009-2010

33
Image Compositing Example
Now the background has changed

Where’s a Daddy-O like this belong?
19 March 2010

HARSHIT SRIVA...
THANK YOU
I WOULD LIKE TO THANK PROF. RICHARD ALLEN PETER II

19 March 2010

HARSHIT SRIVASTAVA 2009-2010

35
Upcoming SlideShare
Loading in …5
×

Harshit ppt image processing

1,193 views

Published on

This presentation basically talks about the image processing techniques and terms of how image is perceived , this is explained in terms of imaginary characters to imagine in different ways

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,193
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
46
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide
  • The "green" and "red" cones are mostly packed into the fovea
    centralis. By population, about 64% of the cones are red-sensitive,
    about 32% green sensitive, and about 2% are blue sensitive. The
    "blue" cones have the highest sensitivity and are mostly found
    outside the fovea.
  • Harshit ppt image processing

    1. 1. Image Processing Introduction and Special Techniques Harshit Srivastava Department of Electrical and Electronics Engineering Fall Semester 2010 Roll No. 0705621028 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 1
    2. 2. Introduction This presentation is an overview of some of the ideas and techniques of image processing.  Image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image.  Image processing usually refers to digital image processing.  Digital image processing is the use of computer algorithms to perform image processing in digital images. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 2
    3. 3. Topics Topics 19 March 2010 1. Image formation 2. Point processing and equalization 3. Colour correction 4. Image sampling and warping 5. Noise reduction 6. Mathematical morphology 7. Image compression 8. Image compositing HARSHIT SRIVASTAVA 2009-2010 3
    4. 4. Tom and Bolt Tom Bolt Tom and Bolt will be subjects of some of the imagery in this introduction. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 4
    5. 5. rc e ou ts lig h ob j ec t lens Image Formation Image Formation image 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 plane 5
    6. 6. Image Formation Image Formation projection projection through lens through lens 19 March 2010 image of object image of object HARSHIT SRIVASTAVA 2009-2010 6
    7. 7. Image Formation Image Formation projection onto projection onto discrete sensor discrete sensor array. array. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 digital camera digital camera 7
    8. 8. Image Formation Image Formation sensors register sensors register average colour. average colour. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 sampled image sampled image 8
    9. 9. Image Formation Image Formation continuous continuous colours, discrete colours, discrete locations. locations. 19 March 2010 discrete realdiscrete realvalued image valued image HARSHIT SRIVASTAVA 2009-2010 9
    10. 10. discrete colour output Digital Image Formation: Quantization continuous colours continuous colours mapped to a finite, mapped to a finite, discrete set of colours. discrete set of colours. continuous colour input 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 10
    11. 11. Sampling and Quantization pixel grid real image 19 March 2010 sampled quantized HARSHIT SRIVASTAVA 2009-2010 sampled & quantized 11
    12. 12. Digital Image Colour images have 3 values per pixel; monochrome images have 1 value per pixel. a grid of squares, a grid of squares, each of which each of which contains a single contains a single colour colour each square is each square is called a pixel (for called a pixel (for picture element) picture element) 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 12
    13. 13. Colour Processing requires some requires some knowledge of knowledge of how we see how we see colors colors Eye’s Light Sensors cone density near fovea #(blue) << #(red) < #(green) 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 13
    14. 14. Colour Sensing / Colour Perception These are approximations These are approximations of the responses to the of the responses to the visible spectrum of the visible spectrum of the “red”, “green”, and “blue” “red”, “green”, and “blue” receptors of aa typical receptors of typical human eye. human eye. The eye has 3 types of The eye has 3 types of photoreceptors: photoreceptors: sensitive to red, green, or sensitive to red, green, or blue light, blue light, The The simultaneous simultaneous red ++blue red blue response causes response causes us to perceive aa us to perceive continuous continuous range of hues range of hues on aacircle. No on circle. No hue is greater hue is greater than or less than or less than any other than any other hue. hue. The brain transforms The brain transforms RGB into separate RGB into separate brightness and color brightness and color channels (e.g., ,LHS). channels (e.g. LHS). 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 14
    15. 15. Colour Images • Are constructed from three intensity maps. • Each intensity map is pro-jected through a colour filter (e.g., red, green, or blue, or cyan, magenta, or yellow) to create a monochrome image. • The intensity maps are overlaid to create a colour image. • Each pixel in a colour image is a three element vector. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 15
    16. 16. Colour Colour Images Images On a On a CRT CRT 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 16
    17. 17. Point Processing Point Processing - gamma - brightness original + brightness + gamma histogram mod - contrast original + contrast histogram EQ 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 17
    18. 18. Colour Balance and Saturation Uniform changes in colour components result in change of tint. E.g., if all G pixel values are multiplied by α > 1 then the image takes a green cast. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 18
    19. 19. Resampling nearest neighbor nearest neighbor 8× 16× bicubic interpolation bicubic interpolation (resizing) 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 19
    20. 20. ROTATION In geometry and linear algebra, a rotation is a transformation in a plane or in space that describes the motion of a rigid body around a fixed point MOTION BLUR lur motion b and Motion blur happens when an camera cannot distinguish these values 1. Egomotion 2. Tracking 3. Optical flow 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 20
    21. 21. Motion Blur regional vertical original zoom 19 March 2010 rotational HARSHIT SRIVASTAVA 2009-2010 21
    22. 22. Image Warping  Image warping is an special type of affect which changes the function of an image…to next level.. In image warping the dimension of every side is changed to get effect.. It is an special type of affect which changes the orignality of image. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 22
    23. 23. Noise Reduction blurred image colour noise colour-only blur Next level of image blurred image 19 March 2010 colour noise HARSHIT SRIVASTAVA 2009-2010 5x5 Wiener filter 23
    24. 24. Morphology Nonlinear Processing: Binary Reconstruction • Used after opening to grow back pieces of the original image that are connected to the opening. • Permits the removal of small regions that are disjoint from larger objects without distorting the small features of the large objects. original 19 March 2010 opened HARSHIT SRIVASTAVA 2009-2010 reconstructed 24
    25. 25. Nonlinear Processing: Grayscale Reconstruction original 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 reconstructed opening 25
    26. 26. Image Compression Original image is Original image is 5244w xx4716h 5244w 4716h @ 1200 ppi: @ 1200 ppi: 127MBytes 127MBytes Yoyogi Park, Tokyo, October 1999. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 26
    27. 27. 19 March 2010 File size in bytes JPEG quality level Image Compression: JPEG HARSHIT SRIVASTAVA 2009-2010 27
    28. 28. Image Compositing • Combine parts from separate images to form a new image. • It’s difficult to do well. • Requires relative positions, orientations, and scales to be correct. • Lighting of objects must be consistent within the separate images. • Brightness, contrast, colour balance, and saturation must match. • Noise colour, amplitude, and patterns must be seamless. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 28
    29. 29. Image Compositing Example This man in his home office. Needs a better shirt. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 29
    30. 30. Image Compositing Example This shirt demands a monogram. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 30
    31. 31. Image Compositing Example He needs some more color. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 31
    32. 32. Image Compositing Example Nice. Now for the way he’d wear his hair if he had any. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 32
    33. 33. Image Compositing Example He can’t stay in the office like this. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 33
    34. 34. Image Compositing Example Now the background has changed Where’s a Daddy-O like this belong? 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 34
    35. 35. THANK YOU I WOULD LIKE TO THANK PROF. RICHARD ALLEN PETER II 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 35

    ×