Harshit ppt image processing
Upcoming SlideShare
Loading in...5
×
 

Harshit ppt image processing

on

  • 630 views

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

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

Statistics

Views

Total Views
630
Views on SlideShare
630
Embed Views
0

Actions

Likes
0
Downloads
16
Comments
0

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • The "green" and "red" cones are mostly packed into the fovea <br /> centralis. By population, about 64% of the cones are red-sensitive, <br /> about 32% green sensitive, and about 2% are blue sensitive. The <br /> "blue" cones have the highest sensitivity and are mostly found <br /> outside the fovea. <br />

Harshit ppt image processing Harshit ppt image processing Presentation Transcript

  • 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
  • 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
  • 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
  • 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
  • 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
  • Image Formation Image Formation projection projection through lens through lens 19 March 2010 image of object image of object HARSHIT SRIVASTAVA 2009-2010 6
  • 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
  • Image Formation Image Formation sensors register sensors register average colour. average colour. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 sampled image sampled image 8
  • 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
  • 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
  • Sampling and Quantization pixel grid real image 19 March 2010 sampled quantized HARSHIT SRIVASTAVA 2009-2010 sampled & quantized 11
  • 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
  • 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
  • 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
  • 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
  • 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 original + contrast histogram EQ 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 17
  • 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
  • Resampling nearest neighbor nearest neighbor 8× 16× bicubic interpolation bicubic interpolation (resizing) 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 19
  • 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
  • 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 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
  • 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
  • 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
  • Nonlinear Processing: Grayscale Reconstruction original 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 reconstructed opening 25
  • 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
  • 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 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
  • Image Compositing Example This man in his home office. Needs a better shirt. 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 29
  • 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 2009-2010 32
  • 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 SRIVASTAVA 2009-2010 34
  • THANK YOU I WOULD LIKE TO THANK PROF. RICHARD ALLEN PETER II 19 March 2010 HARSHIT SRIVASTAVA 2009-2010 35