• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Digital image fundamentals
 

Digital image fundamentals

on

  • 1,805 views

This slide show gives a fundamental knowledge about dip concepts.

This slide show gives a fundamental knowledge about dip concepts.

Statistics

Views

Total Views
1,805
Views on SlideShare
1,802
Embed Views
3

Actions

Likes
0
Downloads
45
Comments
0

1 Embed 3

http://www.docseek.net 3

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

    Digital image fundamentals Digital image fundamentals Presentation Transcript

    • byParesh Kamble
    • Elements of Visual PerceptionDigital Image Processing is built on foundation of Mathematical and Probabilistic formulations Human Intuition & analysis plays a key role in choosing ne technique over other. Study of Human vision system is thus important.
    • Structure of Human Eye
    • Rod & Cone Cells
    • Distribution of Rod & Cone cells
    • Image sensing and AcqusitionThree principal sensor arrangement:1) Image acquisition using a single sensor
    • Image sensing and AcqusitionImage Acquisition using a single sensor:
    • Image sensing and Acqusition2) Image Acquisition using sensor strips:
    • Image sensing and Acqusition3) Image Acquisition using sensor arrays:
    • Image sensing and Acqusition
    • Image sensing and Acqusition
    • Image sensing and AcqusitionSimple Image formation model:o Images are denoted by 2-dimensional function of the form f( x, y).o f at spatial co-ordinates ( x, y) is positive, finite scalar quantity determinedby source of the image. 0 < f( x, y) < ∞o f( x, y) is characterized by two components: o illumination: amount of source illumination incident on scene. denoted by i( x, y) o reflectance : amount of illumination reflected by the objects in scene. denoted by r( x, y) Thus, f(x,y) = i(x,y) r(x,y) where, 0 < i(x,y) < ∞ …………….(1) & 0(total absorption)< r( x, y) < 1(total reflectance) …………….(2)
    • Image sensing and AcqusitionSimple Image formation model:o Let the gray level (intensity) of a monochromatic image at any co-ordinate be denoted by: l = f(x0, y0); From equation (1) & (2) we get Lmin ≤ l ≤ Lmax where, Lmin should be positive & Lmax be finite.We have, Lmin = imin rmin & Lmax = imax rmaxThe interval [Lmin, Lmax] is called the gray (intensity) scale.We shift this interval to interval [0, L-1], where, l = 0 is black & l = L-1 is white on grey scale.
    • Image Sampling & Quantization
    • Image Sampling & Quantization
    • Image Sampling & Quantization
    • Image Sampling & QuantizationSampling: Digitizing the coordinate value.Quantization: Digitizing the amplitude value.
    • Image Sampling & QuantizationSpatial & Intensity ResolutionSpacial Resolution:dpi : (dots per inch)Newspapers : 75 dpiMagazines : 133 dpiGlossy brochures : 175 dpiBooks : 2400dpi
    • Image Sampling & QuantizationIntensity resolution: smallest discernible change in intensity level.Image interpolation: process of using known data to estimate the values at unknown locations. Nearest neighbor interpolation: assigns intensity of nearest neighbor in original image. Bilinear interpolation: uses 4 nearest neighbors. Bicubic interpolation: uses 16 nearest neighbors.
    • High Definition (HD)Video Frame size in Pixels per Scanning Frame rate (Hz)mode pixels (W×H) image type720p 1,280x720 921,600 Progressive 23.976, 24, 25, 29.97, 30, 50, 59.94, 60, 721080i 1,920x1,080 1,036,800 Interlaced 25 (50 fields/s), 29.97 (59.94 fields/s), 30 (60 fields/s)1080p 1,920x1,080 2,073,600 Progressive 23.976, 24, 25, 29.97, 30, 50, 59.94, 60
    • High Definition (HD)