digital image processing lecture 3 image formation n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Digital Image Processing Lecture 3: Image Formation PowerPoint Presentation
Download Presentation
Digital Image Processing Lecture 3: Image Formation

Loading in 2 Seconds...

play fullscreen
1 / 30

Digital Image Processing Lecture 3: Image Formation - PowerPoint PPT Presentation


  • 208 Views
  • Uploaded on

Digital Image Processing Lecture 3: Image Formation. Courtesy. Brian Mac Namee Gonzalez and Woods. Contents. This lecture will cover: Image representation Image sensing and acquisition Sampling, quantisation and resolution.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

Digital Image Processing Lecture 3: Image Formation


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
    Presentation Transcript
    1. Digital Image ProcessingLecture 3: Image Formation

    2. University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 2 Courtesy Brian Mac Namee Gonzalez and Woods

    3. Contents • This lecture will cover: • Image representation • Image sensing and acquisition • Sampling, quantisation and resolution University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 3

    4. Electromagnetic spectrum Electromagnetic radiation (EM radiation or EMR) Form of energy emitted and absorbed by charged particles Exhibits wave-like behaviour Travels through space Has both electric and magnetic components Electromagnetic spectrum  Range of all possible frequencies of  electromagnetic radiation University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 4

    5. Contents University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 5

    6. Image Representation • A digital image is composed of M rows and N columns of pixels each storing a value • Pixel values are most often grey levels in the range 0-255 (black-white) • Images can easily be represented as matrices col f (row, col) row University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 6

    7. Image Acquisition Images are typically generated by illuminating a scene and absorbing the energy reflected by the objects in that scene University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 7

    8. Image Sensing • Incoming energy lands on a sensor material responsive to that type of energy and this generates a voltage • Collections of sensors are arranged to capture images Line of Image Sensors Imaging Sensor Array of Image Sensors University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 8

    9. Image Sampling And Quantisation • A digital sensor can only measure a limited number of samples at a discrete set of energy levels • Quantisation is the process of converting a continuous analogue signal into a digital representation of this signal University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 9

    10. Image Sampling And Quantisation University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 10

    11. Image Sampling And Quantisation University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 11

    12. Image Sampling And Quantisation University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 12

    13. Image Sampling And Quantisation University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 13

    14. Image Sampling And Quantisation 0 50 45 30 20 10 5 1 5 10 20 30 45 50 0 University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 14

    15. Image Sampling And Quantisation (cont…) • Remember that a digital image is always only an approximation of a real world scene University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 15

    16. Image Representation University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 16

    17. Image Representation University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 17

    18. X-Ray image formation

    19. Spatial Resolution • The spatial resolution of an image is determined by how sampling was carried out • Spatial resolution simply refers to the smallest details in an image University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 19

    20. Spatial Resolution (cont…) University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 20

    21. Intensity Level Resolution • Intensity level resolution refers to the number of intensity levels used to represent the image • The more intensity levels used, the finer the level of detail discernable in an image • Intensity level resolution is usually given in terms of the number of bits used to store each intensity level Number of Bits Number of Intensity Levels Examples 1 2 0, 1 2 4 00, 01, 10, 11 4 16 0000, 0101, 1111 8 256 00110011, 01010101 16 65,536 1010101010101010 University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 21

    22. Intensity Level Resolution (cont…) Quality of two images with same contents and same size? 4 grey levels (2 bpp) 2 grey levels (1 bpp) University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 22

    23. Intensity Level Resolution (cont…) Quality of two images with same contents and same size? 4 grey levels (2 bpp) 2 grey levels (1 bpp) University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 23

    24. Intensity Level Resolution (cont…) Quality of two images with same contents and same size? 4 grey levels (2 bpp) 2 grey levels (1 bpp) University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 24

    25. Intensity Level Resolution (cont…) 256 grey levels (8 bits per pixel) 32 grey levels (5 bpp) 128 grey levels (7 bpp) 64 grey levels (6 bpp) 16 grey levels (4 bpp) 8 grey levels (3 bpp) 4 grey levels (2 bpp) 2 grey levels (1 bpp) University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 25

    26. Saturation & Noise University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 26

    27. Resolution: How Much Is Enough? • The big question with resolution is always how much is enough? • This all depends on what is in the image and what you would like to do with it • Key questions include • Does the image look aesthetically pleasing? • Can you see what you need to see within the image? University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 27

    28. Resolution: How Much Is Enough? (cont…) • The picture on the right is fine for counting the number of cars, but not for reading the number plate University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 28

    29. Intensity Level Resolution (cont…) Low Detail Medium Detail High Detail University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 29

    30. Summary • We have looked at: • Image representation • Image sensing and acquisition • Sampling, quantisation and resolution University Of Malakand | Department of Computer Science | UoMIPS| Dr. Engr. Sami ur Rahman | 30