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Image processing ppt
Presented & performed by




                           Arunachalam. PL
                             Nagaraj.K.N
                      COMPUTER ENGINEERING
• INTRODUCTION
• ACQUIRING IMAGES
  – HUMAN RELIANCE ON IMAGES FOR
    INFORMATION
  – ELECTRONICS AND BANDWIDTH
    LIMITATIONS
  – HIGH RESOLUTION IMAGING
  – COLOR IMAGING
  – COLOR SPACES
  – COLOR DISPLAYS
  – IMAGE TYPES
• ITS TIME FOR DEMO
• CONCLUSION
• BIBLIOGRAPHY
Image processing involves processing or
altering an existing image in a desired
manner.

The next step is obtaining an image in a
readable format.

The Internet and other sources provide
countless images in standard formats.
Image processing are of two aspects..

improving the visual appearance         of
images to a human viewer

preparing images for measurement of
the features and structures present.
Since the digital image is “invisible” it must be
prepared for viewing on one or more output device
(laser printer,monitor,etc)

The digital image can be optimized for the application
by enhancing or altering the appearance of structures
within it (based on: body part, diagnostic task,
viewing preferences,etc)

It might be possible to analyze the image in the
computer and provide cues to the radiologists to help
detect   important/suspicious    structures    (e.g.:
Computed Aided Diagnosis, CAD)
Scientific instruments commonly produce
images to communicate results to the
operator, rather than generating an audible
tone or emitting a smell.

Space missions to other planets and Comet
Halley always include cameras as major
components, and we judge the success of those
missions by the quality of the images returned.
Image-to-image transformations

Image-to-information transformations

Information-to-image transformations
Enhancement (make image more useful, pleasing)

Restoration
   Egg. deblurring ,grid line removal

Geometry
   (scaling, sizing , Zooming, Morphing one object
   to another).
Image statistics (histograms)
   Histogram is the fundamental tool for analysis and
   image processing

Image compression

Image analysis (image segmentation, feature
extraction, pattern recognition)

computer-aided detection and diagnosis (CAD)
Decompression of compressed image data.

Reconstruction of image slices from CT or MRI
raw data.

Computer graphics, animations and virtual reality
(synthetic objects).
The process of obtaining an high resolution (HR)
image or a sequence of HR images from a set of
low resolution (LR) observations.

HR techniques are being applied to a variety of
fields, such as obtaining

   improved still images
   high definition television,
   high performance color liquid crystal display (LCD)
   screens,
   video surveillance,
   remote sensing, and
   medical imaging.
Conversion from RGB (the brightness of the individual
red, green, and blue signals at defined wavelengths) to
YIQ/YUV and to the other color encoding schemes is
straightforward and loses no information.

 Y, the “luminance” signal, is just the brightness of a
panchromatic monochrome image that would be displayed
by a black-and-white television receiver
• Most computers use color monitors that have
  much higher resolution than a television set but
  operate on essentially the same principle.

• Smaller phosphor dots, a higher frequency
  scan, and a single progressive scan (rather than
  interlace) produce much greater sharpness and
  color purity.
Digital processing requires images to be obtained in the
form of electrical signals. These signals can be digitized into
sequences of numbers which then can be processed by a
computer. There are many ways to convert images into
digital numbers. Here, we will focus on video technology, as
it is the most common and affordable approach.
• Multiple images may constitute a series of views of
  the same area, using different wavelengths of light
  or other signals.

• Examples include the images produced by
  satellites, such as
    – the various visible and infrared wavelengths recorded
      by the Landsat Thematic Mapper(TM), and
    – images from the Scanning Electron Microscope
      (SEM) in which as many as a dozen different
      elements may be represented by their X-ray
      intensities.

• These images may each require processing.
A general-purpose computer to be useful for image
processing, four key demands must be met: high-
resolution image display, sufficient memory transfer
bandwidth, sufficient storage space, and sufficient
computing power.
A 32-bit computer can address
up to 4GB of memory(RAM).
• Adobe Photoshop
• Corel Draw
• Serif Photoplus
Image processing ppt
In electrical engineering and computer science, 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.
Most image-processing techniques involve treating the
image as a two-dimensional signal and applying standard
signal-processing techniques to it.
Create
     Apply
      Apply                     Knowledge
    Knowledge
    Knowledge
                 Seminar -
                Knowledge
                  Sharing
Assimilate
Assimilate          And            Structure
                                    Structure
Knowledge
Knowledge        Acquiring         Knowledge
                                   Knowledge
                Environment

                  Disseminate
                  Disseminate
                  Knowledge
                   Knowledge
This Paper has been submitted under the guidance of




 K. Megala B.E               – Lecturer (Computer Engg).
 M.Saravanan (M.E) – Lecturer (Computer Engg).
                       Over headed by
     Mr.M. Ramesh Kumar, MCA.,Mphil
                  (Computer Engg).
“Things that think…
                      don’t make sense unless they link.”




                                  Thank You
BIBLIOGRAPHY



 John C. Ross. Image Processing Hand book, CRC Press. 1994.
 [2] Peter Mc Curry, Fearghal Morgan, Liam Kilmartin. Xilinx FPGA
 implementation of a pixel processor for object detection applications. In the
 Proc. Irish Signals and Systems Conference, Volume 3, Page(s):346 – 349,
 Oct. 2001.
 [3] M. Moore. A DSP-based real time image processing system. In the
 Proceedings of the 6th International conference on signal processing
 applications and technology, Boston MA, August 1995.


Simplicity is the key to Victory.
Bruce Lee
Image processing ppt

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Image processing ppt

  • 2. Presented & performed by Arunachalam. PL Nagaraj.K.N COMPUTER ENGINEERING
  • 3. • INTRODUCTION • ACQUIRING IMAGES – HUMAN RELIANCE ON IMAGES FOR INFORMATION – ELECTRONICS AND BANDWIDTH LIMITATIONS – HIGH RESOLUTION IMAGING – COLOR IMAGING – COLOR SPACES – COLOR DISPLAYS – IMAGE TYPES • ITS TIME FOR DEMO • CONCLUSION • BIBLIOGRAPHY
  • 4. Image processing involves processing or altering an existing image in a desired manner. The next step is obtaining an image in a readable format. The Internet and other sources provide countless images in standard formats.
  • 5. Image processing are of two aspects.. improving the visual appearance of images to a human viewer preparing images for measurement of the features and structures present.
  • 6. Since the digital image is “invisible” it must be prepared for viewing on one or more output device (laser printer,monitor,etc) The digital image can be optimized for the application by enhancing or altering the appearance of structures within it (based on: body part, diagnostic task, viewing preferences,etc) It might be possible to analyze the image in the computer and provide cues to the radiologists to help detect important/suspicious structures (e.g.: Computed Aided Diagnosis, CAD)
  • 7. Scientific instruments commonly produce images to communicate results to the operator, rather than generating an audible tone or emitting a smell. Space missions to other planets and Comet Halley always include cameras as major components, and we judge the success of those missions by the quality of the images returned.
  • 9. Enhancement (make image more useful, pleasing) Restoration Egg. deblurring ,grid line removal Geometry (scaling, sizing , Zooming, Morphing one object to another).
  • 10. Image statistics (histograms) Histogram is the fundamental tool for analysis and image processing Image compression Image analysis (image segmentation, feature extraction, pattern recognition) computer-aided detection and diagnosis (CAD)
  • 11. Decompression of compressed image data. Reconstruction of image slices from CT or MRI raw data. Computer graphics, animations and virtual reality (synthetic objects).
  • 12. The process of obtaining an high resolution (HR) image or a sequence of HR images from a set of low resolution (LR) observations. HR techniques are being applied to a variety of fields, such as obtaining improved still images high definition television, high performance color liquid crystal display (LCD) screens, video surveillance, remote sensing, and medical imaging.
  • 13. Conversion from RGB (the brightness of the individual red, green, and blue signals at defined wavelengths) to YIQ/YUV and to the other color encoding schemes is straightforward and loses no information. Y, the “luminance” signal, is just the brightness of a panchromatic monochrome image that would be displayed by a black-and-white television receiver
  • 14. • Most computers use color monitors that have much higher resolution than a television set but operate on essentially the same principle. • Smaller phosphor dots, a higher frequency scan, and a single progressive scan (rather than interlace) produce much greater sharpness and color purity.
  • 15. Digital processing requires images to be obtained in the form of electrical signals. These signals can be digitized into sequences of numbers which then can be processed by a computer. There are many ways to convert images into digital numbers. Here, we will focus on video technology, as it is the most common and affordable approach.
  • 16. • Multiple images may constitute a series of views of the same area, using different wavelengths of light or other signals. • Examples include the images produced by satellites, such as – the various visible and infrared wavelengths recorded by the Landsat Thematic Mapper(TM), and – images from the Scanning Electron Microscope (SEM) in which as many as a dozen different elements may be represented by their X-ray intensities. • These images may each require processing.
  • 17. A general-purpose computer to be useful for image processing, four key demands must be met: high- resolution image display, sufficient memory transfer bandwidth, sufficient storage space, and sufficient computing power. A 32-bit computer can address up to 4GB of memory(RAM).
  • 18. • Adobe Photoshop • Corel Draw • Serif Photoplus
  • 20. In electrical engineering and computer science, 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. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.
  • 21. Create Apply Apply Knowledge Knowledge Knowledge Seminar - Knowledge Sharing Assimilate Assimilate And Structure Structure Knowledge Knowledge Acquiring Knowledge Knowledge Environment Disseminate Disseminate Knowledge Knowledge
  • 22. This Paper has been submitted under the guidance of K. Megala B.E – Lecturer (Computer Engg). M.Saravanan (M.E) – Lecturer (Computer Engg). Over headed by Mr.M. Ramesh Kumar, MCA.,Mphil (Computer Engg).
  • 23. “Things that think… don’t make sense unless they link.” Thank You
  • 24. BIBLIOGRAPHY John C. Ross. Image Processing Hand book, CRC Press. 1994. [2] Peter Mc Curry, Fearghal Morgan, Liam Kilmartin. Xilinx FPGA implementation of a pixel processor for object detection applications. In the Proc. Irish Signals and Systems Conference, Volume 3, Page(s):346 – 349, Oct. 2001. [3] M. Moore. A DSP-based real time image processing system. In the Proceedings of the 6th International conference on signal processing applications and technology, Boston MA, August 1995. Simplicity is the key to Victory. Bruce Lee