1. Digital Image Processing (DIP)
Dr. Abdul Basit Siddiqui
Assistant Professor-FURC
4/22/2017
FURC-BCSE7

2. Digital Image Processing (DIP)
Instructor Dr. Abdul Basit Siddiqui
Text Book R. C. Gonzalez and R. E. Woods, “Digital Image Processing, Pearson Education, Inc., 2002.
Prerequisites 1. Fundamental knowledge of probability and random variables, Vectors and Matrices.
2. Working knowledge of Matlab
3. DSP topics such as convolution, FFT, filtering, etc.
Yahoo Group Lectures and Assignments will be updated on yahoo group regularly.
4/22/2017
FURC-BCSE7

3. Grading Policy Attendance 05% Assignments 05% Quizzes 05% Project 05%
Midterm %
Final %
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4. History Newspaper industry Cable transmission London – New York
1921: Image transmission
Newspaper industry
Cable transmission
London – New York
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5. History Moon picture Enhancement by computer 1960’s: Space program
The first picture of the moon by a U.S. spacecraft on July 31,1964 at 9:09 A.M. (courtesy of NASA)
1960’s: Space program
Moon picture
Enhancement by computer
1970: Computerized tomography (CT)
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FURC-BCSE7

6. Why Do We Process Images?
Facilitate picture storage and transmission
– Efficiently store an image in a digital camera
– Send an image through mobile phone
Enhance and restore images
– Remove scratches from an old photo
– Improve visibility of tumor in a radiograph
Analog images are large in size so for storage in small memory devices such as digital cameras and mobile phones the images are digitized.
Images are sharpened to enhance visibility
4/22/2017
FURC-BCSE7

7. Why Do We Process Images?
Extract information from images
– Measure water pollution from aerial images
– Measure the 3D distances and heights of objects from stereo images
Prepare for display or printing
– Adjust image size
– Halftoning
Image cropping and clipping
Show different grey levels are combination of dots. The process of converting a continuous tone image into an image that can be printed with one color ink (grayscale) or four color inks (color) is referred to as halftoning. Halftoning relys on the inability of the human eye to distinguish spots that are closely spaced.
4/22/2017
FURC-BCSE7

8. Image Processing Applications
Nuclear medicine
Medical Diagnostics
Automated Industrial Inspection
Remote Sensing
Weather Prediction
Military reconnaissance
Geological exploration
Astronomical Observations
Image database management
The paperless office
Photographers, advertising agencies and publishers
Machine vision
Biometrics
Finger Prints
Iris etc.
Movies and entertainment
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9. Image Enhancement
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10. Image Processing Examples
Photo Restoration
Has been enhanced and cracks have been removed
Damaged Image Restored Image
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11. Image Processing Examples
Photo Restoration
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12. Image Processing Examples
Photo Colorization
If color is faded like in old photographs you can improve it through colorization.
Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task
Original B/W Image Colorized Image Original Image Colorized Image
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13. Image Processing Examples
Photo Colorization
neighboring pixels in space-time that have similar intensities should have similar colors.
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14. Image Processing Examples
These methods are very problem-oriented: a method that works fine in one case
may be completely inadequate for another problem
Image enhancement is the improvement of digital image quality (wanted e.g. for visual inspection or for machine analysis), without knowledge about the source of degradation. If the source of degradation is known, one calls the process image restoration. Both are iconical processes, viz. input and output are images.
greyvalue histogram .
point processing
neighbour image
Original Images Enhanced Images
4/22/2017
FURC-BCSE7

15. Image Processing Examples
Restoration of Image from Hubble Space Telescope
Hubble Telescope had some problem with its lens, so the image was faulty and had the blurring effect in it, and now due DIP techniques it has been sharpened and details have been improved
Laplace transforms are rotation-invariant, and produce image sharpening without any directional information.
Faulty Image of Saturn Recovered Image
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FURC-BCSE7

16. Image Processing Examples
Halftoning
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17. Image Processing Examples
Halftoning
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18. Image Processing Examples
Halftoning
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19. Image Processing Examples
Extraction of Settlement Area from an Aerial image
Due to the difference in the texture of the town and the fields the settlement area can be easily identified
Faulty Image of Saturn Recovered Image
4/22/2017
FURC-BCSE7

20. Image Processing Examples
Earthquake Analysis from Space
Ground displacement taken from two images; one before the earthquake and another after the earth quake. Difference between the two is shown with colored areas..
Image shows the ground displacement of a typical area due to earthquake
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21. Image Processing Examples
Stereo Images from Satellite
. Images of the same area but from different angles. We can find heights
Image shows the ground displacement of a typical area due to earthquake
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22. Image Processing Examples
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23. Image Processing Examples
Face Detection
match spec features on the whole image to find faces
Image shows the ground displacement of a typical area due to earthquake
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24. Image Processing Examples
Face Tracking
Different from face detection.
Image shows the ground displacement of a typical area due to earthquake
4/22/2017
FURC-BCSE7

25. Image Processing Examples
Face Morphing
Blend two images: features and colors
In morphing we interpolate colors and features to get face morphing. Gradually changes from one to other.
Faulty Image of Saturn Recovered Image
4/22/2017
FURC-BCSE7

26. Image Morphing
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27. Image Processing Examples
Fingerprint Recognition
Through histogram equalization we can highlight fingerprints in image, then match with db.
Faulty Image of Saturn Recovered Image
4/22/2017
FURC-BCSE7

28. Applications of DIP Electromagnetic (EM) band Imaging
– Gamma ray band images
– X-ray band images
–Ultra-violet band images
– Visual light and infra-red images
– Imaging based on micro-waves and radio waves
Applications according to sources of images; what types of rays are used to get images. visual image is most common
4/22/2017
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29. Some Research Projects
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30. Monitoring Human Behavior from Video Taken in an Office Environment
A system which makes context-based
decisions about the actions of people in a
room. These actions include entering,
using a computer terminal, opening a
cabinet, picking up a phone, etc.
Source:
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31. EM Spectrum 4/22/2017 FURC-BCSE7
Energy of photon is directly proportional to the frequency. Energy of one photon for gamma rays is highest so they can penetrate
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32. Applications of DIP (EM Band Imaging)
Gamma-Ray Imaging
– Nuclear medicine, astronomical observations.
X-Ray Imaging
– Medical diagnostics (CAT scans, x-ray scans), industry, astronomy.
Ultra-Violet Imaging
– Fluorescence microscopy, astronomy
Visible & Infrared-band Imaging (most widely used)
– Light microscopy, astronomy, remote sensing, industry, law enforcement, military recognizance, etc.
Micro-wave and Radio band Imagery
– Radar, Medicine (MRI), astronomy
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33. MONITORING HEAD/EYE MOTION FOR DRIVER ALERTNESS
4/22/2017
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34. MONITORING FAST FOOD PRODUCTION
The purpose of the project is to automatically monitor a fast food employee as she puts together a sandwich. Helpful in determining correctness of sandwich assembly, collecting statistics on employee performance and food safety inspection.
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35. Classification of DIP and Computer Vision Processes
Low-Level Process: (DIP)
– Primitive operations where inputs and outputs are images; major functions: image pre-processing like noise reduction, contrast enhancement, image sharpening, etc.
Mid-Level Process (DIP and Computer Vision)
– Inputs are images, outputs are attributes (e.g., edges); major functions: segmentation, description, classification / recognition of objects
High-Level Process (Computer Vision)
– Make sense of an ensemble of recognized objects; perform the cognitive functions normally associated with vision
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36. Image Processing Steps
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37. DIP Course Digital Image Fundamentals and Image Acquisition (briefly)
Image Enhancement in Spatial Domain
– Pixel operations
– Histogram processing
– Filtering
Image Enhancement in Frequency Domain
– Transformation and reverse transformation
– Frequency domain filters
– Homomorphic filtering
Image Restoration
– Noise reduction techniques
– Geometric transformations
4/22/2017
FURC-BCSE7

38. DIP Course Color Image Processing
– Color models
– Pseudocolor image processing
– Color transformations and color segmentation
Wavelets and Multi-Resolution Processing
– Multi-resolution expansion
– Wavelet transforms, etc.
Image Compression
– Image compression models
– Error free compression
– Lossy compression, etc
4/22/2017
FURC-BCSE7

39. DIP Course Image Segmentation – Edge, point and boundary detection
– Thresholding
– Region based segmentation, etc
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40. Image Representation Image Value of f : Intensity or gray level
Two-dimensional function f(x,y)
x, y : spatial coordinates
Value of f : Intensity or gray level
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41. Digital Image A set of pixels (picture elements, pels) Pixel means
pixel coordinate
pixel value
or both
Both coordinates and value are discrete
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42. Example
640 x bit image
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43. 4/22/2017
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