dip lecture 01 (26 sep)
TRANSCRIPT
-
7/31/2019 DIP Lecture 01 (26 Sep)
1/43
Digital Image Processing (DIP)
Dr. Abdul Basit Siddiqui
Assistant Professor-FUIEMS
10/13/2012 1FUIEMS-BCSE7
-
7/31/2019 DIP Lecture 01 (26 Sep)
2/43
-
7/31/2019 DIP Lecture 01 (26 Sep)
3/43
10/13/2012 FUIEMS-BCSE7 3
Grading Policy
Assignments 05%
Quizzes 05%
Attendance 05%
Midterm 30%
Project 05%
Final 50%
-
7/31/2019 DIP Lecture 01 (26 Sep)
4/43
-
7/31/2019 DIP Lecture 01 (26 Sep)
5/43
History
1960s: Space program
Moon picture
Enhancement by
computer
1970: Computerized
tomography (CT)
10/13/2012 FUIEMS-BCSE7 5
The first picture of themoon by a U.S.
spacecraft on July
31,1964 at 9:09 A.M.
(courtesy of NASA)
-
7/31/2019 DIP Lecture 01 (26 Sep)
6/43
10/13/2012 FUIEMS-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
-
7/31/2019 DIP Lecture 01 (26 Sep)
7/43
10/13/2012 FUIEMS-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
-
7/31/2019 DIP Lecture 01 (26 Sep)
8/43
Image Processing Applications
Nuclear medicine
Medical Diagnostics
Automated Industrial Inspection
Remote Sensing
Weather Prediction
Military Investigations 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
10/13/2012 FUIEMS-BCSE7 8
-
7/31/2019 DIP Lecture 01 (26 Sep)
9/43
Image Enhancement
10/13/2012 FUIEMS-BCSE7 9
-
7/31/2019 DIP Lecture 01 (26 Sep)
10/43
10/13/2012 FUIEMS-BCSE7 10
Image Processing Examples
Photo Restoration
Damaged Image Restored Image
-
7/31/2019 DIP Lecture 01 (26 Sep)
11/43
10/13/2012 FUIEMS-BCSE7 11
Image Processing Examples
Photo Restoration
-
7/31/2019 DIP Lecture 01 (26 Sep)
12/43
-
7/31/2019 DIP Lecture 01 (26 Sep)
13/43
-
7/31/2019 DIP Lecture 01 (26 Sep)
14/43
10/13/2012 FUIEMS-BCSE7 14
Image Processing Examples
Original Images Enhanced Images
-
7/31/2019 DIP Lecture 01 (26 Sep)
15/43
10/13/2012 FUIEMS-BCSE7 15
Image Processing Examples
Restoration of Image from Hubble Space Telescope
Faulty Image of Saturn Recovered Image
-
7/31/2019 DIP Lecture 01 (26 Sep)
16/43
10/13/2012 FUIEMS-BCSE7 16
Image Processing Examples
Halftoning
-
7/31/2019 DIP Lecture 01 (26 Sep)
17/43
10/13/2012 FUIEMS-BCSE7 17
Image Processing Examples
Halftoning
-
7/31/2019 DIP Lecture 01 (26 Sep)
18/43
10/13/2012 FUIEMS-BCSE7 18
Image Processing Examples
Halftoning
-
7/31/2019 DIP Lecture 01 (26 Sep)
19/43
10/13/2012 FUIEMS-BCSE7 19
Image Processing Examples
Extraction of Settlement Area from an Aerial image
Faulty Image of Saturn Recovered Image
-
7/31/2019 DIP Lecture 01 (26 Sep)
20/43
10/13/2012 FUIEMS-BCSE7 20
Image Processing Examples
Earthquake Analysis from Space
Image shows the ground displacement of a typical area due to earthquake
-
7/31/2019 DIP Lecture 01 (26 Sep)
21/43
10/13/2012 FUIEMS-BCSE7 21
Image Processing Examples
Stereo Images from Satellite
Image shows the ground displacement of a typical area due to earthquake
-
7/31/2019 DIP Lecture 01 (26 Sep)
22/43
10/13/2012 FUIEMS-BCSE7 22
Image Processing Examples
-
7/31/2019 DIP Lecture 01 (26 Sep)
23/43
-
7/31/2019 DIP Lecture 01 (26 Sep)
24/43
10/13/2012 FUIEMS-BCSE7 24
Image Processing Examples
Face Tracking
Image shows the ground displacement of a typical area due to earthquake
-
7/31/2019 DIP Lecture 01 (26 Sep)
25/43
10/13/2012 FUIEMS-BCSE7 25
Image Processing Examples
Face Morphing
Faulty Image of Saturn Recovered Image
-
7/31/2019 DIP Lecture 01 (26 Sep)
26/43
Image Morphing
10/13/2012 FUIEMS-BCSE7 26
-
7/31/2019 DIP Lecture 01 (26 Sep)
27/43
10/13/2012 FUIEMS-BCSE7 27
Image Processing Examples
Fingerprint Recognition
Faulty Image of Saturn Recovered Image
-
7/31/2019 DIP Lecture 01 (26 Sep)
28/43
10/13/2012 FUIEMS-BCSE7 28
Applications of DIP
Electromagnetic (EM) band ImagingGamma 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
-
7/31/2019 DIP Lecture 01 (26 Sep)
29/43
Some Research Projects
10/13/2012 FUIEMS-BCSE7 29
-
7/31/2019 DIP Lecture 01 (26 Sep)
30/43
Monitoring Human Behavior from VideoTaken 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:http://server.cs.ucf.edu/~vision/
10/13/2012 FUIEMS-BCSE7 30
http://server.cs.ucf.edu/~vision/projects/Office/IVC.pdfhttp://server.cs.ucf.edu/~vision/projects/Office/IVC.pdfhttp://server.cs.ucf.edu/~vision/projects/Office/IVC.pdfhttp://server.cs.ucf.edu/~vision/projects/Office/IVC.pdf -
7/31/2019 DIP Lecture 01 (26 Sep)
31/43
10/13/2012 FUIEMS-BCSE7 31
EM Spectrum
li i f d i
-
7/31/2019 DIP Lecture 01 (26 Sep)
32/43
10/13/2012 FUIEMS-BCSE7 32
Applications of DIP (EM Band Imaging)
Gamma-Ray ImagingNuclear medicine, astronomical observations.
X-Ray Imaging
Medical diagnostics (CAT scans, x-ray scans), industry, astronomy.
Ultra-Violet ImagingFluorescence 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
-
7/31/2019 DIP Lecture 01 (26 Sep)
33/43
MONITORING HEAD/EYE MOTIONFOR DRIVER ALERTNESS
10/13/2012 FUIEMS-BCSE7 33
-
7/31/2019 DIP Lecture 01 (26 Sep)
34/43
MONITORING FAST FOODPRODUCTION
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 employeeperformance and food
safety inspection.
10/13/2012 FUIEMS-BCSE7 34
Cl ifi i f DIP d C Vi i P
-
7/31/2019 DIP Lecture 01 (26 Sep)
35/43
10/13/2012 FUIEMS-BCSE7 35
Classification of DIP and Computer Vision Processes
Low-Level Process: (DIP)
Primitive operations where inputs and outputs are images; majorfunctions: 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
I P i S
-
7/31/2019 DIP Lecture 01 (26 Sep)
36/43
10/13/2012 FUIEMS-BCSE7 36
Image Processing Steps
DIP C
-
7/31/2019 DIP Lecture 01 (26 Sep)
37/43
10/13/2012 FUIEMS-BCSE7 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 RestorationNoise reduction techniques
Geometric transformations
DIP C
-
7/31/2019 DIP Lecture 01 (26 Sep)
38/43
10/13/2012 FUIEMS-BCSE7 38
DIP Course
Color Image ProcessingColor models
Pseudocolor image processing
Color transformations and color segmentation
Wavelets and Multi-Resolution Processing
Multi-resolution expansion
Wavelet transforms, etc.
Image Compression
Image compression modelsError free compression
Lossy compression, etc
DIP C
-
7/31/2019 DIP Lecture 01 (26 Sep)
39/43
10/13/2012 FUIEMS-BCSE7 39
DIP Course
Image Segmentation
Edge, point and boundary detection
Thresholding
Region based segmentation, etc
-
7/31/2019 DIP Lecture 01 (26 Sep)
40/43
Image Representation
Image
Two-dimensional function f(x,y)
x, y: spatial coordinates
Value off: Intensity orgray level
10/13/2012 FUIEMS-BCSE7 40
-
7/31/2019 DIP Lecture 01 (26 Sep)
41/43
Digital Image
A set ofpixels (picture elements, pels)
Pixel means
pixel coordinate
pixel value
or both
Both coordinates and value are discrete
10/13/2012 FUIEMS-BCSE7 41
-
7/31/2019 DIP Lecture 01 (26 Sep)
42/43
Example
640 x 480 8-bit image
10/13/2012 FUIEMS-BCSE7 42
-
7/31/2019 DIP Lecture 01 (26 Sep)
43/43
10/13/2012 FUIEMS-BCSE7 43