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Digital Image Processing COSC 6380/4393
Lecture – 1
Aug 22st, 2017
Slides from Dr. Shishir K Shah and Frank (Qingzhong) Liu
Digital Image Processing COSC 6380/4393
• Instructor – Pranav Mantini ([email protected])
– Email: [email protected]
– Office: PGH 550E
– Office Hours: TBA
• TA – Arko Barman ([email protected])
– Shikha Tripathi ([email protected])
– Office: TBA
– Office Hours: TBA
Introduction to the course
• Class Time & Location
– Class Tu, Th – 11:30 AM – 1:00 PM
– Location: SEC 201
– qil.uh.edu/dip
• Grading
– Homework: 50%
– Exam: 20%
– Project: 30%
• Individual Assignments
– ~2 ½ Assignments (20% + 20% + 10%) - 50%
– Implementation, Python, OpenCV Libraries
• Group Term Project - 30%
– ~4 to 6 Person team
• Mid-term Exam – 20%
• Individual Assignments
– ~2 ½ Assignments (20% + 20% + 10%) - 50%
– Implementation, Python, OpenCV Libraries
• Group Term Project - 30%
– ~4 to 6 Person team
• Mid-term Exam – 20%
• Survey: https://tinyurl.com/y73vjc8v
Logistics
• Late policy for project and Assignments: – Late by 1 day - 25% off the grade
– Late by 2 days - 50% off the grade
– Late by more than 2 days – No Credit
• Collaboration policy: – Discussing project assignment with each other is
allowed, but coding must be done individually
– Home works or class project coding policy: using on line code or other students/researchers’ code is not allowed in general.
RECOMMENDED BOOKS
• Digital Image Processing, 2nd Edition/3rd Edition, R. C. Gonzales and R. E. Woods, Prentice Hall.
• Digital Image Processing, K. R. Castleman, Prentice Hall, 1996.
• Image Processing, Analysis, and Machine Vision, Milan Sonka, Vaclav Hlavac, and Roger Boyle, Pacific Grove, 1999.
• Fundamentals of Digital Image Processing, Anil K. Jain, Prentice Hall, 1989.
• The Image Processing Handbook, John C. Russ, CRC Press, 2002.
REFERENCES
• Digital Image Processing, W.K. Pratt, Wiley, 1992 - Encyclopedic, somewhat dated.
• Digital Picture Processing, Rosenfeld & Kak, Academic, 1982 - Encyclopedic but readable.
• Fundamentals of Digital Image Processing, Jain, Prentice 1989 - Handbook-style, terse. Meant for advanced level.
• Machine Vision, Jain, Kasturi, and Schunk, McGraw-Hill, 1995 - Beginner’s book on computer vision.
• Robot Vision, B.K.P. Horn, MIT Press, 1986 - Advanced-level book on computer vision.
• Digital Video Processing, M. Tekalp, Prentice-Hall, 1995 - Only book devoted to digital video; high-level; excellent.
SOURCE OF LITERATURE
• IEEE Transactions on: – - Image Processing – - Pattern Analysis and Machine Intelligence – - Biomedical Image Processing – - Remote Sensing
• Computer Vision, Graphics, and Image Processing – - Image Understanding – - Graphics and Image Processing
• Pattern Recognition • Journal of Visual Communication and Image
Representation • Image and Vision Computing
MORE SOURCES
• Proc. IEEE Computer Society Conf. On Computer Vision and Pattern Recognition
• Proc. IEEE Conference on Image Processing
• Proc. Intl. Conference on Pattern Recognition
• Proc. Intl. Conference in Computer Vision
• Proc. Workshop on Computer Vision
• Proc. European Conf. On Computer Vision
• Proc. Asian Conf. On Computer Vision
Pre-Introduction
• Example: Measure depth of the water in meters at a certain pier
• Take measurements randomly over time
H 18 22 4 9 17 7 21 3 19 1 12 13 15 11 6 16 23 10 8 2 20 0 14 5
D 2 1.5 2.4 1.5 2.2 1.75 1.5 2.5 1.75 2.25 2 2.25 2.5 1.75 2 2.4 1.75 1.5 1.5 2.4 1.5 2 2.4 2.25
Pre-Introduction
• Example: Measure depth of the water in meters at a certain pier
• Another representation
H 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
D 2 2.2 2.4 2.5 2.4 2.25 2 1.75 1.5 1.5 1.5 1.7 2 2.25 2.4 2.5 2.4 2.25 2 1.75 1.5 1.5 1.5 1.75
Pre-Introduction
• Example: Measure depth of the water in meters at a certain pier
• Yet another representation (image/graph)
Pre-Introduction
• Example: Measure depth of the water in meters at a certain pier
• Yet another representation
• Image as a mode/format to convey information usually for human consumption
Why an Image?
Why an Image?
• Psychology
– Vision is how we experience the world
– ~ 50% of cerebral cortex is for vision
Image Processing
• How do I acquire images that capture information? – Image Acquisition
• How do I process and present the acquired image? – Filtering and image enhancement
– Image restoration
– Color image processing
– Compression
Example: Image Acquisition
• Solar Eclipse: August 21st, 2017
• Objective: Determine the progression of the eclipse
Example: Image Acquisition
• Image Acquisition device: IPhone 5 camera
Example: Image Acquisition
• Image Acquisition device: Cardboard box with holes
Example: Image Acquisition
• Nasa: Solar Dynamics Observatory
Source: https://www.nasa.gov/image-feature/goddard/2017/sdo-views-2017-solar-eclipse-171-angstrom
Example: Image Processing
Long-exposure photography: Involves using a long-duration shutter speed to sharply capture the stationary elements of images
Weeks 1 & 2 23
Origins of Digital Image Processing
Sent by submarine cable between London and New York, the transportation time was reduced to less than three hours from more than a week
Weeks 1 & 2 24
Origins of Digital Image Processing
WHAT ARE DIGITAL IMAGES? • Images are as variable as the types of radiation that exist and
the ways in which radiation interacts with matter:
WHAT ARE DIGITAL IMAGES? • Images are as variable as the types of radiation that exist and
the ways in which radiation interacts with matter:
WHAT ARE DIGITAL IMAGES? • Images are as variable as the types of radiation that exist and
the ways in which radiation interacts with matter:
WHAT ARE DIGITAL IMAGES? • Images are as variable as the types of radiation that exist and
the ways in which radiation interacts with matter:
GENERAL IMAGE TYPES • We can distinguish between three types of imaging, which create different
types of image information: • Reflection Imaging
– Image information is surface information; how an object reflects/absorbs incident radiation • - Optical (visual, photographic, laser-based) • - Radar • - Sonar, ultrasound (non-EM) • - Electron microscopy
• Emission Imaging – Image information is internal information; how an object creates radiation
• - Thermal, infrared (FLIR) (geophysical, medical, military) • - Astronomy (stars, nebulae, etc.) • - Nuclear (particle emission, e.g., MRI)
• Absorption Imaging – Image information is internal information; how an object modifies/absorbs
radiation passing through it • - X-Rays in many applications • - Optical microscopy in laboratory applications • - Tomography (CAT, PET) in medicine • - “Vibro-Seis” in geophysical prospecting
SCALES OF IMAGING
• As varied as the scales found in nature
video camera
1 m
10-6
m
electronmicroscope
Hubble Space
Telescope
The Great Wall
(of galaxies)
1028
m
Weeks 1 & 2 31
Electromagnetic (EM) energy spectrum
Major uses
Gamma-ray imaging: nuclear medicine and astronomical observations
X-rays: medical diagnostics, industry, and astronomy, etc.
Ultraviolet: lithography, industrial inspection, microscopy, lasers, biological imaging, and astronomical observations
Visible and infrared bands: light microscopy, astronomy, remote sensing, industry, and law enforcement
Microwave band: radar
Radio band: medicine (such as MRI) and astronomy
Weeks 1 & 2 32
Examples: Gama-Ray Imaging
Weeks 1 & 2 33
Examples: X-Ray Imaging
Weeks 1 & 2 34
Examples: Ultraviolet Imaging
Weeks 1 & 2 35
Examples: Light Microscopy Imaging
Weeks 1 & 2 36
Examples: Visual and Infrared Imaging
Weeks 1 & 2 37
Examples: Visual and Infrared Imaging
Weeks 1 & 2 38
Examples: Infrared Satellite Imaging
USA 1993 USA 2003
Weeks 1 & 2 39
Examples: Automated Visual Inspection
Weeks 1 & 2 40
Examples: Automated Visual Inspection
The area in which the imaging system detected the plate
Results of automated reading of the plate content by the system
Weeks 1 & 2 44
Fundamental Steps in DIP
Result is more suitable than the original
Improving the appearance
Extracting image components
Partition an image into its constituent parts or objects
Represent image for computer processing
