Digital Image Processing - University of...

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 (pmantini@uh.edu)

– Email: pmantini@cs.uh.edu

– Office: PGH 550E

– Office Hours: TBA

• TA – Arko Barman (abarman@uh.edu)

– Shikha Tripathi (shikhatripathi005@gmail.com)

– 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