Digital Image Processing COSC 6380/4393 Lecture 1 Aug 21 st, 2018 Slides from Dr. Shishir K Shah and Frank (Qingzhong) Liu
Digital Image Processing COSC 6380/4393 Instructor Pranav Mantini Email: pmantini@uh.edu Office: PGH 550E Office Hours: TTH 2 3 PM TA Office: TBA (Temporary: PGH 550E) Shikha Tripathi (shikhatripathi005@gmail.com) Office Hours: MW 1-2 PM Zhenggang Li (zli36@uh.edu) Office Hours: MW 2 3 PM Poonam Beniwal (pbeniwal@uh.edu) Office Hours: MW 3 4 PM
Introduction to the course Class Time & Location Section 1: Location: D3 W122 Tu, Th 11:30 AM 1:00 PM Section 2 Location: SEC 206 Tu, Th 5:30 PM 7:00 PM qil.uh.edu/dip Grading Homework: 50% Exam: 20% Project: 30%
Individual Assignments 3 Assignments (10% + 20% + 20%) - 50% Implementation, Python, OpenCV Libraries Mid-term Exam 20% Group Term Project - 30% ~6 Person team
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, 2 nd Edition/3 rd 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 21 st, 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 longduration shutter speed to sharply capture the stationary elements of images
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 22
Origins of Digital Image Processing 23
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 The Great Wall As varied as the scales found in nature Hubble Space Telescope 10 28 m (of galaxies) 1 m video camera electron microscope 10-6 m
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 30
Examples: Gama-Ray Imaging Weeks 1 & 2 31
Examples: X-Ray Imaging Weeks 1 & 2 32
Examples: Ultraviolet Imaging Weeks 1 & 2 33
Examples: Light Microscopy Imaging Weeks 1 & 2 34
Examples: Visual and Infrared Imaging Weeks 1 & 2 35
Examples: Visual and Infrared Imaging Weeks 1 & 2 36
Examples: Infrared Satellite Imaging USA 1993 2003 Weeks 1 & 2 37
Examples: Automated Visual Inspection Weeks 1 & 2 38
Examples: Automated Visual Inspection Results of automated reading of the plate content by the system The area in which the imaging system detected the plate Weeks 1 & 2 39
Fundamental Steps in DIP Extracting image components Improving the appearance Result is more suitable than the original Partition an image into its constituent parts or objects Represent image for computer processing Weeks 1 & 2 43
Survey https://tinyurl.com/yc3ojzkb