4. image and video processing from mars.pdf
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imae and video processingTRANSCRIPT
Image and video processing: From Mars
to Hollywood with a stop at the hospital
Guillermo Sapiro
In this class you will look behind the scenes of image and video processing, from the basic and
classical tools to the most modern and advanced algorithms.
Watch intro video
Next Session:
Jan 14th 2013 (9 weeks long) Sign Up
Workload: 4 - 6 hours/ week
About the Course
What is image and video processing? Images and videos are everywhere, from those we take
with our mobile devices and share with our friends to those that we receive from Mars and the
ones we see in the movie theatre, without forgetting the whole ensemble of images of our bodies
that are taken in hospital visits. Image and video processing is the art of working with such
images and movies, from making it possible to store and transmit them to making those dark and
blurry images look nice, as well as interpreting and analyzing the medical data and recognizing
our friends’ faces in social pictures. This discipline is also fascinating because it uses tools from
many areas of applied mathematics. In this class you will look behind the scenes of image and
video processing, from the basic and classical tools to the most modern and advanced algorithms.
The course will start with an introduction to the basics of image formation and the fundamental
concepts that translate a physical scene into a digital image. We will then describe the underlying
concepts of image compression, the enabling technology that makes it possible for images to be
sent from Mars and videos to be stored in our mobile phones. We will cover the most
fundamental tools in image enhancement, showing how simple tools can significantly improve
images. Both geometric and non-geometric tools as well as spatial and non-spatial operations
will be presented. Details on image segmentation will be provided, one of the most fundamental
and useful problems in image processing. The above topics will be extended to color images and
video. Once we have covered the fundamentals, which both provide the basis for modern image
and video processing and serve many important applications until today, we will move into
recent progress in the area, covering image inpainting (how to remove objects from images and
video), image processing via sparse modeling and compressed sensing, geometric partial
differential equations for image analysis, image processing for HIV and virus research, and
image processing for neurosurgery and other medical applications.
About the Instructor(s)
Guillermo Sapiro received his B.Sc. (summa cum laude), M.Sc., and Ph.D. from the Department
of Electrical Engineering at the Technion, Israel Institute of Technology. After post-doctoral
research at MIT, he became Member of Technical Staff at the research facilities of HP Labs in
Palo Alto, California, where he co-developed the image compression techniques used in the
original Mars Rovers expedition. He was with the Department of Electrical and Computer
Engineering at the University of Minnesota, where he held the position of Distinguished
McKnight University Professor and Vincentine Hermes-Luh Chair in Electrical and Computer
Engineering. Currently he is with Duke University. His awards include the Office of Naval
Research Young Investigator Award in 1998, the Presidential Early Career Awards for Scientist
and Engineers (PECASE) in 1998 (awarded in the White House by President Clinton), the
National Science Foundation Career Award in 1999, the National Security Science and
Engineering Faculty Fellowship in 2010, and the Test of Time Award in 2011 for his paper on
image segmentation. His algorithms appear in Adobe’s products, leading medical imaging
packages such as ITK, and are also roaming on Mars. He has been teaching image processing for
over 15 years and delivered numerous invited plenary talks and short courses at leading imaging
and applied mathematics conferences. G. Sapiro is the founding Editor-in-Chief of the SIAM
Journal on Imaging Sciences, currently ranked as the second highest impact journal in the whole
discipline of applied mathematics.
Course Syllabus
Week 1- Introduction to Image and Video Processing: We will cover the fundamentals,
including some elements of visual perception, sensing, sampling, and quantization.
Week 2- Image and Video Compression: We will learn the fundamental tools enabling us to
receive images from Mars, to upload images to the web, and to store a lot of images and videos
in our mobile phones.
Week 3- Spatial Processing: This week we will learn some of the most classical and fundamental
tools that help us still today to make noisy, blurry, and dark images look much better.
Week 4- Image Restoration: When something is known or estimated about the degradation
process, we can do much better, and in this week we will learn how.
Week 5- Image Segmentation: How do we split an image or video in its core components?
Week 6- Geometric PDEs: We will learn about the use of partial differential equations and
geometric deformations for problems like image enhancement and object detection.
Week 7- Image and Video Inpainting: How to make objects disappear and other special effects.
Week 8- Sparse Modeling and Compressed Sensing: We will cover some of the most modern
tools for image enhancement and image analysis.
Week 9- Medical Imaging: As an example of medical image analysis, we will illustrate
examples and techniques in the areas of brain research and virus analysis.
Recommended Background
Image and video analysis can be approached from numerous areas of mathematics, from linear
algebra to geometry, optimization, and differential equations. We plan to make all the lectures as
self-contained as possible, but basic background in linear algebra and digital signal processing
will be helpful.
Suggested Readings
The first 5 lectures will follow, in part, "Digital Image Processing, 3rd edition" by Gonzalez and
Woods. The more advanced material will be based on material the instructor will make available.
Some interesting books for the advanced material include:
Michael Elad, Sparse and Redundant Representations: From Theory to Applications in Signal
and Image Processing, Springer.
Guillermo Sapiro, Geometric Partial Differential Equations in Image Analysis, Cambridge
University Press.
Alex Bronstein, Michael Bronstein, and Ron Kimmel, Numerical Geometry of Non-Rigid
Shapes, Springer
One of the first and still outstanding books in digital image processing is: Azriel Rosenfeld and
Avinash Kak, Digital Picture Processing, Academic Press.
Course Format
The class will consist of lecture videos, normally less then 15 minutes in length. Several such
segments will constitute a weekly class. Weekly homework/quizzes will help students to stay on
track. There will also be frequently assigned optional programming projects to help students
experience the practice of image and video analysis. Weekly subjects will stay as self-contained
as possible, starting every week with a new topic in the rich area of image and video analysis.
FAQ
Will I get a Statement of Accomplishment after completing this class?
Yes. Students who successfully complete the class will receive a Statement of
Accomplishment signed by the instructor.
What resources will I need for this class?
The main resource needed for this class is curiosity and an appetite for learning a new
topic. For those interested in pursuing some of the projects, having Matlab (with their
own license) and the image processing toolbox will be very useful. However, the same
projects could be performed in other programming environments available to the
students.
What are the coolest things I'll learn if I take this class?
How to make objects disappear in images and videos, how to see the shape of viruses,
how to analyze the inside of your brain, and how we can store so many images and
videos in our mobile phones.
For more information and registration visit: https://www.coursera.org/course/images