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Image and video processing From Mars to Hollywood with a stop at the hospital

Course Summary

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.


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    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.

    Computer Exercises- See below for more details on this.

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    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.

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    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.

    Computer Exercises We will have numerous optional computer exercises, with forums and teaching assistants dedicated to them. This in addition to students, which in the past have produced outstanding codes and helped each other tremendously. MathWorks (http://www.mathworks.com/) will provide free Matlab (with the necessary toolboxes) for those students registered to the class and that are interested in using Matlab for their optional homework. Tutorials will be provided to help those that need to learn the language.

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    Suggested Reading

    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 Fee:
Free

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

This course is listed under Development & Implementations , Operating Systems and Telecommunications Community

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