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Aggelos K. Katsaggelos
In this class you will learn the basic principles and tools used to process images and videos, and how to apply them in solving practical problems of commercial and scientific interests.
Read more
In this class you will learn the basic principles and tools used to process images and videos, and how to apply them in solving practical problems of commercial and scientific interests.
Read more
In this class you will learn the basic principles and tools used to process images and videos, and how to apply them in solving practical problems of commercial and scientific interests.
Digital images and videos are everywhere these days – in thousands of scientific (e.g., astronomical, bio-medical), consumer, industrial, and artistic applications. Moreover they come in a wide range of the electromagnetic spectrum - from visible light and infrared to gamma rays and beyond. The ability to process image and video signals is therefore an incredibly important skill to master for engineering/science students, software developers, and practicing scientists. Digital image and video processing continues to enable the multimedia technology revolution we are experiencing today. Some important examples of image and video processing include the removal of degradations images suffer during acquisition (e.g., removing blur from a picture of a fast moving car), and the compression and transmission of images and videos (if you watch videos online, or share photos via a social media website, you use this everyday!), for economical storage and efficient transmission.
This course will cover the fundamentals of image and video processing. We will provide a mathematical framework to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. In this class not only will you learn the theory behind fundamental processing tasks including image/video enhancement, recovery, and compression - but you will also learn how to perform these key processing tasks in practice using state-of-the-art techniques and tools. We will introduce and use a wide variety of such tools – from optimization toolboxes to statistical techniques. Emphasis on the special role sparsity plays in modern image and video processing will also be given. In all cases, example images and videos pertaining to specific application domains will be utilized.
Register for this course and see more details by visiting:
OpenCourser.com/course/87yjxs/fundamentals
What's inside
Syllabus
Introduction to Image and Video Processing
In this module we look at images and videos as 2-dimensional (2D) and 3-dimensional (3D) signals, and discuss their analog/digital dichotomy. We will also see how the characteristics of an image changes depending on its placement over the electromagnetic spectrum, and how this knowledge can be leveraged in several applications.
Read more
Syllabus
Introduction to Image and Video Processing
In this module we look at images and videos as 2-dimensional (2D) and 3-dimensional (3D) signals, and discuss their analog/digital dichotomy. We will also see how the characteristics of an image changes depending on its placement over the electromagnetic spectrum, and how this knowledge can be leveraged in several applications.
Read more
Signals and Systems
In this module we introduce the fundamentals of 2D signals and systems. Topics include complex exponential signals, linear space-invariant systems, 2D convolution, and filtering in the spatial domain.
Fourier Transform and Sampling
In this module we look at 2D signals in the frequency domain. Topics include: 2D Fourier transform, sampling, discrete Fourier transform, and filtering in the frequency domain.
Motion Estimation
In this module we cover two important topics, motion estimation and color representation and processing. Topics include: applications of motion estimation, phase correlation, block matching, spatio-temporal gradient methods, and fundamentals of color image processing
Image Enhancement
In this module we cover the important topic of image and video enhancement, i.e., the problem of improving the appearance or usefulness of an image or video. Topics include: point-wise intensity transformation, histogram processing, linear and non-linear noise smoothing, sharpening, homomorphic filtering, pseudo-coloring, and video enhancement.
Image Recovery: Part 1
In this module we study the problem of image and video recovery. Topics include: introduction to image and video recovery, image restoration, matrix-vector notation for images, inverse filtering, constrained least squares (CLS), set-theoretic restoration approaches, iterative restoration algorithms, and spatially adaptive algorithms.
Image Recovery : Part 2
In this module we look at the problem of image and video recovery from a stochastic perspective. Topics include: Wiener restoration filter, Wiener noise smoothing filter, maximum likelihood and maximum a posteriori estimation, and Bayesian restoration algorithms.
Lossless Compression
In this module we introduce the problem of image and video compression with a focus on lossless compression. Topics include: elements of information theory, Huffman coding, run-length coding and fax, arithmetic coding, dictionary techniques, and predictive coding.
Image Compression
In this module we cover fundamental approaches towards lossy image compression. Topics include: scalar and vector quantization, differential pulse-code modulation, fractal image compression, transform coding, JPEG, and subband image compression.
Video Compression
In this module we discus video compression with an emphasis on motion-compensated hybrid video encoding and video compression standards including H.261, H.263, H.264, H.265, MPEG-1, MPEG-2, and MPEG-4.
Image and Video Segmentation
In this module we introduce the problem of image and video segmentation, and discuss various approaches for performing segmentation including methods based on intensity discontinuity and intensity similarity, watersheds and K-means algorithms, and other advanced methods.
Sparsity
In this module we introduce the notion of sparsity and discuss how this concept is being applied in image and video processing. Topics include: sparsity-promoting norms, matching pursuit algorithm, smooth reformulations, and an overview of the applications.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores digital image and video processing which is the basis that enables the multimedia technology revolution we are experiencing today
Taught by Aggelos K. Katsaggelos who is recognized for their work in image and video processing
Examines the fundamentals of image and video processing and covers topics like image and video enhancement, recovery, and compression
Teachers learners practical problem solving skills in image and video processing for commercial and scientific interests
Develops foundational skills in image and vide processing which are popular in the field of engineering/science, software development, and scientific research
Requires learners to come in with some basic understanding of signals and systems
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Reviews summary
Well-received overview of digital image and video processing
Learners say that this course offers a well-received overview of digital image and video processing. Many students claimed it was very good though a few learners noted that some concepts were new to them as undergraduates.
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Fundamentals of Digital Image and Video Processing with these
activities:
Refresh knowledge of basics of image processing.
Show steps
Warming up with a review of the basics makes more difficult topics in the course seem less intimidating.
Browse courses on
Image Manipulation
Show steps
-
Read lecture notes for Intro to Image Processing.
-
Watch Youtube videos on basic image processing algorithms.
-
Review textbook chapters.
Read Digital Image Processing by Rafael C. Gonzalez and Richard E. Woods.
Show steps
This textbook is widely regarded as the definitive reference on digital image processing and provides a comprehensive overview of the field, making it an excellent resource for supplementing the course material.
View
Digital Image Processing, Global Edition
on Amazon
Show steps
-
Read each chapter thoroughly.
-
Complete the exercises at the end of each chapter.
Follow tutorials on image processing software.
Show steps
Hands-on experience with image processing software is essential for developing proficiency.
Show steps
-
Find tutorials for specific image processing tasks.
-
Follow the tutorials step-by-step.
-
Experiment with the software on your own.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Join a study group.
Show steps
Studying with peers can help improve understanding and retention.
Show steps
-
Find a study group or form your own.
-
Meet regularly to discuss course material.
-
Work together on assignments and projects.
Digital Image and Video Processing Exercises
Show steps
Reinforce your understanding of fundamental image and video processing techniques through practical exercises.
Browse courses on
Image Processing
Show steps
-
Solve practice problems on image and video enhancement.
-
Implement algorithms for image and video recovery and compression.
-
Analyze the results and compare different approaches.
Guided Image Processing Project
Show steps
Apply theoretical concepts learned in the course to a practical scenario by completing a guided image processing project.
Browse courses on
Image Processing
Show steps
-
Familiarize yourself with the project guidelines and requirements.
-
Gather the necessary data and software tools.
-
Develop a plan for the project.
-
Implement your image processing algorithms and techniques.
-
Evaluate the results and iterate to improve the performance of your project.
Complete problem sets.
Show steps
Solving problems is a crucial part of mastering the concepts covered in this course.
Show steps
-
Download the problem sets.
-
Work through the problems.
-
Check your answers against the solutions.
Complete the lab exercises.
Show steps
The lab exercises provide hands-on experience with image processing algorithms and tools.
Browse courses on
MATLAB
Show steps
-
Read the lab instructions carefully.
-
Set up the necessary software and hardware.
-
Follow the instructions to complete the lab.
-
Troubleshoot any problems that arise.
-
Submit your completed lab report.
Create a digital photo album.
Show steps
This project allows students to apply the techniques learned in the course in a practical and creative way.
Browse courses on
Image Manipulation
Show steps
-
Select a theme for your photo album.
-
Collect and edit photos that fit the theme.
-
Use image processing software to enhance the photos.
-
Organize the photos into a coherent narrative.
-
Create a digital photo album using a software program or online service.
Advanced Image Processing with Open Source Tools
Show steps
Expand your image processing skills by exploring advanced techniques using open source libraries and tools.
Browse courses on
Image Processing
Show steps
-
Find and install appropriate open source libraries.
-
Follow tutorials on advanced image processing algorithms.
-
Apply the techniques to real-world image processing tasks.
Write a blog post about a specific image processing technique.
Show steps
Explaining a concept to others helps reinforce understanding.
Browse courses on
Image Enhancement
Show steps
-
Choose a specific image processing technique to write about.
-
Research the technique thoroughly.
-
Write a clear and concise blog post explaining the technique, its applications, and its limitations.
-
Publish your blog post online.
Refresh knowledge of basics of image processing.
Show steps
Warming up with a review of the basics makes more difficult topics in the course seem less intimidating.
Browse courses on
Image Manipulation
Show steps
Show steps
Show steps
- Read lecture notes for Intro to Image Processing.
- Watch Youtube videos on basic image processing algorithms.
- Review textbook chapters.
Read Digital Image Processing by Rafael C. Gonzalez and Richard E. Woods.
Show steps
This textbook is widely regarded as the definitive reference on digital image processing and provides a comprehensive overview of the field, making it an excellent resource for supplementing the course material.
View
Digital Image Processing, Global Edition
on Amazon
Show steps
Show steps
Show steps
- Read each chapter thoroughly.
- Complete the exercises at the end of each chapter.
Follow tutorials on image processing software.
Show steps
Hands-on experience with image processing software is essential for developing proficiency.
Show steps
Show steps
Show steps
- Find tutorials for specific image processing tasks.
- Follow the tutorials step-by-step.
- Experiment with the software on your own.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Join a study group.
Show steps
Studying with peers can help improve understanding and retention.
Show steps
Show steps
Show steps
- Find a study group or form your own.
- Meet regularly to discuss course material.
- Work together on assignments and projects.
Digital Image and Video Processing Exercises
Show steps
Reinforce your understanding of fundamental image and video processing techniques through practical exercises.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Solve practice problems on image and video enhancement.
- Implement algorithms for image and video recovery and compression.
- Analyze the results and compare different approaches.
Guided Image Processing Project
Show steps
Apply theoretical concepts learned in the course to a practical scenario by completing a guided image processing project.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Familiarize yourself with the project guidelines and requirements.
- Gather the necessary data and software tools.
- Develop a plan for the project.
- Implement your image processing algorithms and techniques.
- Evaluate the results and iterate to improve the performance of your project.
Complete problem sets.
Show steps
Solving problems is a crucial part of mastering the concepts covered in this course.
Show steps
Show steps
Show steps
- Download the problem sets.
- Work through the problems.
- Check your answers against the solutions.
Complete the lab exercises.
Show steps
The lab exercises provide hands-on experience with image processing algorithms and tools.
Browse courses on
MATLAB
Show steps
Show steps
Show steps
- Read the lab instructions carefully.
- Set up the necessary software and hardware.
- Follow the instructions to complete the lab.
- Troubleshoot any problems that arise.
- Submit your completed lab report.
Create a digital photo album.
Show steps
This project allows students to apply the techniques learned in the course in a practical and creative way.
Browse courses on
Image Manipulation
Show steps
Show steps
Show steps
- Select a theme for your photo album.
- Collect and edit photos that fit the theme.
- Use image processing software to enhance the photos.
- Organize the photos into a coherent narrative.
- Create a digital photo album using a software program or online service.
Advanced Image Processing with Open Source Tools
Show steps
Expand your image processing skills by exploring advanced techniques using open source libraries and tools.
Browse courses on
Image Processing
Show steps
Show steps
Show steps
- Find and install appropriate open source libraries.
- Follow tutorials on advanced image processing algorithms.
- Apply the techniques to real-world image processing tasks.
Write a blog post about a specific image processing technique.
Show steps
Explaining a concept to others helps reinforce understanding.
Browse courses on
Image Enhancement
Show steps
Show steps
Show steps
- Choose a specific image processing technique to write about.
- Research the technique thoroughly.
- Write a clear and concise blog post explaining the technique, its applications, and its limitations.
- Publish your blog post online.
Career center
Learners who complete Fundamentals of Digital Image and Video Processing will develop knowledge and skills
that may be useful to these careers:
Image Processing Engineer
Image Processing Engineer
The demand for Image Processing Engineers is expected to grow in both industry and research sectors. Fundamentals of Digital Image and Video Processing will help you build a foundation to qualify for entry level roles in this role. This course will give you a solid background in the mathematical framework used to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. You will also learn how to perform key processing tasks in practice using state-of-the-art techniques and tools.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers help develop, test, and maintain computer vision systems. Systems such as object recognition software and facial detection use digital image and video processing. This course can help prepare you for entry level roles in this field. This course will give you a solid background in the mathematical framework used to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. You will also learn how to perform key processing tasks in practice using state-of-the-art techniques and tools.
Data Scientist
Data Scientist
A growing number of Data Scientists are focused on using digital image and video processing to help businesses and organizations leverage visual data. Fundamentals of Digital Image and Video Processing can help prepare you for entry level roles in this field. This course will give you a solid background in the mathematical framework used to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. You will also learn how to perform key processing tasks in practice using state-of-the-art techniques and tools.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers often work with Computer Vision Engineers on projects related to image/video annotation, improving visual search algorithms, and more. Fundamentals of Digital Image and Video Processing can help prepare you for entry level roles in this field. This course will give you a solid background in the mathematical framework used to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. You will also learn how to perform key processing tasks in practice using state-of-the-art techniques and tools.
Software Engineer
Software Engineer
The course, Fundamentals of Digital Image and Video Processing, may be useful for Software Engineers seeking entry level roles working on computer vision projects or using image/video processing techniques in their work. This course will give you a solid background in the mathematical framework used to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains.
Research Scientist
Research Scientist
The course, Fundamentals of Digital Image and Video Processing, may be useful for Research Scientists in a variety of fields where image/video processing techniques are used to process and analyze data. This course will give you a solid foundation in the mathematical framework used to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains.
Product Manager
Product Manager
A Product Manager in the technology sector may find value in taking the course Fundamentals of Digital Image and Video Processing to learn how image/video processing techniques are changing the way products are developed today. This knowledge is important for building competitive products that use digital image and video processing techniques to enhance user experience, improve quality control, and drive sales.
Business Analyst
Business Analyst
The course Fundamentals of Digital Image and Video Processing may be useful for Business Analysts who wish to work in a variety of industries where digital image and video processing techniques are used to make decisions. This course will provide you with an overview of the field.
Consultant
Consultant
A technology Consultant may find value in taking the course Fundamentals of Digital Image and Video Processing to learn how businesses are leveraging digital image and video processing techniques to drive decisions and make improvements. This specialized knowledge can be applied to client work and lead to successful project outcomes.
Marketing Manager
Marketing Manager
Taking the course, Fundamentals of Digital Image and Video Processing, may prove useful for Marketing Managers who lead digital advertising and social media marketing initiatives. This course will provide you with an overview of image/video processing techniques that can be used to improve the quality and effectiveness of digital marketing campaigns.
Sales Manager
Sales Manager
A Sales Manager working in IT sales may find value in taking the course Fundamentals of Digital Image and Video Processing to better understand the challenges and opportunities presented by digital image and video processing in their industry. This knowledge can be applied to sales conversations to better position products and services.
Financial Analyst
Financial Analyst
Financial Analysts working in investment banking and private equity may find value in taking the course Fundamentals of Digital Image and Video Processing to learn about the potential impact of image/video processing techniques on industries and companies. This knowledge can be applied to investment research, due diligence, and portfolio management.
Operations Manager
Operations Manager
An Operations Manager who is working for an organization that leverages image/video processing techniques may find value in taking the course Fundamentals of Digital Image and Video Processing. This course will provide you with an overview of the field.
Customer Success Manager
Customer Success Manager
A Customer Success Manager working with technology companies that have image/video processing capabilities may find value in taking the course Fundamentals of Digital Image and Video Processing. This knowledge can be applied to guiding and supporting customers in the use of these technologies.
Human Resources Manager
Human Resources Manager
An HR Manager may wish to take the course Fundamentals of Digital Image and Video Processing to keep up with the latest technologies that may impact the work of employees in technology and engineering roles
For more career information including salaries, visit:
OpenCourser.com/course/87yjxs/fundamentals
Reading list
We've selected 36 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Fundamentals of Digital Image and Video Processing.
Provides a comprehensive introduction to digital image processing using MATLAB. It covers various topics including image acquisition, enhancement, filtering, segmentation, and compression. This book is commonly used as a textbook in academic institutions and valuable reference for students and practitioners.
Provides a comprehensive overview of the fundamentals of digital image processing. It covers various topics including image acquisition, enhancement, filtering, segmentation, and compression. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
This classic textbook covers the fundamentals of digital image processing, including image acquisition, enhancement, restoration, compression, and analysis.
This textbook provides a comprehensive introduction to computer vision, with a focus on mathematical and computational principles.
This textbook provides a comprehensive introduction to digital image processing, with a focus on the underlying mathematical and computational principles.
Provides a comprehensive overview of digital video processing. It covers various topics including video acquisition, compression, enhancement, and analysis. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
This textbook provides a comprehensive introduction to image processing and analysis, with a focus on mathematical and computational techniques.
Provides a comprehensive introduction to computer vision. It covers various topics including image processing, feature extraction, object recognition, and video analysis. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
Save
This textbook covers the fundamentals of computer vision, including image formation, feature detection, image segmentation, and object recognition.
Provides a comprehensive introduction to computer vision, with a focus on algorithms and applications.
Save
This textbook provides a practical introduction to algorithms for image processing and computer vision.
This textbook provides a comprehensive introduction to advanced methods for image processing and computer vision.
Provides a comprehensive introduction to digital image processing in OpenCV. It covers various topics including image acquisition, enhancement, filtering, segmentation, compression, and analysis. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
Save
This textbook provides an introduction to machine learning for computer vision, with a focus on practical applications.
This textbook provides a practical introduction to digital image processing using Python.
Provides a comprehensive introduction to machine learning for computer vision, with a focus on practical applications.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers various topics including supervised learning, unsupervised learning, and reinforcement learning. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
Provides a comprehensive introduction to digital image processing. It covers various topics including image acquisition, enhancement, filtering, segmentation, compression, and analysis. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
Save
Provides a practical introduction to computer vision using OpenCV.
Provides a practical introduction to computer vision using OpenCV and Python.
Provides a comprehensive introduction to deep learning for computer vision. It covers various topics including convolutional neural networks, recurrent neural networks, and generative adversarial networks. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
Provides a comprehensive introduction to statistical learning. It covers various topics including linear regression, logistic regression, and decision trees. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
Save
Provides a comprehensive introduction to deep learning. It covers various topics including neural networks, convolutional neural networks, and recurrent neural networks. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
Provides a comprehensive introduction to deep learning. It covers various topics including neural networks, convolutional neural networks, and recurrent neural networks. This book valuable reference for students and practitioners who want to gain a deeper understanding of the field.
This textbook provides a comprehensive overview of the fundamental concepts and algorithms used in information theory, inference, and learning algorithms. It covers a wide range of topics, including probability, Bayesian inference, and machine learning.
Provides a comprehensive overview of digital signal processing algorithms. It covers a wide range of topics, including filter design, spectral analysis, and image processing. The book is well-written and provides a good balance between theory and practice.
This textbook provides a comprehensive overview of the fundamental concepts and algorithms used in convex optimization. It covers a wide range of topics, including linear programming, quadratic programming, and semidefinite programming.
Provides a comprehensive overview of statistical signal processing algorithms. It covers a wide range of topics, including parameter estimation, hypothesis testing, and adaptive filtering. The book is well-written and provides a good balance between theory and practice.
This textbook provides a comprehensive overview of the fundamental concepts and algorithms used in linear algebra. It covers a wide range of topics, including vectors, matrices, and linear transformations.
This textbook provides a comprehensive overview of the fundamental concepts and algorithms used in calculus. It covers a wide range of topics, including limits, derivatives, and integrals.
Provides a comprehensive overview of numerical optimization algorithms. It covers a wide range of topics, including unconstrained optimization, constrained optimization, and nonlinear optimization. The book is well-written and provides a good balance between theory and practice.
Provides a comprehensive overview of computer graphics algorithms. It covers a wide range of topics, including 3D modeling, rendering, and animation. The book is well-written and provides a good balance between theory and practice.
This textbook provides a comprehensive overview of the fundamental concepts and algorithms used in mathematical methods for physics and engineering. It covers a wide range of topics, including linear algebra, differential equations, and partial differential equations.
This textbook provides a comprehensive overview of the fundamental concepts and algorithms used in numerical recipes in C++. It covers a wide range of topics, including numerical integration, differential equations, and Monte Carlo methods.
Provides a comprehensive overview of digital image warping algorithms. It covers a wide range of topics, including image registration, image morphing, and image editing. The book is well-written and provides a good balance between theory and practice.
Provides a comprehensive overview of image processing and analysis algorithms. It covers a wide range of topics, including image enhancement, image restoration, and image segmentation. The book is well-written and provides a good balance between theory and practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/87yjxs/fundamentals
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Aggelos K. Katsaggelos
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Good to know
Explores digital image and video processing which is the basis that enables the multimedia technology revolution we are experiencing today
Taught by Aggelos K. Katsaggelos who is recognized for their work in image and video processing
Examines the fundamentals of image and video processing and covers topics like image and video enhancement, recovery, and compression
Teachers learners practical problem solving skills in image and video processing for commercial and scientific interests
Develops foundational skills in image and vide processing which are popular in the field of engineering/science, software development, and scientific research
Requires learners to come in with some basic understanding of signals and systems
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