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Matt Rich,
Amanda Wang,
Megan Thompson,
Brandon Armstrong,
and
Isaac Bruss
In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene.
Read more
In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene.
Read more
In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene.
Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same scene taken at different times or combine images acquired from different scientific instruments, as is common with hyperspectral and medical images.
You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work.
To be successful in this course, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/6elfl5/introduction
What's inside
Syllabus
Introduction to Features
Working With Features
Image Registration
Read more
Syllabus
Introduction to Features
Working With Features
Image Registration
Read more
Image Stitching
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces computer vision concepts, algorithms, and applications relevant to engineering and science
Taught by recognized experts in the field of computer vision
Utilizes MATLAB, a widely used software in engineering and science
Suitable for learners with prior image processing experience
Save this course
Save Introduction to Computer Vision to your list so you can find it easily later:
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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 Introduction to Computer Vision with these
activities:
Organize Course Resources
Show steps
Set yourself up for success by organizing and reviewing course materials.
Show steps
-
Download and save all course materials in a designated folder.
-
Create a system for organizing notes, assignments, and quizzes.
-
Review course materials regularly to reinforce your understanding.
Introduction to Feature Detection and Extraction
Show steps
Refresh your knowledge of feature detection and extraction to better prepare for the course.
Browse courses on
Feature Detection
Show steps
-
Review the concepts of feature detection and extraction.
-
Practice identifying and extracting features from images using popular algorithms.
-
Explore different feature descriptors and their applications.
Image Registration Exercises
Show steps
Reinforce your understanding of image registration through repetitive exercises.
Browse courses on
Image Registration
Show steps
-
Practice aligning images using different geometric transformations.
-
Evaluate the accuracy of image registration using quantitative metrics.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Applying Feature Matching to Image Stitching
Show steps
Enhance your understanding of feature matching by implementing image stitching techniques.
Browse courses on
Image Stitching
Show steps
-
Follow tutorials on image stitching algorithms.
-
Implement feature matching and blending techniques to create stitched images.
-
Experiment with different image pairs and stitching parameters.
Visualize Feature Distributions
Show steps
Develop a deeper understanding of feature distributions by creating visualizations.
Show steps
-
Extract features from a dataset of images.
-
Use data visualization techniques to represent feature distributions.
-
Analyze the visualizations to identify patterns and insights.
Become a Mentor in the Course Discussion Forum
Show steps
Enhance your understanding of the course material and help fellow students by becoming a mentor in the discussion forum.
Show steps
-
Actively participate in the discussion forum and provide thoughtful responses.
-
Identify students who might benefit from your guidance.
-
Offer support and encouragement to your mentees.
Participate in a Computer Vision Hackathon
Show steps
Challenge yourself and gain practical experience by participating in a computer vision hackathon.
Show steps
-
Join a team or form your own for the hackathon.
-
Brainstorm and select a computer vision project idea.
-
Develop and implement a computer vision solution.
-
Present your project and compete for prizes.
Develop a Feature-Based Object Detection System
Show steps
Apply your knowledge and skills to build a feature-based object detection system as a capstone project.
Browse courses on
Object Detection
Show steps
-
Design the architecture of the object detection system.
-
Implement the system using computer vision techniques and algorithms.
-
Evaluate the performance of the system on a dataset of images.
-
Document and present your project to the class.
Organize Course Resources
Show steps
Set yourself up for success by organizing and reviewing course materials.
Show steps
Show steps
Show steps
- Download and save all course materials in a designated folder.
- Create a system for organizing notes, assignments, and quizzes.
- Review course materials regularly to reinforce your understanding.
Introduction to Feature Detection and Extraction
Show steps
Refresh your knowledge of feature detection and extraction to better prepare for the course.
Browse courses on
Feature Detection
Show steps
Show steps
Show steps
- Review the concepts of feature detection and extraction.
- Practice identifying and extracting features from images using popular algorithms.
- Explore different feature descriptors and their applications.
Image Registration Exercises
Show steps
Reinforce your understanding of image registration through repetitive exercises.
Browse courses on
Image Registration
Show steps
Show steps
Show steps
- Practice aligning images using different geometric transformations.
- Evaluate the accuracy of image registration using quantitative metrics.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Applying Feature Matching to Image Stitching
Show steps
Enhance your understanding of feature matching by implementing image stitching techniques.
Browse courses on
Image Stitching
Show steps
Show steps
Show steps
- Follow tutorials on image stitching algorithms.
- Implement feature matching and blending techniques to create stitched images.
- Experiment with different image pairs and stitching parameters.
Visualize Feature Distributions
Show steps
Develop a deeper understanding of feature distributions by creating visualizations.
Show steps
Show steps
Show steps
- Extract features from a dataset of images.
- Use data visualization techniques to represent feature distributions.
- Analyze the visualizations to identify patterns and insights.
Become a Mentor in the Course Discussion Forum
Show steps
Enhance your understanding of the course material and help fellow students by becoming a mentor in the discussion forum.
Show steps
Show steps
Show steps
- Actively participate in the discussion forum and provide thoughtful responses.
- Identify students who might benefit from your guidance.
- Offer support and encouragement to your mentees.
Participate in a Computer Vision Hackathon
Show steps
Challenge yourself and gain practical experience by participating in a computer vision hackathon.
Show steps
Show steps
Show steps
- Join a team or form your own for the hackathon.
- Brainstorm and select a computer vision project idea.
- Develop and implement a computer vision solution.
- Present your project and compete for prizes.
Develop a Feature-Based Object Detection System
Show steps
Apply your knowledge and skills to build a feature-based object detection system as a capstone project.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Design the architecture of the object detection system.
- Implement the system using computer vision techniques and algorithms.
- Evaluate the performance of the system on a dataset of images.
- Document and present your project to the class.
Career center
Learners who complete Introduction to Computer Vision will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop systems that enable computers to see and interpret images and videos. This course provides a comprehensive overview of the fundamental concepts and algorithms used in Computer Vision, including feature detection, extraction, and matching. These techniques are essential for tasks such as object recognition, tracking, and image stitching, which are commonly used in applications such as self-driving cars, medical imaging, and robotics.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve real-world problems. Image features are an important input for many machine learning algorithms, and this course provides a thorough understanding of how to extract and use features for image recognition, classification, and other tasks. This knowledge is essential for Machine Learning Engineers who want to develop high-performing models for computer vision applications.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts use geographic data to solve real-world problems. Image registration is an important technique for Geospatial Analysts who work with satellite imagery or other geospatial data that needs to be aligned. This course provides a practical introduction to the algorithms and techniques used for image registration, which can help Geospatial Analysts develop more accurate and robust models.
Data Scientist
Data Scientist
Data Scientists use mathematical and statistical methods to analyze data and extract insights. Image registration is an important technique for Data Scientists who work with geospatial data, medical imaging, or other fields where multiple images need to be aligned. This course provides a solid foundation in the algorithms and techniques used for image registration, which can help Data Scientists develop more accurate and robust models.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and operate robots. Image registration is an important technique for Robotics Engineers who work with robots that need to navigate and interact with their environment. This course provides a solid foundation in the algorithms and techniques used for image registration, which can help Robotics Engineers develop more robust and autonomous robots.
Medical Image Analyst
Medical Image Analyst
Medical Image Analysts use imaging technologies to diagnose and treat medical conditions. Image stitching is an important technique for Medical Image Analysts who work with large medical images, such as MRI scans, that need to be combined into a single image. This course provides a practical introduction to the algorithms and techniques used for image stitching, which can help Medical Image Analysts develop more efficient and accurate workflows.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Image processing is a common task in software development, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of software applications, including image editing software, medical imaging software, and computer vision applications.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to provide insights for decision-making. Image processing is a common task in data analysis, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of data analysis tasks, including image classification, object detection, and image segmentation.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. Image processing is a common task in product development, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of products, including medical devices, consumer electronics, and industrial equipment.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. Image processing is a common task in project management, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of project management tasks, including project planning, risk assessment, and project evaluation.
Business Analyst
Business Analyst
Business Analysts use data and analysis to improve business processes. Image processing is a common task in business analysis, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of business analysis tasks, including market research, customer segmentation, and process improvement.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. Image processing is a common task in marketing, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of marketing tasks, including image editing, video production, and social media marketing.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation. Image processing is a common task in technical writing, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of technical writing tasks, including user manuals, technical reports, and white papers.
Sales Engineer
Sales Engineer
Sales Engineers provide technical support to customers. Image processing is a common task in sales engineering, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of sales engineering tasks, including product demonstrations, customer training, and technical support.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software and systems to ensure they meet quality standards. Image processing is a common task in quality assurance, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of quality assurance tasks, including image inspection, defect detection, and image analysis.
For more career information including salaries, visit:
OpenCourser.com/course/6elfl5/introduction
Reading list
We've selected seven 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
Introduction to Computer Vision.
Save
This textbook provides a comprehensive overview of computer vision algorithms and techniques. It covers topics such as image formation, feature detection, image segmentation, and object recognition. The book is written in a clear and concise style, and it includes numerous examples and exercises.
This textbook classic in the field of computer vision. It provides a comprehensive overview of the field, and it covers topics such as image formation, feature detection, image segmentation, and object recognition. The book is written in a clear and concise style, and it includes numerous examples and exercises.
This textbook provides a comprehensive overview of digital image processing. It covers topics such as image formation, image enhancement, image segmentation, and image compression. The book is written in a clear and concise style, and it includes numerous examples and exercises.
Save
This textbook provides a comprehensive overview of machine learning for computer vision applications. It covers topics such as supervised learning, unsupervised learning, and deep learning. The book is written in a clear and concise style, and it includes numerous examples and exercises.
This textbook provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and deep learning. The book is written in a clear and concise style, and it includes numerous examples and exercises.
Save
This textbook provides a comprehensive overview of deep learning using MATLAB. It covers topics such as supervised learning, unsupervised learning, and deep learning architectures. The book is written in a clear and concise style, and it includes numerous examples and exercises.
This textbook provides a comprehensive overview of computer vision using Python. It covers topics such as image processing, feature detection, and object recognition. The book is written in a clear and concise style, and it includes numerous examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6elfl5/introduction
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Level
Intermediate
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Institution
MathWorks
Instructors
Matt Rich
Amanda Wang
Megan Thompson
Brandon Armstrong
Isaac Bruss
Language
English
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This course belongs to a
collection
called
Computer Vision for Engineering and Science. It's comprised of three courses:
- Object Tracking and Motion Detection with Computer Vision
- Machine Learning for Computer Vision
- Introduction to Computer Vision (viewing)
Good to know
Introduces computer vision concepts, algorithms, and applications relevant to engineering and science
Taught by recognized experts in the field of computer vision
Utilizes MATLAB, a widely used software in engineering and science
Suitable for learners with prior image processing experience
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