Course Summary
This course is an introduction to the basics of computer vision, including image and video processing, feature detection and matching, and machine learning for computer vision.Key Learning Points
- Learn the fundamentals of computer vision and its applications
- Understand image and video processing techniques
- Gain hands-on experience with feature detection and matching
Related Topics for further study
- Computer Vision Applications
- Image and Video Processing
- Feature Detection and Matching
- Machine Learning for Computer Vision
- Deep Learning
Learning Outcomes
- Understand the fundamentals of computer vision
- Gain the skills needed to develop computer vision applications
- Be able to apply machine learning techniques to computer vision problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of linear algebra and calculus
- Familiarity with programming in Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Computer Vision: Foundations and Applications
- Applied AI with DeepLearning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Fei-Fei Li
Related Books
Description
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks.
Knowledge
- Understand what computer vision is and its goals
- Identify some of the key application areas of computer vision
- Understand the digital imaging process
- Apply mathematical techniques to complete computer vision tasks
Outline
- Computer Vision Overview
- Meet Jeff Bier
- Meet Jungsong Yuan, Ph.D.
- What is Computer Vision?
- Why Computer Vision?
- Related Fields of Computer Vision
- Relevant Fields
- Computer Programming & Computer Vision
- Computer Vision Awareness
- Timelines & Milestones
- Computer Vision Progression
- Computer Vision Applications
- CV Applications
- CV Impact in the Field of Augmented Reality
- Resources (Optional): Computer Vision Overview
- REQUIRED - MATLAB Resources
- What is Computer Vision?
- Related Fields of Computer Vision
- MATLAB Basics
- Color, Light, & Image Formation
- Light Sources
- Pinhole Camera Model
- Digital Camera
- Color Theory
- Resources (Optional): Color, Light, & Image Formation
- Light Sources
- Pinhole Camera Model
- Digital Camera
- Low-, Mid- & High-Level Vision
- Three-Level Paradigm
- Low-, Mid-, High-Level Vision
- Low-Level Vision
- Mid-Level Vision
- High-Level Vision
- Resources (Optional): Low-, Mid- and High-Level Vision
- Three-Level Paradigm
- Low-Level Vision
- Mathematics for Computer Vision
- Mathematic Skills
- Mathematical Preliminaries
- Linear Algebra
- Calculus
- Probability Theory
- Algorithms
- Using Algorithms
- Aligning RGB channels
- Resources (Optional): Mathematics for Computer Vision
- Computer Vision Basics - Key Takeaways
- Algorithms
Summary of User Reviews
Learn the basics of computer vision with this course from Coursera. Users have given positive reviews, highlighting the practicality of the course. However, some users mention that the course could be more challenging.Key Aspect Users Liked About This Course
PracticalityPros from User Reviews
- Good introduction to computer vision
- Clear and concise explanations
- Interactive quizzes and programming assignments
- Instructor is knowledgeable and responsive to questions
Cons from User Reviews
- Course may be too basic for some
- Not enough emphasis on mathematical concepts
- Some technical issues with the programming assignments
Radhakrishna Dasari
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