Stereo Vision, Dense Motion & Tracking
Offered By: University at Buffalo via Coursera
Course Description
Overview
This course enables learners to develop 3D vision applications using a stereo imaging system. They are introduced to stereo vision theory, dense motion and visual tracking. They are able to discuss techniques used to obtain the 3D structure of objects. Topics include epipolar geometry, optical flow, structure from motion, multi-object tracking, 3D vision and visual odometry.
This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables).
Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.
This is the third course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0.
* A free license to install MATLAB for the duration of the course is available from MathWorks.
This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables).
Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.
This is the third course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0.
* A free license to install MATLAB for the duration of the course is available from MathWorks.
Syllabus
Stereo Vision
-In this module, we will discuss the fundamentals and applications of stereo vision and multiple camera systems.
Dense Motion & SFM
-In this module, we will discuss motion perception, optical flow, applications of dense motion, and structure from motion.
Visual Tracking
-This module provides information about visual tracking, including: object tracking, motion models, inferences used in tracking, and multi-object tracking.
3D Vision
-This module discusses the active methods used to develop 3D vision, as well as the applications of 3D vision.
-In this module, we will discuss the fundamentals and applications of stereo vision and multiple camera systems.
Dense Motion & SFM
-In this module, we will discuss motion perception, optical flow, applications of dense motion, and structure from motion.
Visual Tracking
-This module provides information about visual tracking, including: object tracking, motion models, inferences used in tracking, and multi-object tracking.
3D Vision
-This module discusses the active methods used to develop 3D vision, as well as the applications of 3D vision.
Taught by
Radhakrishna Dasari and Junsong Yuan
Related Courses
Advanced Machine LearningThe Open University via FutureLearn On-Ramp to AP* Calculus
Weston High School via edX Preparing for the AP* Calculus AB and BC Exams
University of Houston System via Coursera Calculus: Single Variable Part 4 - Applications
University of Pennsylvania via Coursera Applications of Calculus
Boxplay via FutureLearn