Module 2: Computer Vision¶
Unit 1: A Survey of Computer Vision¶
Goal: Understand the motivation and princples of utilizing computer vision as a means of perception for autonomous systems. Survey several common applications and current research trends in the field of computer vision from a robotics perspective.
Unit 2: Kernels, Edges, Thresholds¶
Goal: This unit introduces the fundamentals of image analysis for machine vision. We will treat images and videos as two-dimensional signals, allowing us to apply signal processing techniques to detect and interpret features in a camera stream on an embedded platform in real-time, using the OpenCV library.
Unit 3: Regression, Line Parameterization¶
Goal: Learn how to detect an LED rope present in a camera image by applying linear regression on thresholded pixel values, as the first step in enabling the drone to follow an LED track.
Unit 4: Color Segmentation¶
Goal: Understand how to detect and segment a specific, narrow range of colors within an image.
Unit 5: Integration with Quadrotor¶
Goal: In order to utilize computer vision techniques for UAV control, we need to be able to pass information between our vision processes (i.e. ROS nodes) and our command/control processes in a parameterized format. The goal of this unit is to develop such ROS messages.
Unit 6: Advanced Topics¶
Goal: Understand the theory behind some of the “black box” algorithms we have used thus far as well as the frontiers of computer vision research
[AR Tag Theory]
[Optical Flow Theory]