We will compare and contrast the terms image processing, computer vision and robotic vision — they have much in common but there are some subtle but important distinctions. When it comes to interpreting an image we typically try to find and describe regions, lines and interest points.
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The relationship between world coordinates, image coordinates and camera spatial velocity has some interesting ramifications. Some very different camera motions cause identical motion of points in the image, and some camera motions leads to no change in the image at all in some parts of the image. Let’s explore at these phenomena and how we […]
The image Jacobian depends not only on the image plane coordinates but also the distance from the camera to the points of interest. If this distance is not known, what can we do? Let’s look at how we can determine this distance, and how the optical flow equation can be rearranged to convert from observed […]
We summarise the important points from this lecture.
We recap the important points from this masterclass.
Frequently we want a trajectory that moves smoothly through a series of points without stopping.
An image contains a huge amount of pixel data, and a video stream is a massive flow of pixel data. Typically a robot has only a few inputs, the position or velocity of its joints. How do we go from all that camera data to the small amount of data the robot really needs?
A critical part of a visual servoing system is establishing correspondence between points in the scene observed by the camera, and points in our desired image of the scene.
Let’s recap the basics of homogeneous coordinates to represent points on a plane.
When a camera moves in the world, points in the image move in a very specific way. The image plane or pixel velocity is a function of the camera’s motion and the position of the points in the world. This is known as optical flow. Let’s explore the link between camera and image motion.