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.
Search Results for: image features
For a camera moving through the environment we frequently wish to track particular world points from one frame to the next. We’ll do a quick introduction to the very large field of feature detection and matching using Harris corner features.
We use MATLAB and some Toolbox functions to find corresponding points between two images using SURF features.
Let’s recap the important points from the topics we have covered about image features, blobs, connectivity analysis, and blob parameters such as centroid position, area, bounding box, moments, equivalent ellipse, and perimeter.
When matching points between scenes with large different viewpoints we need to account for varying image size and rotation. SIFT features are a powerful way to achieve this.
A robot can use a camera to capture an image of the world. The image contains millions of pixels, but the value of each pixel is not particularly informative about what’s present in the scene. We need a more concise or ‘higher level’ way to represent the information, and this is what we refer to […]
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?
Many scenes, particularly of man-made environments, have very dominant lines due to the edges of objects. The Hough transform is a common technique for finding dominant lines, and we ill examine how it works and apply it to a real image.