Bookmark and Share

Computer Vision Metrics: Chapter Six

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.

Interest Point Detector and Feature Descriptor Survey

“Who makes all these?”
—Jack Sparrow, Pirates of the Caribbean

Many algorithms for computer vision rely on locating interest points, or keypoints in each image, and calculating a feature description from the pixel region surrounding the interest point. This is in contrast to methods such as correlation, where a larger rectangular pattern is stepped over the image at pixel intervals and the correlation is measured at each location. The interest point is the anchor point, and often provides the scale, rotational, and illumination invariance attributes for the descriptor; the descriptor adds more detail and more invariance attributes. Groups of interest points and descriptors together describe the actual objects.

However, there are many methods and variations in feature description. Some methods use features that are not anchored at interest points, such as polygon shape descriptors, computed over larger segmented polygon-shaped structures or regions in an image. Other methods use interest points only, without using feature descriptors at all. And some methods use feature descriptors only, computed across a regular grid on the image, with no interest points at all.

Terminology varies across the literature. In some discussions, interest points may be referred to as keypoints. The algorithms used to find the interest points maybe referred to as detectors, and the algorithms used to describe the features may be called descriptors. We use the terminology interchangeably in this work. Keypoints may be considered a set composed of (1) interest points, (2) corners, (3) edges or contours, and (4) larger features or regions such as blobs; see Figure 6-1. This chapter surveys the various methods for designing local interest point detectors and feature descriptors.

Figure 6-1. Types of keypoints, including corners and interest points. (Left to right) Step, roof, corner, line or edge, ridge or contour, maxima region

Interest Point Tuning

What is a good keypoint for a given application? Which ones are most useful? Which ones should be ignored? Tuning the detectors is not simple. Each detector has different parameters to tune for best results on a given image, and each image presents different challenges regarding lighting, contrast, and image pre-processing. Additionally, each...