Detecting Interest Points

In computer vision, the concept of interest points, also called keypoints or feature points, has been largely used to solve many problems in object recognition, image registration, visual tracking, 3D reconstruction, and more. Instead, of evaluating an image as a whole, it could be better to select points that can contain information that perform local analysis on the point to achieve results to apply local or globally. This approach works well as long as a sufficient number of such points are detected in the images of interest and as long as these points are distinct and stable features that can be accurately localized.

Because they are used to analyze image content, feature points should ideally be detected at the same scene or object location no matter from which viewpoint, scale, or orientation the image was taken. View invariance is a very desirable...

Detecting corners in an image

When searching for interesting feature points in images, corners come out as an interesting solution. They are features that can be easily localized in an image and they should abound in scenes of man-made objects (where they are produced by walls, doors, windows, tables, and so on). Corners are also interesting because they are two-dimensional features that can be accurately localized (even at sub-pixel accuracy), as they are at the junction of two or more edges. This is in contrast to points located on a uniform area or on the contour of an object and points that would be difficult to repeatedly localize precisely on other images of the same object. The Harris feature detector is a classical approach to detect corners in an image. We will explore this operator in this recipe.

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Detecting features quickly

The Harris operator proposed a formal mathematical definition for corners (or, more generally, interest points) based on the rate of intensity changes in two perpendicular directions. Although this constitutes a sound definition, it requires the computation of the image derivatives, which is a costly operation, especially considering the fact that interest point detection is often just the first step in a more complex algorithm.

In this recipe, we present another feature point operator, called FAST (short for Features from Accelerated Segment Test). This one has been specifically designed to allow quick detection of interest points in an image; the decision to accept or not to accept a keypoint is based on only a few pixel comparisons.

How to do it...

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Detecting scale-invariant features

The view invariance of feature detection was presented as an important concept in the introduction of this chapter. While orientation invariance, which is the ability to detect the same points even if an image is rotated, has been relatively well handled by the simple feature point detectors that have been presented so far, changes for invariance to scale are more difficult to achieve. To address this problem, the concept of scale-invariant features has been introduced in computer vision.

The idea here is to not only have a consistent detection of keypoints no matter at which scale an object is pictured, but to also have a scale factor associated with each of the detected feature points. Ideally, for the same object point featured at two different scales on two different images, the ratio of the two computed scale factors should correspond to...

Detecting FAST features at multiple scales

FAST has been introduced as a quick way to detect keypoints in an image. With SURF and SIFT, the emphasis was on designing scale-invariant features. More recently, new interest point detectors have been introduced with the objective of achieving both fast detection and invariance-to-scale changes. This recipe presents the Binary Robust Invariant Scalable Keypoints (BRISK) detector. It is based on the FAST feature detector that we described in a previous recipe of this chapter. Another detector, called ORB (Oriented FAST and Rotated BRIEF), will also be discussed at the end of this recipe. These two feature point detectors constitute an excellent solution when fast and reliable image matching is required. They are especially efficient when they are used in conjunction with their associated binary descriptors, as will be discussed in Chapter...