Feature detection (computer vision): Difference between revisions

Revision as of 06:02, 31 August 2016

Revision as of 13:13, 16 September 2016

Line 10:

== Definition of a feature ==

There is no universal or exact definition of what constitutes a feature, and the exact definition often depends on the problem or the type of application. Given that, a feature is defined as an "interesting" part of an [[Digital image|image]], and features are used as a starting point for many computer vision algorithms. Since features are used as the starting point and main primitives for subsequent algorithms, the overall algorithm will often only be as good as its feature detector. Consequently, the desirable property for a feature detector is ''[[repeatability]]'': whether or not the same feature will be detected in two or more different images of the same scene.

Feature detection is a low-level [[image processing]] operation. That is, it is usually performed as the first operation on an image, and examines every [[pixel]] to see if there is a feature present at that pixel. If this is part of a larger algorithm, then the algorithm will typically only examine the image in the region of the features. As a built-in pre-requisite to feature detection, the input image is usually smoothed by a [[Gaussian blur|Gaussian]] kernel in a [[scale space|scale-space representation]] and one or several feature images are computed, often expressed in terms of local [[Image Derivatives|image derivatives]] operations.

Line 28:

=== Corners / interest points ===

The terms corners and interest points are used somewhat interchangeably and refer to point-like features in an image, which have a local two dimensional structure. The name "Corner" arose since early algorithms first performed [[edge detection]], and then analysed the edges to find rapid changes in direction (corners). These algorithms were then developed so that explicit edge detection was no longer required, for instance by looking for high levels of [[curvature]] in the image gradient. It was then noticed that the so-called corners were also being detected on parts of the image which were not corners in the traditional sense (for instance a small bright spot on a dark background may be detected). These points are frequently known as interest points, but the term "corner" is used by tradition.

=== Blobs / regions of interest or interest points ===

Line 127:

* [[Feature learning]]

* [[Vectorization (image tracing)]]

*[[Feature selection]]

== References ==