Feature detection (computer vision): Difference between revisions

Revision as of 17:42, 1 February 2020

Revision as of 19:57, 12 May 2020

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== 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 thatNevertheless, a feature is typically 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.