Feature detection (computer vision): Difference between revisions

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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 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'': willwhether 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]] representation and one or several feature images are computed, often expressed in terms of local [[derivative]] operations.

Occasionally, when feature detection is [[computationally expensive]] and there are time constraints, a higher level algorithm may be used to guide the feature detection stage, so that only certain parts of the image are searched for features.

Very many featurecomputer vision algorithms use feature detection as the initial step, so as a result, a very large number of feature detectors have been developed. These vary widely in the kinds of feature detected, the computational complexity and the repeatability. At an overview level, these feature detectors can (with some overlap) be divided into the following groups:

== Types of image features ==