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Feature detection is a process by which the nervous system sorts or filters complex natural stimuli in order to extract behaviorally relevant cues that have a high probability of being associated with important objects or organisms in their environment, as opposed to irrelevant background or noise.
Feature detection or feature detectors may refer to: Feature detection (nervous system), a biological process for interpreting sensory input Orientation column, also known as a "feature detection column" Feature detection (computer vision), methods for finding parts of an image relevant to a computational task
Feature detection includes methods for computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the form of isolated points, continuous curves or connected regions.
In machine learning (ML), feature learning or representation learning [2] is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data.
Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented by Ethan Rublee et al. in 2011, [1] that can be used in computer vision tasks like object recognition or 3D reconstruction.
The Hessian affine region detector is a feature detector used in the fields of computer vision and image analysis. Like other feature detectors, the Hessian affine detector is typically used as a preprocessing step to algorithms that rely on identifiable, characteristic interest points .
Features from accelerated segment test (FAST) is a corner detection method, which could be used to extract feature points and later used to track and map objects in many computer vision tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006. [ 1 ]
In computer vision, speeded up robust features (SURF) is a local feature detector and descriptor, with patented applications. It can be used for tasks such as object recognition , image registration , classification , or 3D reconstruction .
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