Feature detection in computer vision - Revision history

wikademia>Eme at 22:28, 24 April 2011

2011-04-24T22:28:38Z

← Older revision		Revision as of 22:28, 24 April 2011
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	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.		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.

			==See also==
			* [[List of computer vision conferences]]

	[[Category:Computer vision]]		[[Category:Computer vision]]

wikademia>Eme at 22:25, 24 April 2011

2011-04-24T22:25:59Z

← Older revision		Revision as of 22:25, 24 April 2011
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	In computer vision and image processing the concept of feature detection refers to methods that aim at 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 [[computer vision]] and [[image processing]] the concept of [[feature detection]] refers to methods that aim at 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.

	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 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.		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 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.
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	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 kernel in a scale-space representation and one or several feature images are computed, often expressed in terms of local derivative operations.		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 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.		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.

	[[Category:Computer vision]]		[[Category:Computer vision]]

wikademia>Eme: from http://en.wikipedia.org/wiki/Feature_detection_(computer_vision)

2011-04-24T22:24:10Z

from http://en.wikipedia.org/wiki/Feature_detection_(computer_vision)

New page

In computer vision and image processing the concept of feature detection refers to methods that aim at 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.

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 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 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.

[[Category:Computer vision]]