Scale-space axioms

<p>In <a href="/facts/Image_processing/zenalNCf">image processing</a> and <a href="/facts/Computer_vision/Tl2Yyk66">computer vision</a>, a <a href="/facts/Scale_space/XJNvTH1N">scale space</a> framework can be used to represent an image as a family of gradually smoothed images.  This framework is very general and a variety of <a href="/facts/Scale_space_representation/XJNvTH1N">scale space representations</a> exist.  A typical approach for choosing a particular type of scale space representation is to establish a set of scale-space axioms, describing basic properties of the desired scale-space representation and often chosen so as to make the representation useful in practical applications. Once established, the axioms narrow the possible scale-space representations to a smaller class, typically with only a few free parameters.
</p><p>A set of standard scale space axioms, discussed below, leads to the linear Gaussian scale-space, which is the most common type of scale space used in image processing and computer vision.
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Scale-space axioms open-in-new

Scale-space axioms