Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the sca. DefinitionThe notion of scale space applies to signals of arbitrary numbers of variables. The most common case in the. .
The motivation for generating a scale-space representation of a given data set originates from the basic observation that real-world objects are composed of different structures at different . This implies that real-w. .
At any scale in scale space, we can apply local derivative operators to the scale-space representation: Due to the commutative property between the derivative operator and the Gaussian smoothing. .
Following the idea of expressing visual operations in terms of differential invariants computed at multiple scales using Gaussian derivative operators, we can express an from the set of points that.
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