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  2. Scale invariance - Wikipedia

    en.wikipedia.org/wiki/Scale_invariance

    The fluctuations at temperature T c are scale-invariant, and so the Ising model at this phase transition is expected to be described by a scale-invariant statistical field theory. In fact, this theory is the Wilson–Fisher fixed point , a particular scale-invariant scalar field theory .

  3. Scale-invariant feature transform - Wikipedia

    en.wikipedia.org/wiki/Scale-invariant_feature...

    Alternative methods for scale-invariant object recognition under clutter / partial occlusion include the following. RIFT [38] is a rotation-invariant generalization of SIFT. The RIFT descriptor is constructed using circular normalized patches divided into concentric rings of equal width and within each ring a gradient orientation histogram is ...

  4. Self-similarity - Wikipedia

    en.wikipedia.org/wiki/Self-similarity

    Scale invariance is an exact form of self-similarity where at any magnification there is a smaller piece of the object that is similar to the whole. For instance, a side of the Koch snowflake is both symmetrical and scale-invariant; it can be continually magnified 3x without changing shape. The non-trivial similarity evident in fractals is ...

  5. Power law - Wikipedia

    en.wikipedia.org/wiki/Power_law

    This property of () follows directly from the requirement that () be asymptotically scale invariant; thus, the form of () only controls the shape and finite extent of the lower tail. For instance, if L ( x ) {\displaystyle L(x)} is the constant function, then we have a power law that holds for all values of x {\displaystyle x} .

  6. Feature engineering - Wikipedia

    en.wikipedia.org/wiki/Feature_engineering

    MCMD is designed to output two types of class labels (scale-variant and scale-invariant clustering), and: is computationally robust to missing information, can obtain shape- and scale-based outliers, and can handle high-dimensional data effectively. Coupled matrix and tensor decompositions are popular in multi-view feature engineering. [9]

  7. Jeffreys prior - Wikipedia

    en.wikipedia.org/wiki/Jeffreys_prior

    Using tools from information geometry, the Jeffreys prior can be generalized in pursuit of obtaining priors that encode geometric information of the statistical model, so as to be invariant under a change of the coordinate of parameters. [9] A special case, the so-called Weyl prior, is defined as a volume form on a Weyl manifold. [10]

  8. Renormalization group - Wikipedia

    en.wikipedia.org/wiki/Renormalization_group

    The renormalization group is intimately related to scale invariance and conformal invariance, symmetries in which a system appears the same at all scales (self-similarity), [a] where under the fixed point of the renormalization group flow the field theory is conformally invariant. As the scale varies, it is as if one is decreasing (as RG is a ...

  9. Scale-invariant feature operator - Wikipedia

    en.wikipedia.org/wiki/Scale-invariant_feature...

    The scale-invariant feature operator (SFOP) is based on two theoretical concepts: spiral model [2] feature operator [3] Desired properties of keypoint detectors: Invariance and repeatability for object recognition; Accuracy to support camera calibration; Interpretability: Especially corners and circles, should be part of the detected keypoints ...