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  2. H-maxima transform - Wikipedia

    en.wikipedia.org/wiki/H-maxima_transform

    Second, all local maxima that have height lower or equal to a given threshold are suppressed. The height f of the remaining maxima is decreased by h {\displaystyle h} . The h-maxima transform is defined as the reconstruction by dilation of f {\displaystyle f} from f − h {\displaystyle f-h} :

  3. Local property - Wikipedia

    en.wikipedia.org/wiki/Local_property

    Perhaps the best-known example of the idea of locality lies in the concept of local minimum (or local maximum), which is a point in a function whose functional value is the smallest (resp., largest) within an immediate neighborhood of points. [1]

  4. Scale-invariant feature transform - Wikipedia

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

    Once DoG images have been obtained, keypoints are identified as local minima/maxima of the DoG images across scales. This is done by comparing each pixel in the DoG images to its eight neighbors at the same scale and nine corresponding neighboring pixels in each of the neighboring scales.

  5. Angular resolution - Wikipedia

    en.wikipedia.org/wiki/Angular_resolution

    Modern image processing techniques including deconvolution of the point spread function allow resolution of binaries with even less angular separation. Using a small-angle approximation , the angular resolution may be converted into a spatial resolution , Δ ℓ , by multiplication of the angle (in radians) with the distance to the object.

  6. Global optimization - Wikipedia

    en.wikipedia.org/wiki/Global_optimization

    Global optimization is distinguished from local optimization by its focus on finding the minimum or maximum over the given set, as opposed to finding local minima or maxima. Finding an arbitrary local minimum is relatively straightforward by using classical local optimization methods. Finding the global minimum of a function is far more ...

  7. Quasi-Newton method - Wikipedia

    en.wikipedia.org/wiki/Quasi-Newton_method

    In numerical analysis, a quasi-Newton method is an iterative numerical method used either to find zeroes or to find local maxima and minima of functions via an iterative recurrence formula much like the one for Newton's method, except using approximations of the derivatives of the functions in place of exact derivatives.

  8. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    The Lagrange multiplier theorem states that at any local maximum (or minimum) of the function evaluated under the equality constraints, if constraint qualification applies (explained below), then the gradient of the function (at that point) can be expressed as a linear combination of the gradients of the constraints (at that point), with the ...

  9. Image resolution - Wikipedia

    en.wikipedia.org/wiki/Image_resolution

    Image resolution is the level of detail of an image. The term applies to digital images, film images, and other types of images. "Higher resolution" means more image detail. Image resolution can be measured in various ways. Resolution quantifies how close lines can be to each other and still be visibly resolved. Resolution units can be tied to ...