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  2. Kernel (image processing) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(image_processing)

    2D Convolution Animation. Convolution is the process of adding each element of the image to its local neighbors, weighted by the kernel. This is related to a form of mathematical convolution. The matrix operation being performed—convolution—is not traditional matrix multiplication, despite being similarly denoted by *.

  3. Multidimensional discrete convolution - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_discrete...

    In signal processing, multidimensional discrete convolution refers to the mathematical operation between two functions f and g on an n-dimensional lattice that produces a third function, also of n-dimensions. Multidimensional discrete convolution is the discrete analog of the multidimensional convolution of functions on Euclidean space.

  4. Convolution - Wikipedia

    en.wikipedia.org/wiki/Convolution

    Download as PDF; Printable version; ... the convolution formula can be described as the area under the function ... Discrete 2D Convolution Animation.

  5. Nine-point stencil - Wikipedia

    en.wikipedia.org/wiki/Nine-point_stencil

    Both are isotropic forms of discrete Laplacian, [8] and in the limit of small Δx, they all become equivalent, [11] as Oono-Puri being described as the optimally isotropic form of discretization, [8] displaying reduced overall error, [2] and Patra-Karttunen having been systematically derived by imposing conditions of rotational invariance, [9 ...

  6. Convolutional layer - Wikipedia

    en.wikipedia.org/wiki/Convolutional_layer

    In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry.

  7. Dirichlet kernel - Wikipedia

    en.wikipedia.org/wiki/Dirichlet_kernel

    The convolution of D n (x) with any function f of period 2 π is the nth-degree Fourier series approximation to f, i.e., we have () = () = = ^ (), where ^ = is the k th Fourier coefficient of f. This implies that in order to study convergence of Fourier series it is enough to study properties of the Dirichlet kernel.

  8. Poisson kernel - Wikipedia

    en.wikipedia.org/wiki/Poisson_kernel

    That the boundary value of u is f can be argued using the fact that as r → 1, the functions P r (θ) form an approximate unit in the convolution algebra L 1 (T). As linear operators, they tend to the Dirac delta function pointwise on L p (T). By the maximum principle, u is the only such harmonic function on D.

  9. List of convolutions of probability distributions - Wikipedia

    en.wikipedia.org/wiki/List_of_convolutions_of...

    In probability theory, the probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density ...