Vector magnitude in the context of Triangle inequality


Vector magnitude in the context of Triangle inequality

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⭐ Core Definition: Vector magnitude

In mathematics, a norm is a function from a real or complex vector space to the non-negative real numbers that behaves in certain ways like the distance from the origin: it commutes with scaling, obeys a form of the triangle inequality, and zero is only at the origin. In particular, the Euclidean distance in a Euclidean space is defined by a norm on the associated Euclidean vector space, called the Euclidean norm, the 2-norm, or, sometimes, the magnitude or length of the vector. This norm can be defined as the square root of the inner product of a vector with itself.

A seminorm satisfies the first two properties of a norm but may be zero for vectors other than the origin. A vector space with a specified norm is called a normed vector space. In a similar manner, a vector space with a seminorm is called a seminormed vector space.

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Vector magnitude in the context of Acutance

In photography, acutance describes a subjective perception of visual acuity that is related to the edge contrast of an image. Acutance is related to the magnitude of the gradient of brightness. Due to the nature of the human visual system, an image with higher acutance appears sharper even though an increase in acutance does not increase real resolution.

Historically, acutance was enhanced chemically during development of a negative (high acutance developers), or by optical means in printing (unsharp masking). In digital photography, onboard camera software and image postprocessing tools such as Photoshop or GIMP offer various sharpening facilities, the most widely used of which is known as "unsharp mask" because the algorithm is derived from the eponymous analog processing method.

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