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A common definition used in digital image processing (image analysis) for characterizing 2-D shapes is: Roundness = Perimeter 2 / 4 π × Area . This ratio will be 1 for a circle and greater than 1 for non-circular shapes. Another definition is the inverse of that: Roundness = 4 π × Area / Perimeter 2 ,
The dimensionless quantities often represent the degree of deviation from an ideal shape, such as a circle, sphere or equilateral polyhedron. [1] Shape factors are often normalized, that is, the value ranges from zero to one. A shape factor equal to one usually represents an ideal case or maximum symmetry, such as a circle, sphere, square or cube.
Like any resampling operation, changing image size and bit depth are lossy in all cases of downsampling, such as 30-bit to 24-bit or 24-bit to 8-bit palette-based images. While increasing bit depth is usually lossless, increasing image size can introduce aliasing or other undesired artifacts.
In remote sensing, ground sample distance (GSD) in a digital photo of the ground from air or space is the distance between pixel centers measured on the ground. For example, in an image with a one-meter GSD, adjacent pixels image locations are 1 meter apart on the ground. [1]
Storage aspect ratio is the ratio of the image width to height in pixels, and can be easily calculated from the video file. Display aspect ratio is the ratio of image width to height (in a unit of length such as centimeters or inches) when displayed on screen, and is calculated from the combination of pixel aspect ratio and storage aspect ratio.
An image scaled with nearest-neighbor scaling (left) and 2×SaI scaling (right) In computer graphics and digital imaging, image scaling refers to the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement.
In image processing, computer vision and related fields, an image moment is a certain particular weighted average of the image pixels' intensities, or a function of such moments, usually chosen to have some attractive property or interpretation. Image moments are useful to describe objects after segmentation.
The image of φ is called an immersed submanifold. More specifically, for m = 3, which means that the ambient Euclidean space is ℝ 3, the induced metric tensor is called the first fundamental form. Suppose that φ is an immersion onto the submanifold M ⊂ R m.
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