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The image circle is the cross section of the cone of light transmitted by a lens or series of lenses onto the image plane. When this light strikes a perpendicular target such as photographic film or a digital camera sensor, it forms a circle of light – the image circle. Various sensor aspect ratios may be used which all fit inside the same ...
Adjust image (functions) Text recognition Scripting support CAD features. NAME Export universal DXF Available in AutoCAD application Curve recognition Circle ...
Typical values for the PSNR in lossy image and video compression are between 30 and 50 dB, provided the bit depth is 8 bits, where higher is better. The processing quality of 12-bit images is considered high when the PSNR value is 60 dB or higher. [3] [4] For 16-bit data typical values for the PSNR are between 60 and 80 dB.
Raster graphic image. In computer graphics, rasterisation (British English) or rasterization (American English) is the task of taking an image described in a vector graphics format (shapes) and converting it into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes).
For example, a 28 mm lens delivers a moderately wide-angle FOV on a 35 mm format full-frame camera, but on a camera with a 1.6 crop factor, an image made with the same lens will have the same field of view that a full-frame camera would make with a ~45 mm lens (28 × 1.6 = 44.8). This narrowing of the FOV is a disadvantage to photographers when ...
A circle of radius 23 drawn by the Bresenham algorithm. In computer graphics, the midpoint circle algorithm is an algorithm used to determine the points needed for rasterizing a circle. It is a generalization of Bresenham's line algorithm. The algorithm can be further generalized to conic sections. [1] [2] [3]
The circle Hough Transform (CHT) is a basic feature extraction technique used in digital image processing for detecting circles in imperfect images. The circle candidates are produced by “voting” in the Hough parameter space and then selecting local maxima in an accumulator matrix. It is a specialization of the Hough transform.
Where S(x,y) is the original image, and the filter mask is m by n pixels. Each pixel of the output image at point (x,y) is given by the product of the pixels within the geometric mean mask raised to the power of 1/mn. For example, using a mask size of 3 by 3, pixel (x,y) in the output image will be the product of S(x,y) and all 8 of its ...