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  2. Color difference - Wikipedia

    en.wikipedia.org/wiki/Color_difference

    As most definitions of color difference are distances within a color space, the standard means of determining distances is the Euclidean distance.If one presently has an RGB (red, green, blue) tuple and wishes to find the color difference, computationally one of the easiest is to consider R, G, B linear dimensions defining the color space.

  3. Line drawing algorithm - Wikipedia

    en.wikipedia.org/wiki/Line_drawing_algorithm

    Two rasterized lines. The colored pixels are shown as circles. Above: monochrome screening; below: Gupta-Sproull anti-aliasing; the ideal line is considered here as a surface. In computer graphics, a line drawing algorithm is an algorithm for approximating a line segment on discrete graphical media, such as pixel-based displays and printers.

  4. Color quantization - Wikipedia

    en.wikipedia.org/wiki/Color_quantization

    The three color channels are usually red, green, and blue, but another popular choice is the Lab color space, in which Euclidean distance is more consistent with perceptual difference. The most popular algorithm by far for color quantization, invented by Paul Heckbert in 1979, is the median cut algorithm.

  5. Euclidean distance - Wikipedia

    en.wikipedia.org/wiki/Euclidean_distance

    The distance from a point to a plane in three-dimensional Euclidean space [7] The distance between two lines in three-dimensional Euclidean space [8] The distance from a point to a curve can be used to define its parallel curve, another curve all of whose points have the same distance to the given curve. [9]

  6. Image subtraction - Wikipedia

    en.wikipedia.org/wiki/Image_subtraction

    Image subtraction or pixel subtraction or difference imaging is an image processing technique whereby the digital numeric value of one pixel or whole image is subtracted from another image, and a new image generated from the result.

  7. Bundle adjustment - Wikipedia

    en.wikipedia.org/wiki/Bundle_adjustment

    where (,) is the predicted projection of point on image and (,) denotes the Euclidean distance between the image points represented by vectors and . Because the minimum is computed over many points and many images, bundle adjustment is by definition tolerant to missing image projections, and if the distance metric is chosen reasonably (e.g ...

  8. Bilateral filter - Wikipedia

    en.wikipedia.org/wiki/Bilateral_filter

    Consider a pixel located at (,) that needs to be denoised in image using its neighbouring pixels and one of its neighbouring pixels is located at (,). Then, assuming the range and spatial kernels to be Gaussian kernels , the weight assigned for pixel ( k , l ) {\displaystyle (k,l)} to denoise the pixel ( i , j ) {\displaystyle (i,j)} is given by

  9. Pixel - Wikipedia

    en.wikipedia.org/wiki/Pixel

    The pixel scale used in astronomy is the angular distance between two objects on the sky that fall one pixel apart on the detector (CCD or infrared chip). The scale s measured in radians is the ratio of the pixel spacing p and focal length f of the preceding optics, s = p / f .