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  2. Line integral convolution - Wikipedia

    en.wikipedia.org/wiki/Line_integral_convolution

    Image of the Large Magellanic Cloud, one of the nearest galaxies to our Milky Way, created with LIC. In scientific visualization, line integral convolution (LIC) is a method to visualize a vector field (such as fluid motion) at high spatial resolutions. [1] The LIC technique was first proposed by Brian Cabral and Leith Casey Leedom in 1993. [2]

  3. Normalization (image processing) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(image...

    max is the maximum value for color level in the input image within the selected kernel. min is the minimum value for color level in the input image within the selected kernel. [4] Local contrast stretching considers each range of color palate in the image (R, G, and B) separately, providing a set of minimum and maximum values for each color palate.

  4. Matplotlib - Wikipedia

    en.wikipedia.org/wiki/Matplotlib

    Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.

  5. Triangulation (computer vision) - Wikipedia

    en.wikipedia.org/wiki/Triangulation_(computer...

    In the following, it is assumed that triangulation is made on corresponding image points from two views generated by pinhole cameras. The ideal case of epipolar geometry. A 3D point x is projected onto two camera images through lines (green) which intersect with each camera's focal point, O 1 and O 2. The resulting image points are y 1 and y 2.

  6. Image moment - Wikipedia

    en.wikipedia.org/wiki/Image_moment

    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.

  7. Kernel (image processing) - Wikipedia

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

    For example, attempting to read a pixel 3 units outside an edge reads one 3 units inside the edge instead. Crop / Avoid overlap Any pixel in the output image which would require values from beyond the edge is skipped. This method can result in the output image being slightly smaller, with the edges having been cropped.

  8. Watershed (image processing) - Wikipedia

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

    This flooding process is performed on the gradient image, i.e. the basins should emerge along the edges. Normally this will lead to an over-segmentation of the image, especially for noisy image material, e.g. medical CT data. Either the image must be pre-processed or the regions must be merged on the basis of a similarity criterion afterwards.

  9. Image rectification - Wikipedia

    en.wikipedia.org/wiki/Image_rectification

    If the images to be rectified are taken from camera pairs without geometric distortion, this calculation can easily be made with a linear transformation.X & Y rotation puts the images on the same plane, scaling makes the image frames be the same size and Z rotation & skew adjustments make the image pixel rows directly line up [citation needed].