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  2. 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.

  3. Circle Hough Transform - Wikipedia

    en.wikipedia.org/wiki/Circle_Hough_Transform

    Accumulator array should be A[x,y,r] in the 3D space. Voting should be for each pixels, radius and theta A[x,y,r] += 1 The algorithm : For each A[a,b,r] = 0; Process the filtering algorithm on image Gaussian Blurring, convert the image to grayscale ( grayScaling), make Canny operator, The Canny operator gives the edges on image.

  4. A foolproof guide to image manipulation in Python with OpenCV

    www.aol.com/foolproof-guide-image-manipulation...

    OpenCV is a huge image and video processing library designed to work with many languages such as python, C/C++, Java, and more. It is the foundation for many of the applications you know that deal ...

  5. OpenCV - Wikipedia

    en.wikipedia.org/wiki/OpenCV

    OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly for real-time computer vision. [2] Originally developed by Intel, it was later supported by Willow Garage, then Itseez (which was later acquired by Intel [3]). The library is cross-platform and licensed as free and open-source software under Apache License ...

  6. Grayscale - Wikipedia

    en.wikipedia.org/wiki/Grayscale

    Grayscale images are distinct from one-bit bi-tonal black-and-white images, which, in the context of computer imaging, are images with only two colors: black and white (also called bilevel or binary images). Grayscale images have many shades of gray in between. Grayscale images can be the result of measuring the intensity of light at each pixel ...

  7. 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].

  8. Image segmentation - Wikipedia

    en.wikipedia.org/wiki/Image_segmentation

    The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, [ 3 ] such as color , intensity , or texture .

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