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  2. Integral channel feature - Wikipedia

    en.wikipedia.org/wiki/Integral_channel_feature

    The simple MATLAB implementation below shows how color channels and grayscale channel can be extracted from an input image. I = imread ( 'I_RGB.png' ); % input color image % Output_image = color_channel(I), % where color channel could be red, green or blue.

  3. Edge detection - Wikipedia

    en.wikipedia.org/wiki/Edge_detection

    Edge detection includes a variety of mathematical methods that aim at identifying edges, defined as curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.

  4. Image derivative - Wikipedia

    en.wikipedia.org/wiki/Image_derivative

    Image derivatives can be computed by using small convolution filters of size 2 × 2 or 3 × 3, such as the Laplacian, Sobel, Roberts and Prewitt operators. [1] However, a larger mask will generally give a better approximation of the derivative and examples of such filters are Gaussian derivatives [2] and Gabor filters. [3]

  5. Digital image processing - Wikipedia

    en.wikipedia.org/wiki/Digital_image_processing

    Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s, at Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone ...

  6. MATLAB - Wikipedia

    en.wikipedia.org/wiki/MATLAB

    MATLAB (an abbreviation of "MATrix LABoratory" [22]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks.MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.

  7. Convolution power - Wikipedia

    en.wikipedia.org/wiki/Convolution_power

    If x is itself suitably differentiable, then from the properties of convolution, one has {} = () = {()}where denotes the derivative operator. Specifically, this holds if x is a compactly supported distribution or lies in the Sobolev space W 1,1 to ensure that the derivative is sufficiently regular for the convolution to be well-defined.