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<title>Basics - Boost.GIL documentation</title>
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<div class="section" id="basics">
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<h1>Basics</h1>
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<p>Here are basic concepts that might help to understand documentation
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written in this folder:</p>
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<div class="section" id="convolution">
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<h2>Convolution</h2>
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<p>The simplest way to look at this is “tweaking the input so that it would
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look like the shape provided”. What exact tweaking is applied depends on
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the kernel.</p>
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</div>
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<hr class="docutils" />
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<div class="section" id="filters-kernels-weights">
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<h2>Filters, kernels, weights</h2>
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<p>Those three words usually mean the same thing, unless context is clear
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about a different usage. Simply put, they are matrices, that are used to
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achieve certain effects on the image. Lets consider a simple one, 3 by 3
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Scharr filter</p>
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<p><code class="docutils literal notranslate"><span class="pre">ScharrX</span> <span class="pre">=</span> <span class="pre">[1,0,-1][1,0,-1][1,0,-1]</span></code></p>
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<p>The filter above, when convolved with a single channel image
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(intensity/luminance strength), will produce a gradient in X
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(horizontal) direction. There is filtering that cannot be done with a
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kernel though, and one good example is median filter (mean is the
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arithmetic mean, whereas median will be the center element of a sorted
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array).</p>
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<div class="section" id="derivatives">
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<h2>Derivatives</h2>
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<p>A derivative of an image is a gradient in one of two directions: x
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(horizontal) and y (vertical). To compute a derivative, one can use
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Scharr, Sobel and other gradient filters.</p>
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</div>
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<hr class="docutils" />
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<div class="section" id="curvature">
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<h2>Curvature</h2>
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<p>The word, when used alone, will mean the curvature that would be
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generated if values of an image would be plotted in 3D graph. X and Z
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axises (which form horizontal plane) will correspond to X and Y indices
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of an image, and Y axis will correspond to value at that pixel. By
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little stretch of an imagination, filters (another names are kernels,
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weights) could be considered an image (or any 2D matrix). A mean filter
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would draw a flat plane, whereas Gaussian filter would draw a hill that
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gets sharper depending on it’s sigma value.</p>
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Last updated on 2022-06-05 10:06:24.
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