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  <div class="section" id="basics">
<h1>Basics</h1>
<p>Here are basic concepts that might help to understand documentation
written in this folder:</p>
<div class="section" id="convolution">
<h2>Convolution</h2>
<p>The simplest way to look at this is &#8220;tweaking the input so that it would
look like the shape provided&#8221;. What exact tweaking is applied depends on
the kernel.</p>
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<hr class="docutils" />
<div class="section" id="filters-kernels-weights">
<h2>Filters, kernels, weights</h2>
<p>Those three words usually mean the same thing, unless context is clear
about a different usage. Simply put, they are matrices, that are used to
achieve certain effects on the image. Lets consider a simple one, 3 by 3
Scharr filter</p>
<p><code class="docutils literal"><span class="pre">ScharrX</span> <span class="pre">=</span> <span class="pre">[1,0,-1][1,0,-1][1,0,-1]</span></code></p>
<p>The filter above, when convolved with a single channel image
(intensity/luminance strength), will produce a gradient in X
(horizontal) direction. There is filtering that cannot be done with a
kernel though, and one good example is median filter (mean is the
arithmetic mean, whereas median will be the center element of a sorted
array).</p>
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<div class="section" id="derivatives">
<h2>Derivatives</h2>
<p>A derivative of an image is a gradient in one of two directions: x
(horizontal) and y (vertical). To compute a derivative, one can use
Scharr, Sobel and other gradient filters.</p>
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<div class="section" id="curvature">
<h2>Curvature</h2>
<p>The word, when used alone, will mean the curvature that would be
generated if values of an image would be plotted in 3D graph. X and Z
axises (which form horizontal plane) will correspond to X and Y indices
of an image, and Y axis will correspond to value at that pixel. By
little stretch of an imagination, filters (another names are kernels,
weights) could be considered an image (or any 2D matrix). A mean filter
would draw a flat plane, whereas Gaussian filter would draw a hill that
gets sharper depending on it&#8217;s sigma value.</p>
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