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* Fill values, links and images This commit fills in the template left by the previous commit * Replace Mathjax with code blocks Since GitHub doesn't allow Mathjax, the formula parts have been replaced with code blocks * Remove section on affine transformation It doesn't seem like the concept is used often, so postponed for now * Add basic explanation of convolution * Docs for Harris and Hessian The docs are written with a beginner in mind and has a basics section. The pictures and paper links are to be inserted. * Convert markdown to rst * Add some more relevant papers This commit cites and adds a link to paper about Hessian detector and a review paper about affine region detectors * Move to new concept name Space extrema detector doesn't seem to be a widespread usage of detector the detector class. There is a paper that uses "Affine region detector" term, which has quite a few citations * Fix mistakes in docs Fix mistakes related to terminology and algorithm steps * Add ip docs to index.rst Make sure ip docs are built and are in the final output * Fix formatting It seems like some lines are not properly formatted and rendered file containes unreadable lines. Fixed by not formatting it.
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Basics
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------
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Here are basic concepts that might help to understand documentation
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written in this folder:
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Convolution
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~~~~~~~~~~~
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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.
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--------------
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Filters, kernels, weights
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~~~~~~~~~~~~~~~~~~~~~~~~~
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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
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``ScharrX = [1,0,-1][1,0,-1][1,0,-1]``
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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).
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--------------
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Derivatives
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~~~~~~~~~~~
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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.
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--------------
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Curvature
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~~~~~~~~~
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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.
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