<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Image-Pyramids on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/image-pyramids/</link><description>Recent content in Image-Pyramids on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Thu, 12 Nov 2026 02:04:46 +1100</lastBuildDate><atom:link href="https://abaj.ai/tags/image-pyramids/index.xml" rel="self" type="application/rss+xml"/><item><title>Seeing in Frequency</title><link>https://abaj.ai/wiki/computer-vision/fourier/</link><pubDate>Wed, 11 Nov 2026 21:00:00 +1100</pubDate><guid>https://abaj.ai/wiki/computer-vision/fourier/</guid><description>&lt;p>Spatial filtering slides a small kernel over the pixels. This page looks at the same image through a different lens: as a sum of waves. In that view blurring keeps the slow waves and throws away the fast ones, a periodic artefact is a handful of bright dots that can be deleted one by one, and an edge detector is a filter that keeps a ring of middle frequencies. The Fourier transform is the change of lens; the &lt;em>convolution theorem&lt;/em> is what makes it worth the trouble, because it turns convolution into multiplication.&lt;span class="margin-note" data-note="Distils COMP9517 Week 2 (spatial filtering, the frequency domain and pyramids). Every number below is printed by a script run on the averaged Lab 1 castle image, 512 by 512 pixels with grey values in [0, 1].">
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