<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Image Processing on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/image-processing/</link><description>Recent content in Image Processing on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 23 Apr 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/image-processing/index.xml" rel="self" type="application/rss+xml"/><item><title>Essence of Linear Algebra (17): Linear Algebra in Computer Vision</title><link>https://www.chenk.top/en/linear-algebra/17-linear-algebra-in-computer-vision/</link><pubDate>Wed, 23 Apr 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/linear-algebra/17-linear-algebra-in-computer-vision/</guid><description>&lt;p>Computer vision is the science of teaching machines to see. What is striking is how thoroughly the whole field reduces to linear algebra: an image is a matrix, a geometric transformation is a matrix product, a camera is a &lt;span class="math-inline">$3 \times 4$&lt;/span>
 projection matrix, two-view geometry is the equation &lt;span class="math-inline">$\mathbf{x}_2^\top \mathbf{F}\, \mathbf{x}_1 = 0$&lt;/span>
, and 3D reconstruction is a sparse linear least-squares problem. Once you see the field through that lens, what once looked like a zoo of algorithms turns out to be a small set of linear-algebraic ideas applied repeatedly.&lt;/p></description></item></channel></rss>