<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ImageJ Basics on ChrisK91.me</title><link>https://chrisk91.me/tags/ImageJ-Basics/</link><description>Recent content in ImageJ Basics on ChrisK91.me</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 30 Oct 2016 00:00:00 +0000</lastBuildDate><atom:link href="https://chrisk91.me/tags/ImageJ-Basics/index.xml" rel="self" type="application/rss+xml"/><item><title>Pixels</title><link>https://chrisk91.me/post/2016-10-30-Pixels/</link><pubDate>Sun, 30 Oct 2016 00:00:00 +0000</pubDate><guid>https://chrisk91.me/post/2016-10-30-Pixels/</guid><description>You&amp;rsquo;ll most likely have a general idea of a &amp;ldquo;pixel&amp;rdquo;. But what happens to a pixel when we try to scale up a complete image? Do we get a big pixel? And what size does a pixel have? We will have a basic look and try to get a more accurate idea of pixels. We will also learn, that some manipulations are required in order to magnify or rotate pictures.</description></item><item><title>Bit depths, image data and histograms</title><link>https://chrisk91.me/post/2016-10-09-Bit-depth/</link><pubDate>Sun, 09 Oct 2016 00:00:00 +0000</pubDate><guid>https://chrisk91.me/post/2016-10-09-Bit-depth/</guid><description>Have you ever seen a dialog, asking you for a bit depth when saving an image? Here we will take a short stroll into the storage of image information and see, how a computer needs to transform information in order to store it on disc. We will talk about artifacts and problems that could arise, if no appropriate bit depth is chosen. Finally, we will check out histograms to assess the quality of an image.</description></item><item><title>An introduction to color</title><link>https://chrisk91.me/post/2016-09-28-An-introduction-to-color/</link><pubDate>Wed, 28 Sep 2016 00:00:00 +0000</pubDate><guid>https://chrisk91.me/post/2016-09-28-An-introduction-to-color/</guid><description>When you start working with digital images, some fundamentals should be known. Here we will take a look at color spaces and models &amp;mdash; ways to represent and break up the &amp;ldquo;contents&amp;rdquo; of a color. This knowledge can come in handy, when you want to do automatic feature detection in an image. Maybe you are interested in red precipitates or want to distinguish a purple cell from a green one? Or maybe you are just interested in some basics about images without complicated formulas.</description></item></channel></rss>