<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>máquina de vectores de soporte Archives &#8212; MATLAB Number ONE</title>
	<atom:link href="https://matlab1.com/product-tag/maquina-de-vectores-de-soporte/feed/" rel="self" type="application/rss+xml" />
	<link>https://matlab1.com/product-tag/maquina-de-vectores-de-soporte/</link>
	<description>MATLAB Simulink &#124; Tutorial &#124; Code &#124; Project</description>
	<lastBuildDate>Fri, 03 Dec 2021 06:39:03 +0000</lastBuildDate>
	<language>en-GB</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	

<image>
	<url>https://matlab1.com/wp-content/uploads/2018/08/icon1-100x100.png</url>
	<title>máquina de vectores de soporte Archives &#8212; MATLAB Number ONE</title>
	<link>https://matlab1.com/product-tag/maquina-de-vectores-de-soporte/</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>Texture Segementation Algorithms for Electron Microscopy Images</title>
		<link>https://matlab1.com/shop/matlab-code/texture-segementation-algorithms-for-electron-microscopy-images/</link>
					<comments>https://matlab1.com/shop/matlab-code/texture-segementation-algorithms-for-electron-microscopy-images/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Wed, 09 May 2018 06:03:33 +0000</pubDate>
				<guid isPermaLink="false">https://matlab1.com/?post_type=product&#038;p=5179</guid>

					<description><![CDATA[<p>Image segmentation is a key bottleneck in analysis of electron microscope (EM) images, made challenging by the fact that many features in EM images differ not in intensity but in texture. I have explored several options for texture segmentation of images, based on feature vectors generated from local histograms of filtered versions of the image, which are then classified using a [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/texture-segementation-algorithms-for-electron-microscopy-images/">Texture Segementation Algorithms for Electron Microscopy Images</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
					<wfw:commentRss>https://matlab1.com/shop/matlab-code/texture-segementation-algorithms-for-electron-microscopy-images/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Cell counting by Hough transform and SVM classifier</title>
		<link>https://matlab1.com/shop/matlab-code/cell-counting-by-hough-transform-and-svm-classifier/</link>
					<comments>https://matlab1.com/shop/matlab-code/cell-counting-by-hough-transform-and-svm-classifier/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Mon, 02 Apr 2018 16:01:57 +0000</pubDate>
				<guid isPermaLink="false">https://matlab1.com/?post_type=product&#038;p=4667</guid>

					<description><![CDATA[<p>Analyzing liver cross-section images is a manual and laborious task, slowing down critical research toward finding alternative cures for patients with end-stage liver disease. In this project, we present methods to automatically count hepatocytes cells, count nuclei and classify liver vessel types, using input images of cell boundary and cell nuclei based on a dataset of 21 images. Compared to a [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/cell-counting-by-hough-transform-and-svm-classifier/">Cell counting by Hough transform and SVM classifier</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
]]></description>
		
					<wfw:commentRss>https://matlab1.com/shop/matlab-code/cell-counting-by-hough-transform-and-svm-classifier/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
	</channel>
</rss>
