<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[UnilVision]]></title><description><![CDATA[机器视觉算法实验室]]></description><link>https://blog.youkuaiyun.com/UnilVision</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; UnilVision]]></copyright><item><title><![CDATA[标准Gabor滤波器的C语言实现]]></title><link>https://blog.youkuaiyun.com/UnilVision/article/details/9015729</link><guid>https://blog.youkuaiyun.com/UnilVision/article/details/9015729</guid><author>UnilVision</author><pubDate>Mon, 03 Jun 2013 18:03:28 +0800</pubDate><description><![CDATA[<!--
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html, body, div, span, applet, object, iframe, h1, h2, h3, h4, h5, h6, p, blockquote, pre, a, abbr, acronym, address, big, cite, code, del, dfn, em, img, ins, kbd, q, s, samp, small, strike, str]]></description><category></category></item><item><title><![CDATA[[字符识别系列][一] 字符识别中的图像归一化算法简介]]></title><link>https://blog.youkuaiyun.com/UnilVision/article/details/8624606</link><guid>https://blog.youkuaiyun.com/UnilVision/article/details/8624606</guid><author>UnilVision</author><pubDate>Fri, 01 Mar 2013 11:03:33 +0800</pubDate><description><![CDATA[本文介绍3种基本的字符形状归一化算法(Character Shape Normalization)。字符归一化是光学字符识别中的一个子步骤，给定一个字符区域，我们要做的就是将该区域内的字符归一化到一个标准模板大小，然后才能提取特征，并送给分类器做具体的识别。好的归一化算法可以尽量提高后续特征提取在同一类内的一致性。

先来看一个例子，假如上帝拥有一个完美的字符归一化算法，那么他将可以做到如下所]]></description><category></category></item><item><title><![CDATA[KDE2000, 非参数化背景建模的基本方法]]></title><link>https://blog.youkuaiyun.com/UnilVision/article/details/8611544</link><guid>https://blog.youkuaiyun.com/UnilVision/article/details/8611544</guid><author>UnilVision</author><pubDate>Mon, 25 Feb 2013 22:49:18 +0800</pubDate><description><![CDATA[原论文名称：Non-parametricModel for Background Subtraction
原作者：AhmedElgammal, David Harwood, Larry Davis 
出处：ECCV会议, 2000.


KDE2000是使用Kernel Density Estimation（KDE）做背景建模的基础算法之一，目前被引用的次数已经超过了1500次。后来无数]]></description><category></category></item></channel></rss>