<?xml version="1.0" encoding="utf-8"?><rss version="2.0"><channel><title>RSS for Design of a Configurable ATR System Using MATLAB</title><link>http://academic.research.microsoft.com/Rss.aspx?cata=9&amp;id=50655374</link><description>Search RSS feed for Microsoft Academic Search</description><generator>MSRA Libra RSS Burner</generator><copyright>(c)2008 Microsoft Corpration, All right reserved.</copyright><pubDate>Mon, 20 May 2013 04:31:46 GMT</pubDate><lastBuildDate>Mon, 20 May 2013 04:31:46 GMT</lastBuildDate><category /><item><title>Design of a Configurable ATR System Using MATLAB</title><link>http://academic.research.microsoft.com/Publication/50655374</link><pubDate>Sun, 19 May 2013 21:31:46 GMT</pubDate><guid isPermaLink="false">506553740</guid><description><![CDATA[<div><a href="http://academic.research.microsoft.com/Author/56647986">Eric L. Wright</a>, <a href="http://academic.research.microsoft.com/Author/6117867">James Northern III</a>:
            
            <span style="margin-left:20px" /><span style="margin-left:20px"><a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4562741">view publication</a></span></div><div>Highly efficient and adaptable <a href='http://academic.research.microsoft.com/Keyword/19123/image-recognition'>image recognition</a>  systems have become a <a href='http://academic.research.microsoft.com/Keyword/57043/high-priority'>high priority</a>  within the last decade due to terrorism. This paper describes a <a href='http://academic.research.microsoft.com/Keyword/9554/design-and-development'>design and development</a>  process for a configurable <a href='http://academic.research.microsoft.com/Keyword/2643/automatic-target-recognition'>automatic target recognition</a>  system. Our novel <a href='http://academic.research.microsoft.com/Keyword/41578/template-matching'>template matching</a>  system employs a Matlab algorithm developed to accurately detect object patterns within a JPEG image. After downloading the original JPEG image, the Matlab algorithm processes it in three sequential phases: 1) "Sobel" <a href='http://academic.research.microsoft.com/Keyword/11553/edge-detection'>edge detection</a>  of the original image 2) Grey- level <a href='http://academic.research.microsoft.com/Keyword/41578/template-matching'>template matching</a>  based on the squared <a href='http://academic.research.microsoft.com/Keyword/12953/euclidean-distance'>Euclidean distance</a>  theory and 3) <a href='http://academic.research.microsoft.com/Keyword/39861/statistical-pattern-recognition'>Statistical pattern recognition</a>  of the resulting convoluted image. For our test cases, the <a href='http://academic.research.microsoft.com/Keyword/8562/cross-correlation'>cross correlation</a>  was determined using a template library created for the algorithm's <a href='http://academic.research.microsoft.com/Keyword/19123/image-recognition'>image recognition</a>  process. The average runtime for our system ranges from 5-8 minutes, per test case, with 100% correct recognition using the <a href='http://academic.research.microsoft.com/Keyword/41578/template-matching'>template matching</a>  technique described.</div><div>Conference: <a href="http://academic.research.microsoft.com/Conference/4297">Annual Technical Conference, IEEE Region 5 - TPSD</a>, 2008</div><div></div><div />]]></description></item></channel></rss>