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Update documentation to release 1.5
2012-08-13 09:16:26 -04:00
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<div class="title">Libcv Search Methods </div> </div>
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<div class="textblock"><p>This page describes the methods libcv uses to search for a sub image in a root image. </p>
<h2><a class="anchor" id="tolerance"></a>
Tolerance Values</h2>
<p>Tolerance values are used to control how strict each of the following search methods are. Acceptable values are from <code>INT_MIN</code> to <code>INT_MAX</code>.</p>
<p>Additionally, each of the reference programs - <code>cva-input</code> and <code>cva-match</code> - have a "sane tolerance" built in. This is accessed by the "-o" switch, and allows you to specify a tolerance on scale of 1-100, where 1 is incredibly strict, and 100 is incredibly loose. </p>
<dl class="note"><dt><b>Note:</b></dt><dd>The formula for calculating the sane tolerance is: \( T(x) = (10^{\frac{\log{INT\_MAX}}{\lambda}})^x \) where \( \lambda \) is the highest tolerance value (in our case, 100). Finally, we have to round down a little bit to ensure that we don't accidentally generate a value higher than <code>INT_MAX</code>. The formula used does mean that we will never be able to generate values lower than 0. </dd></dl>
Release version 1.3 documentation
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<dl class="warning"><dt><b>Warning:</b></dt><dd>The "sane-tolerance" option doesn't know which search method you are using - Thus while 1 is an incredibly strict search for <a class="el" href="libcvautomation_search_methods.html#SQDIFF">Squared Difference</a> and <a class="el" href="libcvautomation_search_methods.html#SQDIFF_NORMED">Squared Difference (Normalized)</a>, it is fairly loose search for <a class="el" href="libcvautomation_search_methods.html#CCORR">Cross Correlation</a>, <a class="el" href="libcvautomation_search_methods.html#CCORR_NORMED">Cross Correlation (Normalized)</a>, <a class="el" href="libcvautomation_search_methods.html#CCOEFF">Correlation Coefficient</a>, and <a class="el" href="libcvautomation_search_methods.html#CCOEFF_NORMED">Correlation Coefficient (Normalized)</a></dd></dl>
Release version 1.2 documentation
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<h2><a class="anchor" id="SQDIFF"></a>
Squared Difference</h2>
Release version 1.1 Documentation
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<div class="fragment"><pre class="fragment"><span class="preprocessor"> #define CV_TM_SQDIFF 0 </span>
Release version 1.2 documentation
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</pre></div><p> Squared Difference is the default search method used by <code>libcvautomation</code>, as well as <code>cva-match</code> and <code>cva-input</code>. </p>
<dl class="user"><dt><b>For this method, setting a low tolerance value results in a more strict match.</b></dt><dd></dd></dl>
<p>Formula: \(R(x,y) = \sum_{x&#39;,y&#39;} (T(x&#39;,y&#39;) - I(x + x&#39;, y+y&#39;))^2 \)</p>
<h2><a class="anchor" id="SQDIFF_NORMED"></a>
Squared Difference (Normalized)</h2>
Release version 1.1 Documentation
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<div class="fragment"><pre class="fragment"><span class="preprocessor"> #define CV_TM_SQDIFF_NORMED 1 </span>
Release version 1.3 documentation
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</pre></div><p> This is a normalized version of the <a class="el" href="libcvautomation_search_methods.html#SQDIFF">Squared Difference</a> search method. </p>
Release version 1.2 documentation
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<dl class="user"><dt><b>For this method, setting a low tolerance value results in a more strict match.</b></dt><dd></dd></dl>
<p>Formula: \( R(x,y) = \frac{\sum_{x&#39;,y&#39;}(T(x&#39;,y&#39;) - I(x + x&#39;, y + y&#39;))^2}{ \sqrt{\sum_{x&#39;,y&#39;}T(x&#39;,y&#39;)^2 \cdot \sum_{x&#39;,y&#39;}I(x + x&#39;, y + y&#39;)^2}} \)</p>
<h2><a class="anchor" id="CCORR"></a>
Cross Correlation</h2>
Release version 1.1 Documentation
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<div class="fragment"><pre class="fragment"><span class="preprocessor"> #define CV_TM_CCORR 2 </span>
Release version 1.2 documentation
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</pre></div><p> This is the Cross Correlation search method. </p>
<dl class="user"><dt><b>For this method, setting a high tolerance value results in a more strict match.</b></dt><dd></dd></dl>
<p>Formula: \( R(x,y)= \sum _{x&#39;,y&#39;} (T(x&#39;,y&#39;) \cdot I(x+x&#39;,y+y&#39;)) \)</p>
<h2><a class="anchor" id="CCORR_NORMED"></a>
Cross Correlation (Normalized)</h2>
Release version 1.1 Documentation
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<div class="fragment"><pre class="fragment"><span class="preprocessor"> #define CV_TM_CCORR_NORMED 3 </span>
Release version 1.3 documentation
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</pre></div><p> This is the normalized version of the <a class="el" href="libcvautomation_search_methods.html#CCORR">Cross Correlation</a> search method. </p>
Release version 1.2 documentation
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<dl class="user"><dt><b>For this method, setting a high tolerance value results in a more strict match.</b></dt><dd></dd></dl>
<p>Formula: \( R(x,y)= \frac{\sum_{x&#39;,y&#39;} (T(x&#39;,y&#39;) \cdot I&#39;(x+x&#39;,y+y&#39;))}{\sqrt{\sum_{x&#39;,y&#39;}T(x&#39;,y&#39;)^2 \cdot \sum_{x&#39;,y&#39;} I(x+x&#39;,y+y&#39;)^2}} \)</p>
<h2><a class="anchor" id="CCOEFF"></a>
Correlation Coefficient</h2>
Release version 1.1 Documentation
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<div class="fragment"><pre class="fragment"><span class="preprocessor"> #define CV_TM_CCOEFF 4 </span>
Release version 1.2 documentation
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</pre></div><p> This is the Correlation Coefficient search method. </p>
<dl class="user"><dt><b>For this method, setting a high tolerance value results in a more strict match.</b></dt><dd></dd></dl>
<p>Formula: \( R(x,y)= \sum _{x&#39;,y&#39;} (T&#39;(x&#39;,y&#39;) \cdot I(x+x&#39;,y+y&#39;)) \) where: \( \begin{array}{l} T&#39;(x&#39;,y&#39;)=T(x&#39;,y&#39;) - 1/(w \cdot h) \cdot \sum _{x&#39;&#39;,y&#39;&#39;} T(x&#39;&#39;,y&#39;&#39;) \\ I&#39;(x+x&#39;,y+y&#39;)=I(x+x&#39;,y+y&#39;) - 1/(w \cdot h) \cdot \sum _{x&#39;&#39;,y&#39;&#39;} I(x+x&#39;&#39;,y+y&#39;&#39;) \end{array} \)</p>
<h2><a class="anchor" id="CCOEFF_NORMED"></a>
Correlation Coefficient (Normalized)</h2>
Release version 1.1 Documentation
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<div class="fragment"><pre class="fragment"><span class="preprocessor"> #define CV_TM_CCOEFF_NORMED 5 </span>
Release version 1.3 documentation
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</pre></div><p> This is the normalized version of the <a class="el" href="libcvautomation_search_methods.html#CCOEFF">Correlation Coefficient</a> search method. </p>
Release version 1.2 documentation
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<dl class="user"><dt><b>For this method, setting a high tolerance value results in a more strict match.</b></dt><dd></dd></dl>
<p>Formula: \( R(x,y)= \frac{ \sum_{x&#39;,y&#39;} (T&#39;(x&#39;,y&#39;) \cdot I&#39;(x+x&#39;,y+y&#39;)) }{ \sqrt{\sum_{x&#39;,y&#39;}T&#39;(x&#39;,y&#39;)^2 \cdot \sum_{x&#39;,y&#39;} I&#39;(x+x&#39;,y+y&#39;)^2} } \) </p>
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