.TH "libcvautomation_search_methods" 3 "23 Jul 2012" "Version 1.3" "libcvautomation" \" -*- nroff -*-
.ad l
.nh
.SH NAME
libcvautomation_search_methods \- Libcv Search Methods
This page describes the methods libcv uses to search for a sub image in a root image. 
.SH "Tolerance Values"
.PP
Tolerance values are used to control how strict each of the following search methods are. Acceptable values are from \fCINT_MIN\fP to \fCINT_MAX\fP.
.PP
Additionally, each of the reference programs - \fCcva-input\fP and \fCcva-match\fP - 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. 
.PP
\fBNote:\fP
.RS 4
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 \fCINT_MAX\fP. The formula used does mean that we will never be able to generate values lower than 0. 
.RE
.PP
\fBWarning:\fP
.RS 4
The 'sane-tolerance' option doesn't know which search method you are using - Thus while 1 is an incredibly strict search for \fBSquared Difference\fP and \fBSquared Difference (Normalized)\fP, it is fairly loose search for \fBCross Correlation\fP, \fBCross Correlation (Normalized)\fP, \fBCorrelation Coefficient\fP, and \fBCorrelation Coefficient (Normalized)\fP
.RE
.PP
.SH "Squared Difference"
.PP
.PP
.nf
 #define CV_TM_SQDIFF   0 
.fi
.PP
 Squared Difference is the default search method used by \fClibcvautomation\fP, as well as \fCcva-match\fP and \fCcva-input\fP. 
.PP
\fBFor this method, setting a low tolerance value results in a more strict match.\fP.RS 4

.RE
.PP
Formula: $R(x,y) = \sum_{x',y'} (T(x',y') - I(x + x', y+y'))^2 $
.SH "Squared Difference (Normalized)"
.PP
.PP
.nf
 #define CV_TM_SQDIFF_NORMED    1 
.fi
.PP
 This is a normalized version of the \fBSquared Difference\fP search method. 
.PP
\fBFor this method, setting a low tolerance value results in a more strict match.\fP.RS 4

.RE
.PP
Formula: $ R(x,y) = \frac{\sum_{x',y'}(T(x',y') - I(x + x', y + y'))^2}{ \sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'}I(x + x', y + y')^2}} $
.SH "Cross Correlation"
.PP
.PP
.nf
 #define CV_TM_CCORR    2 
.fi
.PP
 This is the Cross Correlation search method. 
.PP
\fBFor this method, setting a high tolerance value results in a more strict match.\fP.RS 4

.RE
.PP
Formula: $ R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y')) $
.SH "Cross Correlation (Normalized)"
.PP
.PP
.nf
 #define CV_TM_CCORR_NORMED 3 
.fi
.PP
 This is the normalized version of the \fBCross Correlation\fP search method. 
.PP
\fBFor this method, setting a high tolerance value results in a more strict match.\fP.RS 4

.RE
.PP
Formula: $ R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I'(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}} $
.SH "Correlation Coefficient"
.PP
.PP
.nf
 #define CV_TM_CCOEFF   4 
.fi
.PP
 This is the Correlation Coefficient search method. 
.PP
\fBFor this method, setting a high tolerance value results in a more strict match.\fP.RS 4

.RE
.PP
Formula: $ R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I(x+x',y+y')) $ where: $ \begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array} $
.SH "Correlation Coefficient (Normalized)"
.PP
.PP
.nf
 #define CV_TM_CCOEFF_NORMED    5 
.fi
.PP
 This is the normalized version of the \fBCorrelation Coefficient\fP search method. 
.PP
\fBFor this method, setting a high tolerance value results in a more strict match.\fP.RS 4

.RE
.PP
Formula: $ R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} } $