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< title > libcvautomation: Libcv Search Methods< / title >
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< div id = "projectname" > libcvautomation
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  < span id = "projectnumber" > 2.0< / span >
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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 >
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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 >
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< h2 > < a class = "anchor" id = "SQDIFF" > < / a >
Squared Difference< / h2 >
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< div class = "fragment" > < pre class = "fragment" > < span class = "preprocessor" > #define CV_TM_SQDIFF 0 < / span >
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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: \(R(x,y) = \sum_{x' ,y' } (T(x' ,y' ) - I(x + x' , y+y' ))^2 \)< / p >
< h2 > < a class = "anchor" id = "SQDIFF_NORMED" > < / a >
Squared Difference (Normalized)< / h2 >
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< div class = "fragment" > < pre class = "fragment" > < span class = "preprocessor" > #define CV_TM_SQDIFF_NORMED 1 < / span >
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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 >
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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: \( R(x,y) = \frac{\sum_{x' ,y' }(T(x' ,y' ) - I(x + x' , y + y' ))^2}{ \sqrt{\sum_{x' ,y' }T(x' ,y' )^2 \cdot \sum_{x' ,y' }I(x + x' , y + y' )^2}} \)< / p >
< h2 > < a class = "anchor" id = "CCORR" > < / a >
Cross Correlation< / h2 >
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< div class = "fragment" > < pre class = "fragment" > < span class = "preprocessor" > #define CV_TM_CCORR 2 < / span >
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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: \( R(x,y)= \sum _{x' ,y' } (T(x' ,y' ) \cdot I(x+x' ,y+y' )) \)< / p >
< h2 > < a class = "anchor" id = "CCORR_NORMED" > < / a >
Cross Correlation (Normalized)< / h2 >
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< div class = "fragment" > < pre class = "fragment" > < span class = "preprocessor" > #define CV_TM_CCORR_NORMED 3 < / span >
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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 >
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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: \( R(x,y)= \frac{\sum_{x' ,y' } (T(x' ,y' ) \cdot I' (x+x' ,y+y' ))}{\sqrt{\sum_{x' ,y' }T(x' ,y' )^2 \cdot \sum_{x' ,y' } I(x+x' ,y+y' )^2}} \)< / p >
< h2 > < a class = "anchor" id = "CCOEFF" > < / a >
Correlation Coefficient< / h2 >
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< div class = "fragment" > < pre class = "fragment" > < span class = "preprocessor" > #define CV_TM_CCOEFF 4 < / span >
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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: \( R(x,y)= \sum _{x' ,y' } (T' (x' ,y' ) \cdot I(x+x' ,y+y' )) \) where: \( \begin{array}{l} T' (x' ,y' )=T(x' ,y' ) - 1/(w \cdot h) \cdot \sum _{x' ' ,y' ' } T(x' ' ,y' ' ) \\ I' (x+x' ,y+y' )=I(x+x' ,y+y' ) - 1/(w \cdot h) \cdot \sum _{x' ' ,y' ' } I(x+x' ' ,y+y' ' ) \end{array} \)< / p >
< h2 > < a class = "anchor" id = "CCOEFF_NORMED" > < / a >
Correlation Coefficient (Normalized)< / h2 >
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< div class = "fragment" > < pre class = "fragment" > < span class = "preprocessor" > #define CV_TM_CCOEFF_NORMED 5 < / span >
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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 >
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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: \( R(x,y)= \frac{ \sum_{x' ,y' } (T' (x' ,y' ) \cdot I' (x+x' ,y+y' )) }{ \sqrt{\sum_{x' ,y' }T' (x' ,y' )^2 \cdot \sum_{x' ,y' } I' (x+x' ,y+y' )^2} } \) < / p >
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