Object Detection


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void matchTemplate(const Mat& image, const Mat& templ, Mat& result, int method)

Compares a template against overlapped image regions.

  • image – Image where the search is running; should be 8-bit or 32-bit floating-point
  • templ – Searched template; must be not greater than the source image and have the same data type
  • result – A map of comparison results; will be single-channel 32-bit floating-point. If image is W \times H and templ is w \times h then result will be (W-w+1) \times (H-h+1)
  • method – Specifies the comparison method (see below)

The function slides through image , compares the overlapped patches of size w \times h against templ using the specified method and stores the comparison results to result . Here are the formulas for the available comparison methods ( I denotes image , T template , R result ). The summation is done over template and/or the image patch: x' = 0...w-1, y' = 0...h-1

  • method=CV_TM_SQDIFF

    R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2


    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}}

  • method=CV_TM_CCORR

    R(x,y)= \sum _{x',y'} (T(x',y')  \cdot I(x+x',y+y'))


    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}}

  • method=CV_TM_CCOEFF

    R(x,y)= \sum _{x',y'} (T'(x',y')  \cdot I(x+x',y+y'))


    \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}


    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} }

After the function finishes the comparison, the best matches can be found as global minimums (when CV_TM_SQDIFF was used) or maximums (when CV_TM_CCORR or CV_TM_CCOEFF was used) using the minMaxLoc() function. In the case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels (and separate mean values are used for each channel). That is, the function can take a color template and a color image; the result will still be a single-channel image, which is easier to analyze.

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