Object Detection

MatchTemplate

Comments from the Wiki

MatchTemplate(image, templ, result, method) → None

Compares a template against overlapped image regions.

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

The function is similar to CalcBackProjectPatch . It 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 different comparison methods one may use ( 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

  • method=CV_TM_SQDIFF_NORMED

    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'))

  • method=CV_TM_CCORR_NORMED

    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'))

    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}

  • method=CV_TM_CCOEFF_NORMED

    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 ( CV_TM_SQDIFF ) or maximums ( CV_TM_CCORR and CV_TM_CCOEFF ) 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).

Table Of Contents

Previous topic

Feature Detection

Next topic

features2d. Feature Detection and Descriptor Extraction

This Page