The operation matches the object model, inGrayModel, against the input image, inImage. The inSearchRegion region restricts the search area so that only in this region the centers of the objects can be presented. The inMaxDifference parameter determines the maximum average difference between corresponding pixel values of the valid object occurrence. The inMinDistance parameter determines minimum distance between any two valid occurrences (if two occurrences lie closer than inMinDistance from each other, the one with greater score is considered to be valid).

The computation time of the filter depends on the size of the model, the sizes of inImage and inSearchRegion, but also on the value of inMaxDifference. This parameter is a score threshold. Based on its value some partial computation can be sufficient to reject some locations as valid object instances. Moreover, the pyramid of the images is used. Thus, only the highest pyramid level is searched exhaustively, and potential candidates are later validated at lower levels. The inMinPyramidLevel parameter determines the lowest pyramid level used to validate such candidates. Setting this parameter to a value greater than 0 may speed up the computation significantly, especially for higher resolution images. However, the accuracy of the found object occurrences can be reduced. Lower inMaxDifference generates less potential candidates on the highest level to verify on lower levels. It should be noted that some valid occurrences with score above this score threshold can be missed. On higher levels score can be slightly lower than on lower levels. Thus, some valid object occurrences which on the lowest level would be deemed to be valid object instances can be incorrectly missed on some higher level. The diagMatchPyramid output represents all potential candidates recognized on each pyramid level and can be helpful during the difficult process of the proper parameter setting.

To be able to locate objects which are partially outside the image, the filter assumes that there are only black pixels beyond the image border.

The outObjects.Point array contains the model reference points of the matched object occurrences. The corresponding outObjects.Angle array contains the rotation angles of the objects. The corresponding outObjects.Match array provides information about both the position and the angle of each match combined into value of Rectangle2D type. Each element of the outObjects.Alignment array contains informations about the transform required for geometrical objects defined in the context of template image to be transformed into object in the context of corresponding outObjects.Match position. This array can be later used e.g. by 1D Edge Detection or Shape Fitting categories filters.

The SAD (Sum of Absolute Differences) method can be significantly slower than NCC (Normalized Cross-Correlation) method. Moreover, it is not illumination-invariant, as it is required in most applications. Thus, it is highly recommended to use the latter, NCC method instead.

Read more about Local Coordinate Systems in Machine Vision Guide: Local Coordinate Systems.

Additional information about Template Matching can be found in Machine Vision Guide: Template Matching

This operation supports automatic parallelization for multicore and multiprocessor systems.

This filter is available on Expert Complexity Level.

This filter is member of LocateObjects_SAD filter group.

• LocateSingleObject_SAD – Finds a single occurrence of a predefined template on an image by analysing the Square Average Difference between pixel values.

• CreateGrayModel – Creates a model for NCC or SAD template matching.

• LocateMultipleObjects_NCC – Finds all occurrences of a predefined template on an image by analysing the normalized correlation between pixel values.