Several occurrences of a pattern, up to a user-defined number, are returned. Only the reliable ones are retained.

The correlation is computed on continuous tone values (as opposed to binary). It is well known that when the lighting conditions vary, as it is often the case, straight comparison of the pattern and image behaves badly. To cope with this, automatically adjusting the contrast and/or intensity of the pattern before comparison is very effective. This process is known as normalization. EasyMatch provides four distinct normalization modes, depending on whether a gain and/or offset compensation is used.

Because of particular lighting effects, an object can appear with inverted contrast (white on black instead of black on white or conversely). Depending on the application, it can be useful to keep inverted instances or to disregard them. Three matching modes are available: consider positive occurrences only, negative occurrences only or both.

To find the best matches between the pattern and target image, the target is allowed to translate horizontally and vertically. Additionally, it can be allowed to rotate and/or to change its scale in the X and Y directions simultaneously or independently. The rotation angle and scale factors vary in a user-specified interval. All degrees of freedom can be combined at will.

The accuracy with which the pattern is measured can be chosen (the less accurate, the faster). A one tenth-of-a-pixel accuracy can be achieved.

When the pattern cannot be inscribed in a rectangular ROI, the surrounding of the pattern can be ignored by setting the pixels values below a threshold level. These pixels will not take part in the matching process. The same feature can be used if parts of the template change from sample to sample.

EasyMatch processes 8-bit grayscale images as well as 24-bit RGB images.

When images are acquired with non-square pixels, rotated objects appear skewed. Taking the pixel aspect ratio into account can compensate for this effect.