View on GitHub

Multi-Template Matching for object-detection

HomePage and documentation for the Multi-Template matching project. A simple solution for object-detection from one or several template images. Available for Fiji, Python and KNIME.

Multi-Template Matching

Multi-Template-Matching is an accessible method to perform object-detection in images using one or several template images for the search.
The strength of the method compared to previously available single-template matching, is that by combining the detections from multiple templates, one can improve the range of detectable patterns. This helps if you expect variability of the object-perspective in your images, such as rotation, flipping…
The detections from the different templates are not simply combined, they are filtered using Non-Maxima Supression (NMS) to prevent overlapping detections.


We currently have implemented Multi-Template-Matching (MTM) in:


Refer to the wiki sections of the respective GitHub repository for the implementation-specific documentation.
In particular, the Fiji and KNIME implementation have dedicated youtube tutorials, while the python implementation comes with example notebooks that can be executed in a browser.
Below some generic documentation pages:

Additional resources:


If you use these implementations, please cite:

Thomas, L.S.V., Gehrig, J.
Multi-template matching: a versatile tool for object-localization in microscopy images
BMC Bioinformatics 21, 44 (2020).
Download the citation as a ris file.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721537 “ImageInLife”.