Multi-Template-Matching is a package to perform object-recognition in images using one or several smaller template images.
The template and images should have the same bitdepth (8,16,32-bit) and number of channels (single/Grayscale or RGB).
The main function MTM.matchTemplates returns the best predicted locations provided either a score_threshold and/or the expected number of objects in the image.
Using pip, pip install Multi-Template-Matching
Once installed, import MTMshould work.
Check out the jupyter notebook tutorial for some example of how to use the package.
The wiki section of this related repository also provides some information about the implementation.
If you use this implementation for your research, please cite:
Multi-Template Matching: a versatile tool for object-localization in microscopy images;
Laurent SV Thomas, Jochen Gehrig
bioRxiv 619338; doi: https://doi.org/10.1101/619338
See this repo for the implementation as a Fiji plugin.
Here for a KNIME workflow using Multi-Template-Matching.
This work has been part of the PhD project of Laurent Thomas under supervision of Dr. Jochen Gehrig at:
ACQUIFER a division of DITABIS AG
Digital Biomedical Imaging Systems AG
Freiburger Str. 3
75179 Pforzheim
This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 721537 ImageInLife.
