Journal
APPLIED SCIENCES-BASEL
Volume 13, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/app13095467
Keywords
photogrammetry; computer vision; artificial intelligence; feature-based matching; feature extraction methods; hand-crafted methods; learning-based methods
Ask authors/readers for more resources
This article presents PhotoMatch, an open-source tool for multi-view and multi-modal feature-based image matching, including various state-of-the-art methods for preprocessing, feature extraction, and matching. The tool also provides tools for detailed assessment and comparison of different methods, allowing users to select the best combination of methods for each specific dataset.
The accurate and reliable extraction and matching of distinctive features (keypoints) in multi-view and multi-modal datasets is still an open research topic in the photogrammetric and computer vision communities. However, one of the main milestones is selecting which method is a suitable choice for specific applications. This encourages us to develop an educational tool that encloses different hand-crafted and learning-based feature-extraction methods. This article presents PhotoMatch, a didactical, open-source tool for multi-view and multi-modal feature-based image matching. The software includes a wide range of state-of-the-art methodologies for preprocessing, feature extraction and matching, including deep learning detectors and descriptors. It also provides tools for a detailed assessment and comparison of the different approaches, allowing the user to select the best combination of methods for each specific multi-view and multi-modal dataset. The first version of the tool was awarded by the ISPRS (ISPRS Scientific Initiatives, 2019). A set of thirteen case studies, including six multi-view and six multi-modal image datasets, is processed by following different methodologies, and the results provided by the software are analysed to show the capabilities of the tool. The PhotoMatch Installer and the source code are freely available.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available