DAGM German Conference on Pattern Recognition (DAGM GCPR) 2022
- Start: Sep 28, 2022
- End: Sep 30, 2022
- Speaker: Bastian Goldücke, Oliver Deussen, Georg Umlauf
- Location: Konstanz
- Host: German Association for Pattern Recognition (DAGM)
The DAGM German Conference on Pattern Recognition (DAGM GCPR) 2022 is the 44th annual symposium of the German Association for Pattern Recognition (DAGM). It is an international premier venue for recent advances in pattern recognition including image processing, machine learning, and computer vision and welcomes submissions from all areas of pattern recognition. Authors are invited to submit high-quality papers presenting original research. Submitted papers will be reviewed based on the criteria of originality, soundness, empirical evaluation, and presentation. Accepted papers will be published by Springer as a proceeding of the Lecture Notes in Computer Science (LNCS). The best papers will be invited to contribute to a special issue of the International Journal of Computer Vision (IJCV).
Topics of interest include, but are not limited to, the following:
- Image/video processing, analysis, and computer vision
- Machine learning and pattern recognition
- Mathematical foundations, statistical data analysis and models
- Computational photography and confluence of vision and graphics
- Biomedical image processing and analysis
- Document analysis
We especially invite submissions for the following Special Tracks, which are chaired and reviewed explicitly by experts from the respective fields. They also have special review criteria which will be explicitly communicated to the reviewers to ensure clear quality expectations and interesting contributions.
Computer vision systems and applications
The computer vision systems and applications track invites papers on systems and applications with significant, interesting vision and machine learning components. The track provides a forum for researchers working on industrial applications to share their latest developments. The focus is not on state-of-the-art research novelties, but the system and applied papers need to stand out in the successful transfer and application of research results to industry with measurable success indicators, such as performance, robustness, memory or energy consumption, big data, the systems-level innovation or the adaptation of existing methods to a complete novel domain while satisfying industrial requirements.
Pattern recognition in the life and natural sciences
Pattern recognition and machine learning have become a major driver in the sciences already, for example, for data driven analysis or understanding of processes. This special track asks for original work that demonstrates successful development and application of pattern recognition methods tailored to the specific domain from the life- and natural sciences.
Photogrammetry and remote sensing
The photogrammetry and remote sensing track invites papers on theory and applications in photogrammetry and remote sensing with significant computer vision or machine learning components. The track provides a forum for researchers developing approaches from image classification and segmentation to high-precision photogrammetry to share their latest developments. Besides state-of-the-art research novelties, papers will also be considered if they present interesting, complex applications possibly in unexpected domains or with novel extensive data sets.
The robot vision track invites papers on state-of-the-art research in computer vision approaches for robotics. The papers in the track will be reviewed by experts in the field and judged by criteria of technical merit, quality, originality, and scientific novelty. The track provides a forum for researchers on robotics-related methods for computer vision and machine learning at the conference.
The dates for all submissions are as follows (deadlines will not be extended):
- Paper Submission Deadline: 3.6.
- Supplementary Material Deadline: 10.6.
- Decisions to Authors: 15.7.
- Camera Ready Deadline: 31.7.
- Conference: 28.9. – 30.9.