Call for Papers

 

The DAGM German Conference on Pattern Recognition (DAGM GCPR) 2021 is the 43rd 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). Revised ICCV 2021 submissions can be submitted to the Fast Review Track.

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
• Biometrics
• Applications

We especially invite submissions for these Special Tracks, which are chaired and reviewed explicitly by experts from the respective fields:

Computer vision systems and applications
Chairs: Bodo Rosenhahn (Leibniz University Hannover), Carsten Steger (MVTec Software GmbH, Technical University of Munich)

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 reviewing criteria will be slightly different for this track: The focus is not on state-of-the-art research novelties, but the system and applied papers have 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. These review criteria will be explicitly communicated to the reviewers to ensure clear quality expectations and interesting contributions.

Pattern recognition in the life and natural sciences
Chairs: Joachim Denzler (University of Jena), Xiaoyi Jiang (University of Münster)

Pattern recognition and machine learning already became a major driver in the sciences, 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
Chairs: Helmut Mayer (Bundeswehr University Munich), Uwe Sörgel (University of Stuttgart)

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. The reviewing criteria will be slightly different for this track: Besides based on 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. These review criteria will be explicitly communicated to the reviewers to ensure clear quality expectations and interesting contributions.

Robot vision
Chairs: Friedrich Fraundorfer (Graz University of Technology), Jörg Stückler (Max Planck Institute for Intelligent Systems)

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.

Papers are submitted through Microsoft CMT (https://cmt3.research.microsoft.com/DAGMGCPR2021) using the author kit.