Following the success of the three first éditions, we propose the 4th edition of the ICPR workshop on Reproducible Research in Pattern Recognition.

RRPR 2022 is intended as both a short participative course on the Reproducible Research (RR) aspects, leading to open discussions with the participants, and also as a practical workshop on how to actually perform RR. In addition, another key goal for gathering the research community is to further advance the scientific aspects of reproducibility in pattern recognition research. 

This workshop is of interest for all ICPR participants and attendees since it allows to handle various topics not restricted to one specific fields. The reproducibility is an important topics in general and particularly good for PhD students and young researchers to learn "good habits". Following the third edition a special track on Geometry and Deep Learning is also proposed.

This Call for Papers expects two kinds of contributions.

The track 1 on RR Frameworks is dedicated to the general topics of Reproducible Research in experimental Computer Science with clear links to Image Processing and Pattern Recognition. Papers describing experiences, frameworks or platforms are welcome. The contributions might also include discussions on software libraries, experiences highlighting how the works benefit from Reproducible Research.

In the track 2 on RR Results, authors are invited to describe their works in terms of Reproducible Research. For example, authors of papers already accepted to ICPR might propose a companion paper describing the quality of the reproducible aspects. In particular the papers of this track can focus mainly (but not limited) for instance on:

• Algorithmic implementation details
• Influence of parameter(s) for the result quality (criteria to optimize them).
• Integration of source code in an other framework.
• Known limitations (or difficult cases).
• Future improvements.
• Installation procedure.

For this track, the topics could overlap with the main topics of the ICPR tracks:

• Geometry and Deep Learning (special track)
• Discrete Geometry and Mathematical Morphology
• Pattern Recognition and Machine Learning
• Computer Vision and Robot Vision
• Image, Speech, Signal, and Video Processing
• Document Analysis, Biometrics, and Pattern Recognition Applications.
• Biomedical Image Analysis and Applications