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It is our great pleasure to welcome you to the 6th ACM International Workshop on Multimedia Content Analysis in Sports (ACM MMSports'23). The workshop is co-located with ACM Multimedia 2023. After two years of pure virtual MMSports workshops and one hybrid year, we are more than happy that MMSports'23 is held on-site only again and we can all meet and interact in person. The workshop addresses a very timely topic because the influence of rapidly developing technologies has changed the way of how we participate, watch, understand and research sports. For example, television broadcasts augment live video footage with computer vision-based graphics in real time to emphasize different aspects of a game or performance and assist focus and understanding of viewers. Moreover, the astonishing impact of wearables within the last years plays a pivotal role in how we pursue and evaluate our personal training goals. In a professional setting, coaches and training scientists directly benefit from the latest technological research, reshaping the way we think about improving the performance and technique of athletes, understand sport injuries or enhance the qualitative and quantitative analyses of performances.

Computer science. --- Image segmentation.

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Cell image segmentation. --- Measurement uncertainty (Statistics)

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Signal processing --- Image segmentation. --- Mathematical optimization. --- Neural networks (Computer science)

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This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.

Image segmentation. --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

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Le but de ce travail de recherche est d'évaluer et d'améliorer des algorithmes de
segmentation pour la détection et le contourage automatique de tumeurs au sein d'images
à haute-résolution de tissus.
Ce travail permettra à l'étudiant(eà d'approfondir la compréhension, l'utilisation et
l'adaptation de méthodes à base d'ensembles d'arbres ou de réseaux profonds (deep
learning) sur de grandes quantités d'images liées à des problématiques concrètes dans le
domaine biomédical.

image segmentation --- machine learning --- u-net --- deep learning --- Ingénierie, informatique & technologie > Sciences informatiques