Laura Leal-Taixé

Jan-Michael Frahm

Frank Dellaert

Bodo Rosenhahn

Marc Pollefeys

This book constitutes the thoroughly refereed post-proceedings of the 15th International Workshop on Theoretic Foundations of Computer Vision, held as a Dagstuhl Seminar in Dagstuhl Castle, Germany, in June/July 2011. The 19 revised full papers presented were carefully reviewed and selected after a blind peer-review process. The topic of this Workshop was Outdoor and Large-Scale Real-World Scene Analysis, which covers all aspects, applications and open problems regarding the performance or design of computer vision algorithms capable of working in outdoor setups and/or large-scale environments. Developing these methods is important for driver assistance, city modeling and reconstruction, virtual tourism, telepresence, and motion capture.

15th International Workshop on Theoretical Foundations of Computer Vision, Dagstuhl Castle, Germany, June 26 - July 1, 2011. Revised Selected Papers

Outdoor and Large-Scale Real-World Scene Analysis

Christopher Buckley

Giovanni Ponti

Andrea Ferretti

Valerio Bitetta

Lorenzo Severini

Francesco Gullo

Ilaria Bordino

Ricard Gavaldà

João Gama

Donato Malerba

Min Zhou

Rita Ribeiro

Philippe Fournier-Viger

Massimiliano Ruocco

Michael Kamp

Lee Cooper

Przemyslaw Biecek

Sebastian Ventura

Mykola Pechenizkiy

Riccardo Guidotti

Anna Monreale

Christine Largeron

Jefrey Lijffijt

Irena Koprinska

Linara Adilova

Yamuna Krishnamurthy

Pedro M. Ferreira

Ibéria Medeiros

Eirini Ntoutsi

Salvatore Rinzivillo

Pablo Lanillos

Holger Fröning

Damian Roqueiro

Gregor Schiele

Franz Pernkopf

Michaela Blott

Giuseppina Andresini

Guilherme Graça

Naghmeh Ghazaleh

Jonas Richiardi

Konstantinos Sechidis

Benoît Frénay

Bo Kang

Tiphaine Viard

Pascal Welke

Erlend Aune

Claudio Gallicchio

Adrien Bibal

Tassadit Bouadi

Luis Galárraga

José Oramas

Günther Schindler

Daniela Cialfi

Maxwell Ramstead

Tim Verbelen

M. Saqib Nawaz

Meng Sun

Diego Saldana Miranda

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021)Workshop on Parallel, Distributed and Federated Learning (PDFL 2021)Workshop on Graph Embedding and Mining (GEM 2021)Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021)Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021)Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021)Workshop on Bias and Fairness in AI (BIAS 2021)Workshop on Workshop on Active Inference (IWAI 2021)Workshop on Machine Learning for Cybersecurity (MLCS 2021)Workshop on Machine Learning in Software Engineering (MLiSE 2021)Workshop on MIning Data for financial applications (MIDAS 2021)Sixth Workshop on Data Science for Social Good (SoGood 2021)Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021)Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020)Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)

International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I

Vittorio Murino

Michael Yang

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Algorithms, Applications and Deep Learning

Multimodal Scene Understanding

Bodo Rosenhahn

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