Pattern Recognition - 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27-30, 2022, Proceedings (Paperback, 1st ed. 2022)


This book constitutes the refereed proceedings of the 44th DAGM German Conference on Pattern Recognition, DAGM GCPR 2022, which was held during September 27 - 30, 2022.The 37 papers presented in this volume were carefully reviewed and selected from 78 submissions. They were organized in topical sections as follows: machine learning methods; unsupervised, semi-supervised and transfer learning; interpretable machine learning; low-level vision and computational photography; motion, pose estimation and tracking; 3D vision and stereo; detection and recognition; language and vision; scene understanding; photogrammetry and remote sensing; pattern recognition in the life and natural sciences; systems and applications.

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Product Description

This book constitutes the refereed proceedings of the 44th DAGM German Conference on Pattern Recognition, DAGM GCPR 2022, which was held during September 27 - 30, 2022.The 37 papers presented in this volume were carefully reviewed and selected from 78 submissions. They were organized in topical sections as follows: machine learning methods; unsupervised, semi-supervised and transfer learning; interpretable machine learning; low-level vision and computational photography; motion, pose estimation and tracking; 3D vision and stereo; detection and recognition; language and vision; scene understanding; photogrammetry and remote sensing; pattern recognition in the life and natural sciences; systems and applications.

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Product Details

General

Imprint

Springer International Publishing AG

Country of origin

Switzerland

Series

Lecture Notes in Computer Science, 13485

Release date

September 2022

Availability

Expected to ship within 10 - 15 working days

First published

2022

Editors

, , , , ,

Dimensions

235 x 155mm (L x W)

Format

Paperback

Pages

608

Edition

1st ed. 2022

ISBN-13

978-3-03-116787-4

Barcode

9783031167874

Categories

LSN

3-03-116787-2



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