
Computer Vision Systems
13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings
Springer (Publisher)
Published on 19. September 2021
Book
Paperback/Softback
XI, 259 pages
978-3-030-87155-0 (ISBN)
Description
This book constitutes the refereed proceedings of the 13th International Conference on Computer Vision Systems, ICVS 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually.
The 20 papers presented were carefully reviewed and selected from 29 submissions. cover a broad spectrum of issues falling under the wider scope of computer vision in real-world applications, including among others, vision systems for robotics, autonomous vehicles, agriculture and medicine. In this volume, the papers are organized into the sections: attention systems; classification and detection; semantic interpretation; video and motion analysis; computer vision systems in agriculture.
The 20 papers presented were carefully reviewed and selected from 29 submissions. cover a broad spectrum of issues falling under the wider scope of computer vision in real-world applications, including among others, vision systems for robotics, autonomous vehicles, agriculture and medicine. In this volume, the papers are organized into the sections: attention systems; classification and detection; semantic interpretation; video and motion analysis; computer vision systems in agriculture.
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
8 s/w Abbildungen, 84 farbige Abbildungen
XI, 259 p. 92 illus., 84 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 15 mm
Weight
417 gr
ISBN-13
978-3-030-87155-0 (9783030871550)
DOI
10.1007/978-3-030-87156-7
Schweitzer Classification
Other editions
Additional editions

Markus Vincze | Timothy Patten | Henrik I. Christensen
Computer Vision Systems
13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings
E-Book
09/2021
Springer
€69.54
Available for download
Content
Attention Systems.- Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields.- MARL: Multimodal Attentional Representation Learning for Disease Prediction.- Object Localization with Attribute Preference based on Top-Down Attention.- See the silence: improving visual-only voice activity detection by optical flow and RGB fusion.- Classification and Detection.- Score to Learn: a Comparative Analysis of Scoring Functions for Active Learning in Robotics.- Enhancing the performance of image classification through features automatically learned from depth-maps.- Object Detection on TPU Accelerated Embedded Devices.- Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification.- Semantic Interpretation.- Measuring the Sim2Real gap in 3D Object classification for different 3D data representation.- Spatially-Constrained Semantic Segmentation with Topological ?aps and Visual ?mbeddings.- Knowledge-enabled generation of semantically annotated image sequences of manipulation activities from VR demonstrations.- Make It Easier: An Empirical Simplification of a Deep 3D Segmentation Network for Human Body Parts.- Video and Motion Analysis.- Video Popularity Prediction through Fusing Early Viewership with Video Content.- Action Prediction during Human-Object Interaction based on DTW and Early Fusion of Human and Object Representations.- GridTrack: Detection and Tracking of Multiple Objects in Dynamic Occupancy Grids.- An Efficient Video Desnowing and Deraining Method with a Novel Variant Dataset.- Computer Vision Systems in Agriculture.- Robust Counting of Soft Fruit through Occlusions with Re-identification.- Non-destructive Soft Fruit Mass and Volume Estimation for Phenotyping in Horticulture.- Learning Image-based Contaminant Detection in Wool Fleece from Noisy Annotations.- Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network's Feature Pyramid Levels.