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Computer Vision Systems: 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings 1st ed. 2021 [Pehme köide]

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  • Formaat: Paperback / softback, 259 pages, kõrgus x laius: 235x155 mm, kaal: 421 g, 84 Illustrations, color; 8 Illustrations, black and white; XI, 259 p. 92 illus., 84 illus. in color., 1 Paperback / softback
  • Sari: Theoretical Computer Science and General Issues 12899
  • Ilmumisaeg: 19-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303087155X
  • ISBN-13: 9783030871550
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Computer Vision Systems: 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings 1st ed. 2021Suurem pilt
  • Formaat: Paperback / softback, 259 pages, kõrgus x laius: 235x155 mm, kaal: 421 g, 84 Illustrations, color; 8 Illustrations, black and white; XI, 259 p. 92 illus., 84 illus. in color., 1 Paperback / softback
  • Sari: Theoretical Computer Science and General Issues 12899
  • Ilmumisaeg: 19-Sep-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 303087155X
  • ISBN-13: 9783030871550
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030871550.html
Märksõnad:
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.
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 Networks
Feature Pyramid Levels.