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Computer Vision Systems = 13th Inter...
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Computer Vision Systems = 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Computer Vision Systems/ edited by Markus Vincze, Timothy Patten, Henrik I Christensen, Lazaros Nalpantidis, Ming Liu.
其他題名:
13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings /
其他作者:
Vincze, Markus.
面頁冊數:
XI, 259 p. 92 illus., 84 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Optical data processing. -
電子資源:
https://doi.org/10.1007/978-3-030-87156-7
ISBN:
9783030871567
Computer Vision Systems = 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings /
Computer Vision Systems
13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings /[electronic resource] :edited by Markus Vincze, Timothy Patten, Henrik I Christensen, Lazaros Nalpantidis, Ming Liu. - 1st ed. 2021. - XI, 259 p. 92 illus., 84 illus. in color.online resource. - Theoretical Computer Science and General Issues ;12899. - Theoretical Computer Science and General Issues ;9163.
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 Μaps and Visual Εmbeddings -- 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.
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.
ISBN: 9783030871567
Standard No.: 10.1007/978-3-030-87156-7doiSubjects--Topical Terms:
639187
Optical data processing.
LC Class. No.: TA1630-1650
Dewey Class. No.: 006.6
Computer Vision Systems = 13th International Conference, ICVS 2021, Virtual Event, September 22-24, 2021, Proceedings /
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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 Μaps and Visual Εmbeddings -- 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.
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