Image and Video Processing and Recognition Based on Artificial Intelligence (Volume II)
| dc.contributor.editor | Park, Kang Ryoung | |
| dc.contributor.editor | Lee, Sangyoun | |
| dc.contributor.editor | Kim, Euntai | |
| dc.date.accessioned | 2023-06-23T09:50:00Z | |
| dc.date.available | 2023-06-23T09:50:00Z | |
| dc.date.issued | 2023 | |
| dc.identifier | ONIX_20230623_9783036576022_109 | |
| dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/100877 | |
| dc.description.abstract | This reprint focuses on challenging issues in the field of AI-based image and video processing and recognition, including the topics of AI-based image processing, understanding, recognition, compression, and reconstruction; AI-based video processing, understanding, recognition, compression, and reconstruction; computer vision based on AI; AI-based biometrics; AI-based object detection and tracking; approaches that combine AI techniques and conventional methods for image and video processing and recognition; explainable AI (XAI) for image and video processing and recognition; generative adversarial network (GAN)-based image and video processing and recognition; and approaches that combine AI techniques and blockchain methods for image and video processing and recognition. | |
| dc.language | English | |
| dc.subject.classification | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues | en_US |
| dc.subject.classification | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology | en_US |
| dc.subject.classification | thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities | en_US |
| dc.subject.other | Finger-vein recognition | |
| dc.subject.other | motion blur image restoration | |
| dc.subject.other | modified DeblurGAN | |
| dc.subject.other | CNN | |
| dc.subject.other | deep learning | |
| dc.subject.other | stress recognition | |
| dc.subject.other | stress database | |
| dc.subject.other | spatial attention | |
| dc.subject.other | temporal attention | |
| dc.subject.other | facial landmark | |
| dc.subject.other | pose estimation | |
| dc.subject.other | convolutional neural network | |
| dc.subject.other | lightweight | |
| dc.subject.other | knowledge distillation | |
| dc.subject.other | histogram oriented graphic | |
| dc.subject.other | multi-camera multi-object tracking | |
| dc.subject.other | detection quality | |
| dc.subject.other | traffic sign recognition | |
| dc.subject.other | sliding windows | |
| dc.subject.other | meta learning | |
| dc.subject.other | classification | |
| dc.subject.other | construction sign detection | |
| dc.subject.other | image synthesis | |
| dc.subject.other | cut-and-paste | |
| dc.subject.other | perspective transformation | |
| dc.subject.other | group-sparsity loss | |
| dc.subject.other | temporal attention module | |
| dc.subject.other | video-based pedestrian-attribute recognition | |
| dc.subject.other | gait recognition | |
| dc.subject.other | self-supervised learning | |
| dc.subject.other | multi-task learning | |
| dc.subject.other | weakly-supervised learning | |
| dc.subject.other | biometrics | |
| dc.subject.other | cross-sensor fingerprints | |
| dc.subject.other | fingerprint enhancement | |
| dc.subject.other | cGAN | |
| dc.subject.other | adversarial learning | |
| dc.subject.other | video summarization | |
| dc.subject.other | spatial–temporal features | |
| dc.subject.other | cross-attention | |
| dc.subject.other | adaptive video streaming | |
| dc.subject.other | A-LSTM networks | |
| dc.subject.other | bit rate measurement | |
| dc.subject.other | client–server model | |
| dc.subject.other | HTTP | |
| dc.subject.other | reference metrics | |
| dc.subject.other | video quality | |
| dc.subject.other | image deraining | |
| dc.subject.other | neural network | |
| dc.subject.other | vision transformer | |
| dc.subject.other | generative adversarial network | |
| dc.subject.other | computer vision | |
| dc.subject.other | information retrieval | |
| dc.subject.other | content-based video retrieval | |
| dc.subject.other | anomaly detection | |
| dc.subject.other | generation error | |
| dc.subject.other | feature trajectory smoothness | |
| dc.subject.other | surveillance video | |
| dc.subject.other | facial expression recognition | |
| dc.subject.other | spatial transformation network | |
| dc.subject.other | attention mechanism | |
| dc.subject.other | triplet loss function | |
| dc.subject.other | intra-similarity problem | |
| dc.subject.other | breast cancer | |
| dc.subject.other | ultrasonography | |
| dc.subject.other | autoencoder | |
| dc.subject.other | brain image registration | |
| dc.subject.other | generation adversarial network | |
| dc.subject.other | hand detection | |
| dc.subject.other | hand classification | |
| dc.subject.other | YOLO-family networks | |
| dc.subject.other | convolutional neural networks (CNNs) | |
| dc.subject.other | egocentric vision | |
| dc.subject.other | unsupervised learning | |
| dc.subject.other | reinforcement learning | |
| dc.subject.other | piecewise linear interpolation | |
| dc.title | Image and Video Processing and Recognition Based on Artificial Intelligence (Volume II) | |
| dc.type | book | |
| oapen.identifier.doi | 10.3390/books978-3-0365-7603-9 | |
| oapen.relation.isPublishedBy | 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 | |
| oapen.relation.isbn | 9783036576022 | |
| oapen.relation.isbn | 9783036576039 | |
| oapen.pages | 382 | |
| oapen.place.publication | Basel |
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