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Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments
| Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments |
| Autore | Woźniak Marcin |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (454 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
3D convolutional neural networks
3D imaging activity measure advanced driver assistance system (ADAS) anchor box artefacts artificial bee colony atrous convolution augmented reality automatic design benchmark bio-inspired techniques brain hemorrhage cascade classifier cascaded center-ness citrus CNN complex search request computer vision continuous casting convolution neural networks (CNNs) convolutional neural network convolutional neural networks cross-scale CT brain CT images data acquisition deep learning deep sort defect detection deformable localization drone detection evidence chains evolving connectionist systems fabric defect feature extraction feature fusion few shot learning focal loss generative adversarial network grow-when-required neural network hand gesture recognition hepatic cancer high-speed trains human-robot interaction Hungarian algorithm hunting image analysis image processing image recognition industrial environments information retriever sensor InSAR machine learning marine systems mixed kernels multi-hop reasoning multi-scale multi-sensor fusion n/a nearest neighbor filtering neural network non-stationary object detection object detector object tracking one-class classifier optical flows parameter efficiency pests and diseases identification pixel convolution pose estimation reinforcement learning RFI semantic segmentation ship classification ship radiated noise spatial pooling spatiotemporal interest points sports scene superalloy tool surface defects surface electromyography (sEMG) SVM synthetic images three-dimensional (3D) vision thresholding tool wear monitoring Traffic sign detection and tracking (TSDR) UAV detection UAV imagery underwater acoustics unmanned aerial vehicles vehicle detection vehicular traffic congestion vehicular traffic flow classification vehicular traffic flow detection video classification video surveillance visual detection visual inspection visual question answering Yolo YOLOv2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557360703321 |
Woźniak Marcin
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep Learning-Based Action Recognition
| Deep Learning-Based Action Recognition |
| Autore | Lee Hyo Jong |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (240 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
3D skeletal
3D-CNN action recognition activity recognition artificial intelligence class regularization class-specific features CNN continuous hand gesture recognition convolutional receptive field data augmentation deep learning dynamic gesture recognition Dynamic Hand Gesture Recognition embedded system feature fusion feedforward neural networks fusion strategies gesture classification gesture spotting graph convolution hand gesture recognition hand shape features high-order feature human action recognition human activity recognition human-computer interaction human-machine interface Long Short-Term Memory multi-modal features multi-modalities network multi-person pose estimation n/a partition pose representation partitioned centerpose network pose estimation real-time spatio-temporal differential spatio-temporal feature spatio-temporal image formation spatiotemporal activations spatiotemporal feature stacked hourglass network transfer learning |
| ISBN | 3-0365-5200-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619465803321 |
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Lee Hyo Jong
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sensor Systems for Gesture Recognition II
| Sensor Systems for Gesture Recognition II |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2023 |
| Descrizione fisica | 1 online resource (254 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
abnormal gait behavior
adaptive interactive game artificial neural network background clutter biomedical engineering computer vision data glove dataset deep learning deep Q-network electromyography emotion driven emotion judgment system ensemble learning extreme learning machine football force myography forearm amputee frequency emphasis general movements gloss prediction grasshopper optimization algorithm hand gesture recognition hand posture horse locomotion human activity recognition human-computer interactive IMU inertial measurement unit inertial measurement units infant movement analysis k-tournament selection machine learning MARG markerless MIMU motion analysis movement disorders multi-modal OpenPose orientation algorithm benchmarking orientation estimation pattern recognition pose estimation pose-based approach random forest reinforcement learning RGB-D sensor sensor fusion algorithm set of optimal signal features Siamese tracker sign language recognition sports technology surface electromyography surgical education surgical skills assessment test tracking drift training effect trajectory reconstruction transformer virtual hand visual feedback training visual tracking XGBoost |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910743280103321 |
| MDPI - Multidisciplinary Digital Publishing Institute, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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