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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 electronic resource (454 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
Traffic sign detection and tracking (TSDR)
advanced driver assistance system (ADAS) computer vision 3D convolutional neural networks machine learning CT brain brain hemorrhage visual inspection one-class classifier grow-when-required neural network evolving connectionist systems automatic design bio-inspired techniques artificial bee colony image analysis feature extraction ship classification marine systems citrus pests and diseases identification convolutional neural network parameter efficiency vehicle detection YOLOv2 focal loss anchor box multi-scale deep learning neural network generative adversarial network synthetic images tool wear monitoring superalloy tool image recognition object detection UAV imagery vehicular traffic flow detection vehicular traffic flow classification vehicular traffic congestion video classification benchmark semantic segmentation atrous convolution spatial pooling ship radiated noise underwater acoustics surface electromyography (sEMG) convolution neural networks (CNNs) hand gesture recognition fabric defect mixed kernels cross-scale cascaded center-ness deformable localization continuous casting surface defects 3D imaging defect detection object detector object tracking activity measure Yolo deep sort Hungarian algorithm optical flows spatiotemporal interest points sports scene CT images convolutional neural networks hepatic cancer visual question answering three-dimensional (3D) vision reinforcement learning human-robot interaction few shot learning SVM CNN cascade classifier video surveillance RFI artefacts InSAR image processing pixel convolution thresholding nearest neighbor filtering data acquisition augmented reality pose estimation industrial environments information retriever sensor multi-hop reasoning evidence chains complex search request high-speed trains hunting non-stationary feature fusion multi-sensor fusion unmanned aerial vehicles drone detection UAV detection visual detection |
| 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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Remote Sensing in Agriculture: State-of-the-Art
| Remote Sensing in Agriculture: State-of-the-Art |
| Autore | Borgogno-Mondino Enrico |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (220 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Environmental science, engineering & technology |
| Soggetto non controllato |
feature selection
spectral angle mapper support vector machine support vector regression hyperspectral imaging UAV cross-scale yellow rust spatial resolution winter wheat MODIS northern Mongolia remote sensing indices spring wheat yield estimation UAV-based LiDAR biomass crop height field phenotyping oasis crop type mapping Sentinel-1 and 2 integration statistically homogeneous pixels (SHPs) red-edge spectral bands and indices recursive feature increment (RFI) random forest (RF) unmanned aerial vehicles (UAVs) remote sensing (RS) thermal UAV RS thermal infrared (TIR) precision agriculture (PA) crop water stress monitoring plant disease detection vegetation status monitoring Landsat data blending crop yield prediction gap-filling volumetric soil moisture synthetic aperture radar (SAR) Sentinel-1 soil moisture semi-empirical model soil moisture Karnataka India reflectance digital number (DN) vegetation index (VI) Parrot Sequoia (Sequoia) DJI Phantom 4 Multispectral (P4M) Synthetic Aperture Radar SAR lodging Hidden Markov Random Field HMRF CDL corn soybean crop Monitoring crop management apple orchard damage polarimetric decomposition entropy anisotropy alpha angle storm damage mapping economic loss insurance support |
| ISBN | 3-0365-5484-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Remote Sensing in Agriculture |
| Record Nr. | UNINA-9910637779903321 |
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Borgogno-Mondino Enrico
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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