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Digital Signal, Image and Video Processing for Emerging Multimedia Technology
| Digital Signal, Image and Video Processing for Emerging Multimedia Technology |
| Autore | Kim Byung-Gyu |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (392 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
closed circuit television (CCTV)
character order preserving cloud system privacy risk security video surveillance 3D depth map inter-component prediction MVD reversible data hiding texture wavelet analysis deep learning super-resolution deep neural architecture pattern mining multi-scale analysis reversible data hiding (RDH) image processing cloud computing public key cryptography (PKC) classification content-based image retrieval genetic algorithms image retrieval image classification Wiener-Granger causality block-compressive sensing (BCS) saliency error analysis flexible partitioning step-less adaptive sampling non-linear filters MCV and MLV filters de-noising noise removal edge preserving video coding motion estimation motion compensation affine motion model perspective motion model VVC quantization (signal) channel allocation scalable video coding convolution neural network scene recognition vector of locally aggregated descriptor weakly supervised attention map fire and smoke detection spatial and temporal wavelet transform coefficient of variation image steganalysis WOW UNIWARD ternary classification convolutional neural network (CNN) bayesian optimization gaussian process learning rate acauisition function machine learning moving object image stabilization object detection optical flow surveillance UAVs multiview high efficiency video coding ρ model bit allocation rate control image similarity frame complexity image deblurring generative adversarial network Wasserstein distance adversarial loss perceptual loss sentiment analysis social media lexicon image fusion multi-focus trimaps focus maps VisDrone2019 aerial imagery Faster R-CNN SSD RFCN YOLOv3 RetinaNet SNIPER CenterNet wrist-mounted DiverPAD electrical insulator capacitive touchscreen marine leisure activities convolutional neural networks pattern recognition low light image restoration denoise noise reduction deep leaning multiple feature dependency detection surveillance system |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557136003321 |
Kim Byung-Gyu
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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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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)
| Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) |
| Autore | Tang Bo |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (344 p.) |
| Soggetto non controllato |
FPGA
recurrence plot (RP) residual learning neural networks driver monitoring navigation depthwise separable convolution optimization dynamic path-planning algorithms object tracking sub-region cooperative systems convolutional neural networks DSRC VANET joystick road scene convolutional neural network (CNN) multi-sensor p-norm occlusion crash injury severity prediction deep leaning squeeze-and-excitation electric vehicles perception in challenging conditions T-S fuzzy neural network total vehicle mass of the front vehicle electrocardiogram (ECG) communications generative adversarial nets camera adaptive classifier updating Vehicle-to-X communications convolutional neural network predictive Geobroadcast infinity norm urban object detector machine learning automated-manual transition red light-running behaviors photoplethysmogram (PPG) panoramic image dataset parallel architectures visual tracking autopilot ADAS kinematic control GPU road lane detection obstacle detection and classification Gabor convolution kernel autonomous vehicle Intelligent Transport Systems driving decision-making model Gaussian kernel autonomous vehicles enhanced learning ethical and legal factors kernel based MIL algorithm image inpainting fusion terrestrial vehicle driverless drowsiness detection map generation object detection interface machine vision driving assistance blind spot detection deep learning relative speed autonomous driving assistance system discriminative correlation filter bank recurrent neural network emergency decisions LiDAR real-time object detection vehicle dynamics path planning actuation systems maneuver algorithm autonomous driving smart band the emergency situations two-wheeled support vector machine model global region biological vision automated driving |
| ISBN | 3-03921-376-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Machine Learning and Embedded Computing in Advanced Driver Assistance Systems |
| Record Nr. | UNINA-9910367757403321 |
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Tang Bo
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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