Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments

• Autore: Woźniak Marcin
• Pubblicazione: 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
• Persona (resp. second.): WoźniakMarcin
• Sommario/riassunto: Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue "Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments" present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland -
• Titolo autorizzato: Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557360703321
• Lo trovi qui: Univ. Federico II