Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments
Woźniak, Marcin (editor)
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 –
KeywordsTraffic 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; n/a
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Publication date and placeBasel, Switzerland, 2021
Information technology industries