06223nam 2201585z- 450 991055736070332120220111(CKB)5400000000042285(oapen)https://directory.doabooks.org/handle/20.500.12854/76767(oapen)doab76767(EXLCZ)99540000000004228520202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor EnvironmentsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (454 p.)3-0365-1268-3 3-0365-1269-1 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 -Information technology industriesbicssc3D convolutional neural networks3D imagingactivity measureadvanced driver assistance system (ADAS)anchor boxartefactsartificial bee colonyatrous convolutionaugmented realityautomatic designbenchmarkbio-inspired techniquesbrain hemorrhagecascade classifiercascaded center-nesscitrusCNNcomplex search requestcomputer visioncontinuous castingconvolution neural networks (CNNs)convolutional neural networkconvolutional neural networkscross-scaleCT brainCT imagesdata acquisitiondeep learningdeep sortdefect detectiondeformable localizationdrone detectionevidence chainsevolving connectionist systemsfabric defectfeature extractionfeature fusionfew shot learningfocal lossgenerative adversarial networkgrow-when-required neural networkhand gesture recognitionhepatic cancerhigh-speed trainshuman-robot interactionHungarian algorithmhuntingimage analysisimage processingimage recognitionindustrial environmentsinformation retriever sensorInSARmachine learningmarine systemsmixed kernelsmulti-hop reasoningmulti-scalemulti-sensor fusionn/anearest neighbor filteringneural networknon-stationaryobject detectionobject detectorobject trackingone-class classifieroptical flowsparameter efficiencypests and diseases identificationpixel convolutionpose estimationreinforcement learningRFIsemantic segmentationship classificationship radiated noisespatial poolingspatiotemporal interest pointssports scenesuperalloy toolsurface defectssurface electromyography (sEMG)SVMsynthetic imagesthree-dimensional (3D) visionthresholdingtool wear monitoringTraffic sign detection and tracking (TSDR)UAV detectionUAV imageryunderwater acousticsunmanned aerial vehiclesvehicle detectionvehicular traffic congestionvehicular traffic flow classificationvehicular traffic flow detectionvideo classificationvideo surveillancevisual detectionvisual inspectionvisual question answeringYoloYOLOv2Information technology industriesWoźniak Marcinedt1303762Woźniak MarcinothBOOK9910557360703321Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments3027189UNINA