06191nam 2201561z- 450 991055736070332120231214133005.0(CKB)5400000000042285(oapen)https://directory.doabooks.org/handle/20.500.12854/76767(EXLCZ)99540000000004228520202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor EnvironmentsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic 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 industriesbicsscTraffic sign detection and tracking (TSDR)advanced driver assistance system (ADAS)computer vision3D convolutional neural networksmachine learningCT brainbrain hemorrhagevisual inspectionone-class classifiergrow-when-required neural networkevolving connectionist systemsautomatic designbio-inspired techniquesartificial bee colonyimage analysisfeature extractionship classificationmarine systemscitruspests and diseases identificationconvolutional neural networkparameter efficiencyvehicle detectionYOLOv2focal lossanchor boxmulti-scaledeep learningneural networkgenerative adversarial networksynthetic imagestool wear monitoringsuperalloy toolimage recognitionobject detectionUAV imageryvehicular traffic flow detectionvehicular traffic flow classificationvehicular traffic congestionvideo classificationbenchmarksemantic segmentationatrous convolutionspatial poolingship radiated noiseunderwater acousticssurface electromyography (sEMG)convolution neural networks (CNNs)hand gesture recognitionfabric defectmixed kernelscross-scalecascaded center-nessdeformable localizationcontinuous castingsurface defects3D imagingdefect detectionobject detectorobject trackingactivity measureYolodeep sortHungarian algorithmoptical flowsspatiotemporal interest pointssports sceneCT imagesconvolutional neural networkshepatic cancervisual question answeringthree-dimensional (3D) visionreinforcement learninghuman-robot interactionfew shot learningSVMCNNcascade classifiervideo surveillanceRFIartefactsInSARimage processingpixel convolutionthresholdingnearest neighbor filteringdata acquisitionaugmented realitypose estimationindustrial environmentsinformation retriever sensormulti-hop reasoningevidence chainscomplex search requesthigh-speed trainshuntingnon-stationaryfeature fusionmulti-sensor fusionunmanned aerial vehiclesdrone detectionUAV detectionvisual detectionInformation technology industriesWoźniak Marcinedt1303762Woźniak MarcinothBOOK9910557360703321Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments3027189UNINA