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001482583 001__ 1482583
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001482583 019__ $$a1369599442
001482583 020__ $$a9783031457258$$q(electronic bk.)
001482583 020__ $$a3031457250$$q(electronic bk.)
001482583 020__ $$z9783031457241
001482583 020__ $$z3031457242
001482583 0247_ $$a10.1007/978-3-031-45725-8$$2doi
001482583 035__ $$aSP(OCoLC)1405844824
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001482583 08204 $$a006.3/7$$223/eng/20231024
001482583 1112_ $$aVISIGRAPP (Conference)$$n(17th :$$d2022 :$$cOnline)
001482583 24510 $$aComputer vision, imaging and computer graphics theory and applications :$$b17th international joint conference, VISIGRAPP 2022, virtual event, February 6-8, 2022, revised selected papers /$$cA. Augusto de Sousa, Kurt Debattista, Alexis Paljic, Mounia Ziat, Christophe Hurter, Helen Purchase, Giovanni Maria Farinella, Petia Radeva, Kadi Bouatouch, editors.
001482583 24630 $$aVISIGRAPP 2022
001482583 264_1 $$aCham :$$bSpringer,$$c[2023]
001482583 264_4 $$c©2023
001482583 300__ $$a1 online resource :$$billustrations (chiefly color).
001482583 336__ $$atext$$btxt$$2rdacontent
001482583 337__ $$acomputer$$bc$$2rdamedia
001482583 338__ $$aonline resource$$bcr$$2rdacarrier
001482583 4901_ $$aCommunications in computer and information science,$$x1865-0937 ;$$v1815
001482583 500__ $$aInternational conference proceedings.
001482583 500__ $$aIncludes author index.
001482583 5050_ $$aIntro -- Preface -- Organization -- Contents -- Automatic Threshold RanSaC Algorithms for Pose Estimation Tasks -- 1 Introduction -- 2 RanSaC Methods -- 2.1 Notation -- 2.2 History of RanSaC Algorithms -- 3 Adaptative RanSaC Algorithms -- 4 Data Generation Methodology -- 4.1 Models and Estimators -- 4.2 Semi-artificial Data Generation Method -- 5 Benchmark and Results -- 5.1 Performance Measures -- 5.2 Parameters -- 5.3 Results -- 5.4 Analysis and Comparison -- 6 Conclusion -- References -- Semi-automated Generation of Accurate Ground-Truth for 3D Object Detection -- 1 Introduction
001482583 5058_ $$a2 Related Work on 3D Object Detection -- 2.1 Techniques for Early Object Detection -- 2.2 CNN-Based 3D Object Detection -- 2.3 Conclusions on Related Work -- 3 Semi-automated 3D Dataset Generation -- 3.1 Orientation Estimation -- 3.2 3D Box Estimation -- 4 Experiments -- 4.1 Experimental Setup and Configuration -- 4.2 Evaluation 1: Annotation-Processing Chain -- 4.3 Evaluation 2: 3D Object Detector Trained on the Annotation-Processing Configurations -- 4.4 Cross-Validation on KITTI Dataset -- 4.5 Unsupervised Approach -- 5 Conclusion -- References
001482583 5058_ $$aA Quantitative and Qualitative Analysis on a GAN-Based Face Mask Removal on Masked Images and Videos -- 1 Introduction -- 2 Related Works -- 2.1 Inpainting -- 2.2 Face Completion -- 3 Method -- 3.1 Pix2pix-Based Inpainting -- 3.2 Custom Loss Function -- 3.3 System Overview -- 3.4 Predicting Feature Points on a Face -- 4 Experiment -- 4.1 Image Evaluation -- 4.2 Video Evaluation -- 5 Discussion -- 5.1 Quality of Generated Images -- 5.2 Discriminating Facial Expressions -- 5.3 Generating Smooth Videos -- 5.4 Additional Quantitative Analyses -- 6 Limitations -- 7 Conclusion -- References
001482583 5058_ $$aDense Material Segmentation with Context-Aware Network -- 1 Introduction -- 2 Related Works -- 2.1 Material Segmentation Datasets -- 2.2 Fully Convolutional Network -- 2.3 Material Segmentation with FCN -- 2.4 Global and Local Training -- 2.5 Boundary Refinement -- 2.6 Self-training -- 3 CAM-SegNet Architecture -- 3.1 Feature Sharing Connection -- 3.2 Context-Aware Dense Material Segmentation -- 3.3 Self-training Approach -- 4 CAM-SegNet Experiment Configurations -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 5 CAM-SegNet Performance Analysis
001482583 5058_ $$a5.1 Quantitative Analysis -- 5.2 Qualitative Analysis -- 5.3 Ablation Study -- 6 Conclusions -- References -- Partial Alignment of Time Series for Action and Activity Prediction -- 1 Introduction -- 2 Related Work -- 3 Temporal Alignment of Action/Activity Sequences -- 3.1 Alignment Methods -- Segmented Sequences -- 3.2 Alignment Methods -- Unsegmented Sequences -- 3.3 Action and Activity Prediction -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Alignment-Based Prediction in Segmented Sequences -- 4.3 Alignment-Based Action Prediction in Unsegmented Sequences
001482583 506__ $$aAccess limited to authorized users.
001482583 520__ $$aThis book constitutes the referred proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2022, Virtual Event, February 6–8, 2022. The 15 full papers included in this book were carefully reviewed and selected from 392 submissions. The purpose of VISIGRAPP is to bring together researchers and practitioners interested in both theoretical advances and applications of computer vision, computer graphics and information visualization. VISIGRAPP is composed of four co-located conferences, each specialized in at least one of the aforementioned main knowledge areas, namely GRAPP, IVAPP, HUCAPP and VISAPP. .
001482583 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 24, 2023).
001482583 650_6 $$aVision par ordinateur$$vCongrès.
001482583 650_6 $$aInfographie$$vCongrès.
001482583 650_0 $$aComputer vision$$vCongresses.$$vCongresses$$0(DLC)sh2008101162
001482583 650_0 $$aComputer graphics$$vCongresses.$$0(DLC)sh2006004464
001482583 655_0 $$aElectronic books.
001482583 655_7 $$aproceedings (reports)$$2aat
001482583 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001482583 655_7 $$aConference papers and proceedings.$$2lcgft
001482583 655_7 $$aActes de congrès.$$2rvmgf
001482583 7001_ $$aSousa, A. Augusto de,$$eeditor.
001482583 7001_ $$aDebattista, Kurt,$$d1975-$$eeditor.
001482583 7001_ $$aPaljic, Alexis,$$eeditor.
001482583 7001_ $$aZiat, Mounia,$$eeditor.
001482583 7001_ $$aHurter, Christophe,$$eeditor.
001482583 7001_ $$aPurchase, Helen C.,$$eeditor.
001482583 7001_ $$aFarinella, Giovanni Maria,$$eeditor.
001482583 7001_ $$aRadeva, Petia,$$eeditor.
001482583 7001_ $$aBouatouch, K.$$q(Kadi),$$d1950-$$eeditor.
001482583 77608 $$iPrint version: $$z3031457242$$z9783031457241$$w(OCoLC)1360308325
001482583 830_0 $$aCommunications in computer and information science ;$$v1815.$$x1865-0937
001482583 852__ $$bebk
001482583 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-45725-8$$zOnline Access$$91397441.1
001482583 909CO $$ooai:library.usi.edu:1482583$$pGLOBAL_SET
001482583 980__ $$aBIB
001482583 980__ $$aEBOOK
001482583 982__ $$aEbook
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