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001482698 001__ 1482698
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001482698 019__ $$a1406083884
001482698 020__ $$a9789819975495$$q(electronic bk.)
001482698 020__ $$a9819975492$$q(electronic bk.)
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001482698 0247_ $$a10.1007/978-981-99-7549-5$$2doi
001482698 035__ $$aSP(OCoLC)1406411737
001482698 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dOCLCO
001482698 049__ $$aISEA
001482698 050_4 $$aTA1637$$b.I48 2023
001482698 08204 $$a006.6$$223/eng/20231102
001482698 1112_ $$aIGTA (Conference)$$n(18th :$$d2023 :$$cBeijing, China)
001482698 24510 $$aImage and graphics technologies and applications :$$b18th Chinese Conference, IGTA 2023, Beijing, China, August 17-19, 2023, Revised selected papers /$$cWang Yongtian, Wu Lifang, editors.
001482698 2463_ $$aIGTA 2023
001482698 260__ $$aSingapore :$$bSpringer,$$c2023.
001482698 300__ $$a1 online resource (502 p.).
001482698 4901_ $$aCommunications in Computer and Information Science ;$$v1910
001482698 500__ $$a3.3 Multi-frame Dynamic Target Tracking Based on Target Detection
001482698 500__ $$aIncludes author index.
001482698 5050_ $$aIntro -- Preface -- Organization -- Contents -- Image Processing and Enhancement Techniques -- Underwater Image Enhancement and Restoration Techniques: A Comprehensive Review, Challenges, and Future Trends -- 1 Introduction -- 2 Underwater Image Enhancement and Restoration -- 2.1 Challenges of Underwater Imaging -- 2.2 Underwater Image Enhancement -- 2.3 Underwater Image Restoration -- 3 Underwater Image Datasets and Quality Metrics -- 4 Experiments and Analysis -- 5 Opportunities and Future Trends -- 6 Conclusion -- References
001482698 5058_ $$aA Self-supervised Learning Reconstruction Algorithm with an Encoder-Decoder Architecture for Diffuse Optical Tomography -- 1 Introduction -- 2 Method -- 2.1 Light Propagation Model -- 2.2 Deep Convolutional Encoder-Decoder Architecture -- 3 Results -- 3.1 Dataset -- 3.2 Reconstruction Results -- 4 Conclusions -- References -- TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Excited Luminescence Scanned Tomography -- 1 Introduction -- 2 Methods -- 2.1 Forward Problem -- 2.2 Inverse Problem -- 2.3 Two-Step Reconstruction Algorithm -- 3 Results -- 3.1 Reconstruction Depth Test
001482698 5058_ $$a3.2 Spatial Resolution Test -- 3.3 Generalization Ability Test -- 4 Discussion and Conclusions -- References -- A Method for Enhancing the Quality of Compressed Videos Based on 2D Convolution and Aggregating Spatio-Temporal Information -- 1 Introduction -- 2 Related Work -- 2.1 Unet -- 2.2 Attention Mechanisms -- 3 The Proposed Approach -- 3.1 Method Proposal -- 3.2 Framework -- 3.3 Conv-block Module -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Dataset -- 4.3 Comparison to State-of-the-Arts -- 4.4 Analysis and Discussions -- 5 Conclusion -- References
001482698 5058_ $$aMultimedia-Based Informal Learning in Museum Using Augmented Reality -- 1 Introduction -- 2 Related Work -- 2.1 Cognitive Theory of Multimedia Learning in AR -- 2.2 AR in Museum Learning -- 3 Method -- 3.1 Environment Scenario -- 3.2 Experiment Design -- 3.3 Experiment Procedure -- 3.4 Participant -- 4 Result -- 4.1 Task Load -- 4.2 Cognitive Load -- 4.3 Acquired Knowledge -- 4.4 User Preference -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Machine Vision and 3D Reconstruction -- MAIM-VO: A Robust Visual Odometry with Mixed MLP for Weak Textured Environment -- 1 Introduction
001482698 5058_ $$a2 Related Work -- 3 Method -- 3.1 System Architecture -- 3.2 Mixer-WMLP Architecture -- 4 Experiments -- 4.1 Feature Matching -- 4.2 Localization on TUM RGB-D Dataset -- 5 Conclusions -- References -- Visual SLAM Algorithm Based on Target Detection and Direct Geometric Constraints in Dynamic Environments -- 1 Introduction -- 2 Related Works -- 2.1 Indirect Geometric Constraint Method -- 2.2 Direct Geometric Constraint Method -- 3 Method -- 3.1 System Overview -- 3.2 Dynamic Feature Detection Based on Target Detection and Direct Geometric Constraints
001482698 506__ $$aAccess limited to authorized users.
001482698 520__ $$aThis book constitutes the refereed proceedings of the 18th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2023, held in Beijing, China, during August 1719, 2023. The 35 full papers included in this book were carefully reviewed and selected from 129 submissions. They were organized in topical sections as follows: image processing and enhancement techniques; machine vision and 3D reconstruction; image/video big data analysis and understanding; computer graphics; visualization and visual analysis; virtual reality and human-computer interaction; and applications of image and graphics.
001482698 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 2, 2023).
001482698 650_6 $$aTraitement d'images$$xTechniques numériques$$vCongrès.
001482698 650_6 $$aInfographie$$vCongrès.
001482698 650_6 $$aVision par ordinateur$$vCongrès.
001482698 650_0 $$aImage processing$$xDigital techniques$$vCongresses.
001482698 650_0 $$aComputer graphics$$vCongresses.$$0(DLC)sh2006004464
001482698 650_0 $$aComputer vision$$vCongresses.$$vCongresses$$0(DLC)sh2008101162
001482698 655_0 $$aElectronic books.
001482698 7001_ $$aYongtian, Wang.
001482698 7001_ $$aLifang, Wu.
001482698 77608 $$iPrint version:$$aYongtian, Wang$$tImage and Graphics Technologies and Applications$$dSingapore : Springer,c2023$$z9789819975488
001482698 830_0 $$aCommunications in computer and information science ;$$v1910.
001482698 852__ $$bebk
001482698 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-7549-5$$zOnline Access$$91397441.1
001482698 909CO $$ooai:library.usi.edu:1482698$$pGLOBAL_SET
001482698 980__ $$aBIB
001482698 980__ $$aEBOOK
001482698 982__ $$aEbook
001482698 983__ $$aOnline
001482698 994__ $$a92$$bISE