001484477 000__ 08997cam\\2200745\i\4500 001484477 001__ 1484477 001484477 003__ OCoLC 001484477 005__ 20240117003326.0 001484477 006__ m\\\\\o\\d\\\\\\\\ 001484477 007__ cr\cn\nnnunnun 001484477 008__ 231202s2023\\\\sz\a\\\\o\\\\\101\0\eng\d 001484477 019__ $$a1412623923 001484477 020__ $$a9783031479694$$q(electronic bk.) 001484477 020__ $$a3031479696$$q(electronic bk.) 001484477 020__ $$z9783031479687 001484477 0247_ $$a10.1007/978-3-031-47969-4$$2doi 001484477 035__ $$aSP(OCoLC)1411349428 001484477 040__ $$aOCLKB$$beng$$epn$$erda$$cOCLKB$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCQ$$dOCLCO 001484477 049__ $$aISEA 001484477 050_4 $$aQ337.5 001484477 08204 $$a006.4$$223/eng/20231212 001484477 1112_ $$aInternational Symposium on Visual Computing$$n(18th :$$d2023 :$$cLake Tahoe, Nev.). 001484477 24510 $$aAdvances in visual computing :$$b18th international symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16-18, 2023, proceedings.$$nPart I /$$cGeorge Bebis, Golnaz Ghiasi, Yi Fang, Andrei Sharf, Yue Dong, Chris Weaver, Zhicheng Leo, Joseph J. LaViola Jr., Luv Kohli, editors. 001484477 24630 $$aISVC 2023 001484477 264_1 $$aCham :$$bSpringer,$$c[2023] 001484477 264_4 $$c©2023 001484477 300__ $$a1 online resource (xlii, 614 pages) :$$billustrations (chiefly color). 001484477 336__ $$atext$$btxt$$2rdacontent 001484477 337__ $$acomputer$$bc$$2rdamedia 001484477 338__ $$aonline resource$$bcr$$2rdacarrier 001484477 4901_ $$aLecture notes in computer science ;$$v14361 001484477 500__ $$aInternational conference proceedings. 001484477 500__ $$aIncludes author index. 001484477 504__ $$aReferences -- Deep Learning Based GABA Edited-MRS Signal Reconstruction -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 J-Difference Spectrum -- 2.3 Dual Branch Self-Attention Neural Network -- 2.4 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Investigating the Impact of Attention on Mammogram Classification -- 1 Introduction -- 2 Data and Methods -- 2.1 Data Selection and Preprocessing -- 2.2 Selection of Models -- 2.3 Selection of Attention Methods -- 2.4 Training and Testing Process -- 3 Results and Discussion -- 3.1 Impact of Attention on CNN Performance 001484477 5050_ $$aST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management -- Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images -- Deep Learning Based GABA Edited-MRS Signal Reconstruction -- Investigating the Impact of Attention on Mammogram Classification -- ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical Images -- A Data-centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Images -- Visualization -- Visualizing Multimodal Time Series at Scale -- Hybrid Tree Visualizations for Analysis of Gerrymandering -- ArcheryVis: A Tool for Analyzing and Visualizing Archery Performance Data -- Spiro: Order-preserving Visualization in High Performance Computing Monitoring -- From Faces To Volumes - Measuring Volumetric Asymmetry in 3D Facial Palsy Scans -- Video Analysis and Event Recognition -- Comparison of Autoencoder Models for Unsupervised Representation Learning of Skeleton Sequences -- Local and Global Context Reasoning for Spatio-Temporal Action Localization -- Zero-Shot Video Moment Retrieval using BLIP-based Models -- Self-Supervised Representation Learning for Fine Grained Human Hand Action Recognition in Industrial Assembly Lines -- ST: Innovations in Computer Vision & Machine Learning for Critical & Civil Infrastructures -- Pretext Tasks in Bridge Defect Segmentation within a ViT-Adapter Framework -- A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation -- Efficient Resource Provisioning in Critical Infrastructures based on Multi-Agent Rollout enabled by Deep Q-Learning -- Video-Based Recognition of Aquatic Invasive Species Larvae Using Attention-LSTM Transformer -- ST: Generalization in Visual Machine Learning -- Latent Space Navigation for Face Privacy: A Case Study on the MNIST Dataset -- Domain Generalization for Foreground Segmentation Using Federated Learning -- Probabilistic Local Equivalence Certification for Robustness Evaluation -- Challenges of Depth Estimation for Transparent Objects -- Volumetric Body Composition through Cross-Domain Consistency Training for Unsupervised Domain Adaptation -- Computer Graphics -- Water Animation Using Coupled SPH and Wave Equation -- Neural Rendering into Unity -- Virtual Home Staging: Inverse Rendering and Editing an Indoor Panorama under Natural Illumination -- SwarmCurves: Evolutionary Curve Reconstruction -- Medical Image Analysis -- Brain Cortical Surface Registration with Anatomical Atlas Constraints -- When System Model meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging -- 3D Reconstruction from 2D Cerebral Angiograms as a Volumetric Denoising Problem -- An Integrated Shape-Texture Descriptor for Modeling Whole-Organism Phenotypes in Drug Screening -- Enhancing Image Reconstruction via Phase-Constrained Data in an Iterative Process -- Biometrics -- I Got Your Emotion: Emotion Preserving Face De-identification Using Injection-based Generative Adversarial Networks -- DoppelVer: A Benchmark for Face Verification -- Two-stage Face Detection and Anti-spoofing -- Autonomous Anomaly Detection in Images -- Driver Anomaly Detection Using Skeleton Images -- Driver Anomaly Detection Using Skeleton Images -- Latent Diffusion based Multi-class Anomaly Detection -- ST: Artificial Intelligence in Aerial and Orbital Imagery -- Investigating the impact of a low-rank tensor-based approach on deforestation imagery -- Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning A Case Study on Object Tracking Tasks -- Autonomous Navigation Via A Cascading CNN Framework Leveraging Synthetic Terrain Images -- ST: Data Gathering, Curation, and Generation for Computer Vision and Robotics in Precision Agriculture -- Synthetically labeled images for maize plant detection in UAS images -- An open source simulation toolbox for annotation of images and point clouds in agricultural scenarios -- Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms -- Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches -- Deep Learning for Super Resolution of Sugarcane Crop Line Imagery from Unmanned Aerial Vehicles. . 001484477 506__ $$aAccess limited to authorized users. 001484477 520__ $$aThis volume LNCS 14361 and 14362 constitutes the refereed proceedings of the, 16th International Symposium, ISVC 2023, in October 2023, held at Lake Tahoe, NV, USA. The 42 full papers and 13 poster papers were carefully reviewed and selected from 120 submissions. A total of 25 papers were also accepted for oral presentation in special tracks from 34 submissions. The following topical sections followed as: Part 1: ST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management; Visualization; Video Analysis and Event Recognition; ST: Innovations in Computer Vision & Machine Learning for Critical & Civil Infrastructures; ST: Generalization in Visual Machine Learning; Computer Graphics; Medical Image Analysis; Biometrics; Autonomous Anomaly Detection in Images; ST: Artificial Intelligence in Aerial and Orbital Imagery; ST: Data Gathering, Curation, and Generation for Computer Vision and Robotics in Precision Agriculture. Part 2: Virtual Reality; Segmentation; Applications; Object Detection and Recognition; Deep Learning; Poster. 001484477 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 12, 2023). 001484477 650_6 $$aInfographie$$vCongrès. 001484477 650_6 $$aVision par ordinateur$$vCongrès. 001484477 650_0 $$aComputer graphics$$vCongresses.$$0(DLC)sh2006004464 001484477 650_0 $$aComputer vision$$vCongresses.$$vCongresses$$0(DLC)sh2008101162 001484477 655_7 $$aproceedings (reports)$$2aat 001484477 655_7 $$aConference papers and proceedings.$$2lcgft 001484477 655_7 $$aActes de congrès.$$2rvmgf 001484477 655_0 $$aElectronic books. 001484477 7001_ $$aBebis, George,$$eeditor. 001484477 7001_ $$aGhiasi, Golnaz,$$eeditor. 001484477 7001_ $$aFang, Yi,$$eeditor. 001484477 7001_ $$aSharf, Andrei,$$eeditor. 001484477 7001_ $$aDong, Yue,$$eeditor. 001484477 7001_ $$aWeaver, Chris$$q(Christopher Eric),$$eeditor. 001484477 7001_ $$aLeo, Zhicheng,$$eeditor. 001484477 7001_ $$aLaViola, Joseph J.,$$eeditor. 001484477 7001_ $$aKohli, Luv,$$eeditor. 1411853845 001484477 77608 $$iPrint version:$$aBebis, George$$tAdvances in Visual Computing$$dCham : Springer,c2024$$z9783031479687 001484477 830_0 $$aLecture notes in computer science ;$$v14361. 001484477 852__ $$bebk 001484477 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-47969-4$$zOnline Access$$91397441.1 001484477 909CO $$ooai:library.usi.edu:1484477$$pGLOBAL_SET 001484477 980__ $$aBIB 001484477 980__ $$aEBOOK 001484477 982__ $$aEbook 001484477 983__ $$aOnline 001484477 994__ $$a92$$bISE