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This book constitutes the refereed proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021, held as a virtual event, February 8-10, 2021.
The 16 full papers presented in this volume were carefully reviewed and selected from 371 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.
The contributions were organized in topical sections as follows: Computer Graphics Theory and Applications; Human Computer Interaction Theory and Applications; Information Visualization Theory and Applications; Computer Vision Theory and Applications.
List of contents
Computer Graphics Theory and Applications.- Impact of Avatar Representation in a Virtual Reality-based Multi-user Tunnel Fire Simulator for Training Purposes.- Facade Layout Completion with Long Short-term Memory Networks.- Human Computer Interaction Theory and Applications.- Generating Haptic Sensations over Spherical Surface.- Effects of Emotion-Induction Words on Memory and Pupillary Reactions while Viewing Visual Stimuli with Audio Guide.- A Bimanual Flick-based Japanese Software Keyboard using Direct Kanji Input.- Comparison of Cardiac Activity and Subjective Measures during Virtual Reality and Real Aircraft Flight.- Information Visualization Theory and Applications.- Improving Self-supervised Dimensionality Reduction: Exploring Hyperparameters and Pseudo-labeling Strategies.- Visualization of Source Code Similarity using 2.5D Semantic Software Maps.- Revisiting Order-Preserving, Gap-Avoiding Rectangle Packing.- Exploratory Data Analysis of Population Level Smartphone-sensed Data.- Towards Interactive Geovisualization Authoring Toolkit for Industry Use Cases.- Computer Vision Theory and Applications.- Global-fiirst Training Strategy with Convolutional Neural Networks to Improve Scale Invariance.- Spline-based Dense Medial Descriptors for Image Simplification using Saliency Maps.- BS-GAENets: Brain-spatial Feature Learning Via a Graph Deep Autoencoder for Multi-modal Neuroimaging Analysis.- Enhancing Backlight and Spotlight Images by the Retinex-inspired Bilateral Filter SuPeR-B.- Rethinking RNN-based Video Object Segmentation.
