Titlebook: Computer Vision, Imaging and Computer Graphics Theory and Applications; 17th International J A. Augusto de Sousa,Kurt Debattista,Kadi Bouat

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,MinMax-CAM: Increasing Precision of Explaining Maps by Contrasting Gradient Signals and Regularizinins a popular alternative today. Highly versatile, and sufficiently simple, CAM-based techniques have been consistently used to explain the predictions of Convolutional Neural Networks over classification problems. However, many studies conducted to assert the precision of the produced explaining ma

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,DIAR: Deep Image Alignment and Reconstruction Using Swin Transformers,collection of images contains all relevant information if properly aligned and aggregated. In this paper, we attempt to build a deep learning pipeline that simultaneously aligns a sequence of distorted images and reconstructs them. We create a dataset that contains images with image distortions, suc

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,Active Learning with Data Augmentation Under Small vs Large Dataset Regimes for Semantic-KITTI Dataactive learning methods applied to the task of dataset distillation or core subset selection (subset with near equivalent performance as full dataset). We also study the effect of application of data augmentation (DA) within Bayesian AL based dataset distillation. We perform these experiments on the

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,A Study of Aerial Image-Based 3D Reconstructions in a Metropolitan Area, both open-source methods from the state of the art, and some of the most used commercial photogrammetry applications. The performance of these methods is measured against the densest annotated LiDAR point cloud available at a city scale. The study not only analyzes the accuracy and completeness of

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https://doi.org/10.1057/9781137366870erent vision transformers and compare them against contemporary CNN-based methods. Our study shows that transformer-based architecture, though lower in run-time efficiency, achieves comparable performance while being more robust against natural corruptions, as well as untargeted and targeted attacks. (Code: .).

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,The Electrodermal Activity of Player Experience in Virtual Reality Games: An Extended Evaluation ofetween the phasic component data and the reported experience variables. This shows that the phasic component of EDA data should be further investigated in conjunction with other psychophysiological signals because it has only recently demonstrated a weak link with player experience.

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,Transformers in Unsupervised Structure-from-Motion,erent vision transformers and compare them against contemporary CNN-based methods. Our study shows that transformer-based architecture, though lower in run-time efficiency, achieves comparable performance while being more robust against natural corruptions, as well as untargeted and targeted attacks. (Code: .).

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Conference proceedings 2023 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