727 research outputs found
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Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]
No full textPre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC.
For more information, also see the code documentation: https://github.com/vislearn/ngransa
Narratives for Lengyel funerary practice (Supplementary Material)
No full textSupplementary tables 1–6; supplementary figures 1–
DSAC++ Visual Camera Re-Localization [Data]
No full textSupplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the Cambridge Landmarks dataset. We also provide pre-trained models of our method for the MSR 7Scenes dataset and the Cambridge Landmarks dataset.
For more information, also see the code documentation: https://github.com/vislearn/LessMor
DSAC* Visual Re-Localization [Data]
No full textSupplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the MSR 7Scenes dataset, and the Stanford 12Scenes dataset, as well as precomputed camera coordinate files for both aforementioned datasets.
For more information, also see the code documentation: https://github.com/vislearn/dsacstar
Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]
No full textPre-trained models of our camera re-localization method for the MSR 7Scenes dataset.
For more information, also see the code documentation:
https://github.com/cvlab-dresden/DSA
Expert Sample Consensus (ESAC) for Visual Re-Localization [Data]
No full textSupplementary training data for visual camera re-localization, particularly pre-computed scene coordinates to the MSR 7Scenes dataset and the Standford 12Scenes dataset. We also provide pre-trained models of our method for the 7Scenes, 12Scenes, Dubrovnik and Aachen (day) datasets.
For more information, also see the code documentation: https://github.com/vislearn/esa
Gender differences in meat-eating behavior and environmental attitudes – The mediating role of the Dark Triad
No full textRecently, Machiavellianism was identified as potential mediator explaining gender differences in meat-eating justification strategies, which in turn predicted actual meat consumption. The current study aimed to – on the one hand – replicate this empirical finding and to – on the other hand – investigate the mediating role of the Dark Triad with regard to gender differences in pro-environmental attitudes. Five-hundred-forty-eight participants took part in the study. Women compared to men justified meat-eating less and held more positive attitudes toward the environment. More importantly, we replicated the finding that the association between gender and meat-eating justification strategies was mediated by Machiavellianism. Additionally, the association between gender and pro-environmental attitudes was mediated by psychopathy. These findings support the idea that while Machiavellianism is an important mediator explaining gender differences in meat-eating justification strategies, psychopathy is able to explain gender differences in attitudes toward the environment
HULC lab Tutorials
No full textThis archive provides data sets for the use in several tutorials/manuals on empirical research methods in Psycholinguistics/Cognitive Science created by members of the Heidelberg University Language & Cognition Lab.
We recommend to download respective files in the original format (comma separated vectors, "csv"), as most tutorials will have specified this file type
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code
No full textPublic urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 meters fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster-Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95\%, which was mainly influenced by the uncertainty of the public accessibility model
(MI)MI Model
No full textSource data for 'A minimal-invasive approach for standardized induction of myocardial infarction in mice'