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pro vyhledávání: '"Rottensteiner, Franz"'
In recent years, three-dimensional point clouds are used increasingly to document natural environments. Each dataset contains a diverse set of objects, at varying shapes and sizes, distributed throughout the data and intricately intertwined with the
Externí odkaz:
http://arxiv.org/abs/2408.14421
Autor:
Rei, Luis, Mladenić, Dunja, Dorozynski, Mareike, Rottensteiner, Franz, Schleider, Thomas, Troncy, Raphaël, Lozano, Jorge Sebastián, Salvatella, Mar Gaitán
Publikováno v:
Multimedia Systems 29 (2023) 847-869
We develop a multimodal classifier for the cultural heritage domain using a late fusion approach and introduce a novel dataset. The three modalities are Image, Text, and Tabular data. We based the image classifier on a ResNet convolutional neural net
Externí odkaz:
http://arxiv.org/abs/2406.00423
Panoptic segmentation unifies semantic and instance segmentation and thus delivers a semantic class label and, for so-called thing classes, also an instance label per pixel. The differentiation of distinct objects of the same class with a similar app
Externí odkaz:
http://arxiv.org/abs/2405.10947
Autor:
Wittich, Dennis, Rottensteiner, Franz
This paper addresses domain adaptation for the pixel-wise classification of remotely sensed data using deep neural networks (DNN) as a strategy to reduce the requirements of DNN with respect to the availability of training data. We focus on the setti
Externí odkaz:
http://arxiv.org/abs/2108.07779
Autor:
Coenen, Max, Rottensteiner, Franz
The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a comm
Externí odkaz:
http://arxiv.org/abs/2107.10898
Land use as contained in geospatial databases constitutes an essential input for different applica-tions such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to ve
Externí odkaz:
http://arxiv.org/abs/2104.06991
Autor:
Coenen, Max, Rottensteiner, Franz
The retrieval of the 3D pose and shape of objects from images is an ill-posed problem. A common way to object reconstruction is to match entities such as keypoints, edges, or contours of a deformable 3D model, used as shape prior, to their correspond
Externí odkaz:
http://arxiv.org/abs/2102.10681
Autor:
Kölle, Michael, Laupheimer, Dominik, Schmohl, Stefan, Haala, Norbert, Rottensteiner, Franz, Wegner, Jan Dirk, Ledoux, Hugo
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially
Externí odkaz:
http://arxiv.org/abs/2102.05346
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