Search Results for author:
Found 8 papers, 1 papers with code
Evaluating saliency scores in point clouds of natural environments by learning surface anomalies
We assume that within the natural environment any change from the prevalent surface would suggest a salient object.
Multimodal Metadata Assignment for Cultural Heritage Artifacts
We develop a multimodal classifier for the cultural heritage domain using a late fusion approach and introduce a novel dataset.
Appearance Based Deep Domain Adaptation for the Classification of Aerial Images
Our method is based on adversarial training of an appearance adaptation network (AAN) that transforms images from DS such that they look like images from DT.
Pose Estimation and 3D Reconstruction of Vehicles from Stereo-Images Using a Subcategory-Aware Shape Prior
A multi-branch CNN is presented to derive predictions of the vehicle type and orientation.
A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases
In this paper, a hierarchical deep learning framework is proposed to verify the land use information.
Probabilistic Vehicle Reconstruction Using a Multi-Task CNN
In this paper, we present a probabilistic approach for shape-aware 3D vehicle reconstruction from stereo images that leverages the outputs of a novel multi-task CNN.
The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo
Automated semantic segmentation and object detection are of great importance in geospatial data analysis.
A two-layer Conditional Random Field for the classification of partially occluded objects
Conditional Random Fields (CRF) are among the most popular techniques for image labelling because of their flexibility in modelling dependencies between the labels and the image features.
