FG Computer Vision & Remote Sensing

7 Items

Recent Submissions
3D reconstruction of anatomical structures from 2D X-ray images

Ehlke, Moritz (2021)

Two-dimensional (2D) radiographs are widely used for diagnosis, treatment planning, and follow-up in orthopedics. The images depict bone at high contrast to surrounding tissue and capture weight-bearing situations when taken in upright position. Compared to three-dimensional (3D) computed tomography (CT), 2D radiographs further expose the patient to relatively low radiation doses. Projectional ...

Pixel2point: 3D object reconstruction from a single image using CNN and initial sphere

Afifi, Ahmed J. ; Magnusson, Jannes ; Soomro, Toufique A. ; Hellwich, Olaf (2020-12-23)

3D reconstruction from a single image has many useful applications. However, it is a challenging and ill-posed problem as various candidates can be a solution for the reconstruction. In this paper, we propose a simple, yet powerful, CNN model that generates a point cloud of an object from a single image. 3D data can be represented in different ways. Point clouds have proven to be a common and s...

Analyzing and improving image-based 3D surface reconstruction challenged by weak texture or low illumination

Aldeeb, Nader H. (2020)

Image-based 3D reconstruction is a photogrammetric technique that recovers the real 3D world from its 2D images. It has become an active research field in computer vision because of its application in numerous areas, including science, medicine, culture, military, architecture, and entertainment. The principle that stands behind image-based 3D reconstruction is called triangulation. It is used...

Image-based tracking, quantification and exploration of cardiac dynamics

Tautz, Lennart (2020)

The heart is the central driver of blood circulation, which supplies the body with oxygen. Heart function is determined by a complex interaction between heart wall contraction, heart valves and respiration. Cardiac diseases and disease progression manifest often regionally and diversely across patients. Modern clinical imaging allows the acquisition of image data for the detailed assessment of...

Volcanic Hot-Spot Detection Using SENTINEL-2: A Comparison with MODIS−MIROVA Thermal Data Series

Massimetti, Francesco ; Coppola, Diego ; Laiolo, Marco ; Valade, Sébastien ; Cigolini, Corrado ; Ripepe, Maurizio (2020-03-03)

In the satellite thermal remote sensing, the new generation of sensors with high-spatial resolution SWIR data open the door to an improved constraining of thermal phenomena related to volcanic processes, with strong implications for monitoring applications. In this paper, we describe a new hot-spot detection algorithm developed for SENTINEL-2/MSI data that combines spectral indices on the SWIR ...

Exploiting SAR Tomography for Supervised Land-Cover Classification

D’Hondt, Olivier ; Hänsch, Ronny ; Wagener, Nicolas ; Hellwich, Olaf (2018-11-05)

In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms PolSAR data provided relevant features are extract...

Classification of PolSAR Images by Stacked Random Forests

Hänsch, Ronny ; Hellwich, Olaf (2018-02-23)

This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor as an additional feature. To this aim, the internal node tests are designed to work not only directly on the complex-valued image data, but also...