activity
20162022
most citedICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents

35 citations · 300 across the 72 of their papers we have counts for

collaborators
Showing 2017Show all

10 papers · 1 filter

cs.CV20173 cited

Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation

Sulaiman Vesal, Andres Diaz-Pinto, Nishant Ravikumar +3

Magnetic resonance imaging (MRI) is an effective imaging modality for identifying and localizing breast lesions in women. Accurate and precise lesion segmentation using a computer-…

cs.CV20172 cited

Image Registration for the Alignment of Digitized Historical Documents

AmirAbbas Davari, Tobias Lindenberger, Armin Häberle +3

In this work, we conducted a survey on different registration algorithms and investigated their suitability for hyperspectral historical image registration applications. After the…

cs.CV20172 cited

Sketch Layer Separation in Multi-Spectral Historical Document Images

AmirAbbas Davari, Armin Häberle, Vincent Christlein +2

High-resolution imaging has delivered new prospects for detecting the material composition and structure of cultural treasures. Despite the various techniques for analysis, a signi…

cs.CV20172 cited

Robust Seed Mask Generation for Interactive Image Segmentation

Mario Amrehn, Stefan Steidl, Markus Kowarschik +1

In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist…

cs.CV201714 cited

Frangi-Net: A Neural Network Approach to Vessel Segmentation

Weilin Fu, Katharina Breininger, Tobias Würfl +3

In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to…

cs.CV201729 cited

Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network

Shuqing Chen, Holger Roth, Sabrina Dorn +7

Automatic multi-organ segmentation of the dual energy computed tomography (DECT) data can be beneficial for biomedical research and clinical applications. However, it is a challeng…