Datasets

This competition investigates the performance of large-scale retrieval of historical document fragments based on writer recognition. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists. To simulate fragments, we extract random text patches from histo...

This dataset was used in the ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI). The dataset used in this competition consists of 3600 handwritten pages originating from 13th to 20th century. It contains manuscripts from 720 different writers where each writer ...

The DeepAL fieldwork data 2017/2018 (DLFD) was collected via a 15-m research trimaran in Northern British Columbia (Vancouver Island) by an interdisciplinary team consisting of marine biologists, computer scientists and psychologists, adhering to the requirements by Department of Fisheries and Ocea...

Here, we provide two 3-D X-ray CT volumes and the segmented 32 slips of a wooden scroll bought in Beijing. The scroll is made of bamboo with carved writings and drawings. The first scan we made was a initial scan of the scroll without any soiling. As the scrolls are normally found underneath the ea...

Here, we provide the data sets used in our research on multi-sensor super-resolution for hybrid range imaging. In the current state, the database contains synthetic as well as real datasets acquired for different applications and modalities (e.g. hybrid 3-D endoscopy based on Ti...

Here, we present the 25 frames used for situs reconstruction in our MICCAI publication ("3-D Operation Situs Reconstruction With Time-of-Flight Satellite Cameras Using Photogeometric Data Fusion"). The single frames are included as vtk files and the complete sequence with all frames is included as ...

We have created a database of five cell lines in order to assess the performance of cell detection algorithms. The database is composed of more than 3500 cells in 16 images of three real cell lines and 30000 cells in 200 images of two simulated cell lines. Download and more information

RabbitCT is an open-source benchmark for C-arm CT FBP reconstruction. It features a clincal C-arm CT dataset with 496 projections, and a software framework for measuring execution time and calculating reconstruction errors.For more information please visit www.rabbitct.com.