65 citations · 91 across the 5 of their papers we have counts for
5 papers
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg +2
Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consi…
A tomographic workflow to enable deep learning for X-ray based foreign object detection
Mathé T. Zeegers, Tristan van Leeuwen, Daniël M. Pelt +3
Detection of unwanted (`foreign') objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasi…
Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning
Sophia Bethany Coban, Vladyslav Andriiashen, Poulami Somanya Ganguly +2
We present three parallel-beam tomographic datasets of 94 apples with internal defects along with defect label files. The datasets are prepared for development and testing of data-…
A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten +3
Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high…
Non-linearity in monochromatic transmission tomography
William R. B. Lionheart, Bjørn Tore Hjertaker, Rachid Maad +3
While it is well known that X-ray tomography using a polychromatic source is non-linear, as the linear attenuation coefficient depends on the wavelength of the X-rays, tomography u…