The dataset contains foraminifera images of over 1,000 forams taken under 16 different lighting directions with an optical microscope. The species and locations of the samples are also specified. It also contains manual segmentation of over 400 samples from the images described above. The segmentation labels are matched by their name.
To capture these images, a visual identification system was developed in order to automate the identification of target microorganisms. The visual system incorporates a controllable LED lighting ring used to capture images by illuminating the specimens from several directions, mimicking an important step in the traditional identification process.
The dataset was originally used for foraminifera identification and segmentation with machine learning and computer vision techniques. This work is a collaboration between the Dr. Edgar Lobaton (Associate Professor at the North Carolina State University), Dr. Thomas Marchitto (Associate Professor at the University of Colorado Boulder) and Dr. Ritayan Mitra (Assistant Professor at IIT Bombay).
Please refer to https://research.ece.ncsu.edu/aros/foram-identification/ for more information about the datasets, related studies and downloading the dataset
