@article{richmond_cole_dangler_daniele_marchitto_lobaton_2022, title={Forabot: Automated Planktic Foraminifera Isolation and Imaging}, volume={23}, ISSN={["1525-2027"]}, url={http://dx.doi.org/10.1029/2022gc010689}, DOI={10.1029/2022GC010689}, abstractNote={AbstractPhysical inspection and sorting of foraminifera is a necessity in many research labs, as foraminifera serve as paleoenvironmental and chronostratigraphic indicators. In order to gain counts of species from samples, analyze chemical compositions, or extract morphological properties of foraminifera, research labs require human time and effort handling and sorting these microscopic fossils. The presented work describes Forabot, an open‐source system which can physically manipulate individual foraminifera for imaging and isolation with minimal human interaction. The major components to build a Forabot are outlined in this work, with supplementary information available which allows for other researchers to build a Forabot with low‐cost, off‐the‐shelf components. From a washed and sieved sample of hundreds of foraminifera, the Forabot is shown to be capable of isolating and imaging individual forams. The timing of the Forabot's current pipeline allows for the processing of up to 27 foram specimens per hour, a rate that can be improved for future classification purposes by reducing image quality and/or quantity. Along with the physical descriptions, the image processing and classification pipelines are also reviewed. A proof‐of‐concept classifier utilizes a finetuned VGG‐16 network to achieve a classification accuracy of 79% on a validation set of foraminifera images collected with Forabot. In conclusion, the system is able to be built by researchers for a low cost, effectively manipulate foraminifera with few mistakes, provide quality images for future research, and classify the species of imaged forams.}, number={12}, journal={GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS}, publisher={American Geophysical Union (AGU)}, author={Richmond, Turner and Cole, Jeremy and Dangler, Gabriella and Daniele, Michael and Marchitto, Thomas and Lobaton, Edgar}, year={2022}, month={Dec} }
 @article{ge_richmond_zhong_marchitto_lobaton_2021, title={Enhancing the morphological segmentation of microscopic fossils through Localized Topology-Aware Edge Detection}, volume={45}, ISSN={["1573-7527"]}, url={https://doi.org/10.1007/s10514-020-09950-9}, DOI={10.1007/s10514-020-09950-9}, number={5}, journal={AUTONOMOUS ROBOTS}, publisher={Springer Science and Business Media LLC}, author={Ge, Qian and Richmond, Turner and Zhong, Boxuan and Marchitto, Thomas M. and Lobaton, Edgar J.}, year={2021}, month={Jun}, pages={709–723} }
 @inproceedings{richmond_lokare_lobaton_2017, title={Robust trajectory-based density estimation for geometric structure recovery}, url={http://dx.doi.org/10.23919/eusipco.2017.8081400}, DOI={10.23919/eusipco.2017.8081400}, abstractNote={We propose a method to both quickly and robustly extract geometric information from trajectory data. While point density may be of interest in some applications, trajectories provide different guarantees about our data such as path densities as opposed to location densities provided by points. We aim to utilize the concise nature of quadtrees in two dimensions to reduce run time complexity of counting trajectories in a neighborhood. We compare the accuracy of our methodology to a common current practice for subsampling a structure. Our results show that the proposed method is able to capture the geometric structure. We find an improvement in performance over the current practice in that our method is able to extract only the salient data and ignore trajectory outliers.}, booktitle={2017 25th European Signal Processing Conference (EUSIPCO)}, publisher={IEEE}, author={Richmond, Turner and Lokare, Namita and Lobaton, Edgar}, year={2017}, month={Aug}, pages={1210–1214} }