@article{martin_holder_nichols_park_roberts_bozkurt_2022, title={Comparing Accelerometry and Depth Sensing-Based Computer Vision for Canine Tail Wagging Interpretation}, DOI={10.1145/3565995.3566025}, abstractNote={This paper presents a preliminary effort to evaluate alternative sensing modalities for automated, high-resolution tracking of dog tail position and movement as a behavioral communication tool. We compare two different methods: (1) inertial measurement devices placed on dog outfits, and (2) remotely positioned cameras supported with custom vision-based tail wag detection algorithms. The small size and non-invasiveness of the inertial sensors and the non-contact and remote nature of the camera system both promote subject comfort and continuous signal acquisition while not affecting the mechanics of dog tail movement. The preliminary findings support that the higher-resolution and continuous interpretations on the dog tail movements and positions can pave the way for assessing their emotional states and designing more appropriate training and play environments.}, journal={NINTH INTERNATIONAL CONFERENCE ON ANIMAL-COMPUTER INTERACTION, ACI 2022}, author={Martin, Devon and Holder, Timothy and Nichols, Colt and Park, Jeremy and Roberts, David and Bozkurt, Alper}, year={2022} }
 @article{graham_park_billings_hulse-kemp_haigler_lobaton_2022, title={Efficient imaging and computer vision detection of two cell shapes in young cotton fibers}, volume={11}, ISSN={["2168-0450"]}, url={https://doi.org/10.1002/aps3.11503}, DOI={10.1002/aps3.11503}, abstractNote={AbstractPremiseThe shape of young cotton (Gossypium) fibers varies within and between commercial cotton species, as revealed by previous detailed analyses of one cultivar of G. hirsutum and one of G. barbadense. Both narrow and wide fibers exist in G. hirsutum cv. Deltapine 90, which may impact the quality of our most abundant renewable textile material. More efficient cellular phenotyping methods are needed to empower future research efforts.MethodsWe developed semi‐automated imaging methods for young cotton fibers and a novel machine learning algorithm for the rapid detection of tapered (narrow) or hemisphere (wide) fibers in homogeneous or mixed populations.ResultsThe new methods were accurate for diverse accessions of G. hirsutum and G. barbadense and at least eight times more efficient than manual methods. Narrow fibers dominated in the three G. barbadense accessions analyzed, whereas the three G. hirsutum accessions showed a mixture of tapered and hemisphere fibers in varying proportions.DiscussionThe use or adaptation of these improved methods will facilitate experiments with higher throughput to understand the biological factors controlling the variable shapes of young cotton fibers or other elongating single cells. This research also enables the exploration of links between early cell shape and mature cotton fiber quality in diverse field‐grown cotton accessions.}, journal={APPLICATIONS IN PLANT SCIENCES}, author={Graham, Benjamin P. and Park, Jeremy and Billings, Grant T. and Hulse-Kemp, Amanda M. and Haigler, Candace H. and Lobaton, Edgar}, year={2022}, month={Nov} }