Automated whisker tracking is important for researching active touch in rodents. Earlier efforts to detect whiskers and represent them in a small set of parameters were either not accurate enough to enable tracking over time, or computationally expensive. In this article we propose an algorithm to cluster whisker centerline points, detected through a curvilinear structure algorithm, using the shape of smaller clusters to form bigger clusters of centerline points. After that, a least-squares approach is used to define each whisker by a set of four parameters. We implemented the algorithm in MATLAB in a parallelized fashion, and found that the processing time per frame is reasonable in MATLAB, and is likely to be short when ported to a lower-level language. When tested on a 33,634-frame segment, 89.2% of the whiskers could be represented in an abstract fashion by four parameters with a mean-squares fitting error of lower than 10 pixels, and visual inspection shows that crossing whiskers are detected and parameterized in an accurate way.

Biology computing, Clustering methods, High performance computing, Image processing, MATLAB, Parallel algorithms, Parallel processing,
11th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2020
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Erasmus MC: University Medical Center Rotterdam

Betting, J.-H.L.F. (Jan-Harm L.F.), Romano, V, Bosman, L.W.J, Al-Ars, Z, de Zeeuw, C.I, & Strydis, C. (2020). Stairway to Abstraction: An Iterative Algorithm for Whisker Detection in Video Frames. In 2020 IEEE 11th Latin American Symposium on Circuits and Systems, LASCAS 2020. doi:10.1109/LASCAS45839.2020.9068992