Feature computation and classification of grating pitch.

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Presentation transcript:

Feature computation and classification of grating pitch. Feature computation and classification of grating pitch. (A) Features extracted from an example trial where an ACES-FA receptor array is moved over a grating pattern with 4-mm pitch. Red trace in top panel indicates estimated tangential speed using ACES, whereas the dotted blue trace is the ground truth speed derived from frequency components (see Materials and Methods). Bottom panel is a 2D histogram of event frequencies over time. Color codes correspond to bin counts C normalized by total counts ΣC. Magenta box highlights the 100 × 1 vector of bin counts for classification. (B) Selected trials illustrating outputs from five different grating sizes. (C) Classification accuracy of grating sizes obtained when sampled at different rates. Error bars denote SD. (D) Comparison of classification accuracy achievable (with speed estimates) based on data rate consumed. For event-based output, the vertical line in the box plot is the median data rate, the caps indicate 1st to 99th percentiles, and the sides of the box indicate 25th to 75th percentiles of distribution. Wang Wei Lee et al. Sci. Robotics 2019;4:eaax2198 Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works