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!FTL, an Articulation-Invariant Stroke Gesture Recognizer with Controllable Position, Scale, and Rotation Invariances

Published: 02 October 2018 Publication History

Abstract

Nearest neighbor classifiers recognize stroke gestures by computing a (dis)similarity between a candidate gesture and a training set based on points, which may require normalization, resampling, and rotation to a reference before processing. To eliminate this expensive preprocessing, this paper introduces a vector-between-vectors recognition where a gesture is defined by a vector based on geometric algebra and performs recognition by computing a novel Local Shape Distance (LSD) between vectors. We mathematically prove the LSD position, scale, and rotation invariance, thus eliminating the preprocessing. To demonstrate the viability of this approach, we instantiate LSD for n=2 to compare !FTL, a 2D stroke-gesture recognizer with respect to $1 and $P, two state-of-the-art gesture recognizers, on a gesture set typically used for benchmarking. !FTL benefits from a recognition rate similar to $P, but a significant smaller execution time and a lower algorithmic complexity.

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Appendices of the paper with pseudo-code and video demonstrations
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cover image ACM Other conferences
ICMI '18: Proceedings of the 20th ACM International Conference on Multimodal Interaction
October 2018
687 pages
ISBN:9781450356923
DOI:10.1145/3242969
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 October 2018

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Author Tags

  1. articulation invariance
  2. isochronicity
  3. isometricity
  4. isoparameterization
  5. local shape distance
  6. stroke gesture recognition

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ICMI '18
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  • SIGCHI

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ICMI '18 Paper Acceptance Rate 63 of 149 submissions, 42%;
Overall Acceptance Rate 453 of 1,080 submissions, 42%

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