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Proceedings Paper

Nonlinear spectral preprocessing for small-brain machine learning
Author(s): Luat T. Vuong; Hobson Lane
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Paper Abstract

Substantial computing costs are required to use deep-learning algorithms. Here, we implement feature extraction based on analytic relations in the Fourier-transform domain. In an example relevant to visual odometry, we demonstrate a reduction in algorithmic complexity with cross-power spectral preprocessors for feature extraction in lieu of learned convolutional filters. With spectral reparameterization and spectral pooling, not only can the optical flow (spatial disparity of images in a sequence) be computed, but occluding objects can also be tracked in the foreground without deep learning. There is evidence that insects with small brains implement similar visual-data spectral preprocessors, which may be critical in the development of future real-time machine learning applications.

Paper Details

Date Published: 6 September 2019
PDF: 9 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390T (6 September 2019);
Show Author Affiliations
Luat T. Vuong, Univ. of California, Riverside (United States)
Hobson Lane, Total Good (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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