Skip Nav Destination
Close Modal
Update search
NARROW
Format
Journal
Date
Availability
1-2 of 2
Matthias O. Franz
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Publisher: Journals Gateway
Neural Computation (2006) 18 (12): 3097–3118.
Published: 01 December 2006
Abstract
View articletitled, A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression
View
PDF
for article titled, A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression
Volterra and Wiener series are perhaps the best-understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their application has been limited to rather low-dimensional and weakly nonlinear systems due to the exponential growth of the number of terms that have to be estimated. We show that Volterra and Wiener series can be represented implicitly as elements of a reproducing kernel Hilbert space by using polynomial kernels. The estimation complexity of the implicit representation is linear in the input dimensionality and independent of the degree of nonlinearity. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.
Journal Articles
Insect-Inspired Estimation of Egomotion
UnavailablePublisher: Journals Gateway
Neural Computation (2004) 16 (11): 2245–2260.
Published: 01 November 2004
Abstract
View articletitled, Insect-Inspired Estimation of Egomotion
View
PDF
for article titled, Insect-Inspired Estimation of Egomotion
Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge about the distance distribution of the environment and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.