Normalized phase shift motion energy neuron populations for image velocity estimation

Authors:
Yicong Meng;Bertram E. Shi
Affiliations:
Electronic and Computer Engineering Department, Hong Kong University of Science and Technology, Hong Kong;Electronic and Computer Engineering Department, Hong Kong University of Science and Technology, Hong Kong
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 4
Cited 0

The Design and Use of Steerable Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
A coarse-to-fine disparity energy model with both phase-shift and position-shift receptive field mechanisms

Neural Computation
Binocular receptive field models, disparity tuning, and characteristic disparity

Neural Computation
Normalization enables robust validation of disparity estimates from neural populations

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Motion energy neurons are commonly used in biologically motivated algorithms for image velocity estimation. These algorithms typically use a large population of neurons tuned to different locations in the spatial-temporal frequency domain, with each neuron requiring a complex-valued separate spatio-temporal image filter. Here, we show that it is possible to construct a large population of motion energy neurons by combining the outputs of a much fewer number of filters with differing phase shifts. With spatial pooling, the velocity estimation using this phase-tuned population is more reliable than estimation using a more conventional frequency-tuned population. In addition, we show that by normalizing the population response, we can obtain a confidence measure for the resulting velocity estimates.