[Tyler]

Robust Optical Flow Model Derived From Biology
     


Optical Flow
image processing algorithms have been around for a while
but have yet to match the motion detection capabilities of insects. In
particular current models tend to fail in real-world situations which
include high-dynamic range as well as rapidly changing contrast,
velocity, and acceleration. Russel
Brinkworth and David
O'Carroll of the University of
Adelaide in Australia explain:
The extraction of accurate self-motion information from the
visual world is a difficult problem that has been solved very
efficiently by biological organisms utilizing non-linear processing.
Previous bio-inspired models for motion detection based on a correlation
mechanism have been dogged by issues that arise from their sensitivity
to undesired properties of the image, such as contrast, which vary
widely between images.

Brinkworth and O'Carroll have come up with a new optical flow model,
described in their paper, Robust
Models for Optic Flow Coding in Natural Scenes Inspired by Insect
Biology (PDF format). Their new system is based on the actual neural
processing pathways of the fly which could prove to be a very robust
velocity estimator and accurate sensor for self-motion in robots.
CC-licensed
image of female Tabanus Horse Fly by flickr user Thomas Shahan.
   

http://robots.net/article/2945.html