Regular Polygon Detection
Gareth Loy (KTH Sweden, Sweden)
This talk describes a new robust regular polygon detector. The regular
polygon transform is posed as a mix- ture of regular polygons in a five
dimensional space. Given the edge structure of an image, we derive the a
posteriori probability for a mixture of regular polygons, and thus the
probability density function for the appearance of a mix- ture of
regular polygons. Likely regular polygons can be isolated quickly by
discretising and collapsing the search space into three dimensions. The
remaining dimensions may be efficiently recovered subsequently using
maximum likelihood at the locations of the most likely polygons in the
subspace. This leads to an efficient algorithm. Also the a posteriori
formulation facilitates inclusion of additional a priori information
leading to real-time application to road sign detection. The use of
gradient information also reduces noise compared to existing approaches
such as the gener- alised Hough transform. Results are presented for
images with noise to show stability.
The detector is also applied to two separate applications: real-time
road sign detection for on-line driver assistance; and feature
detection, recovering stable features in rectilinear environments.