Optimizing random patterns for invariants-based identification

Maurizio Pilu

doi:10.1117/12.380052

16 March 2000 Optimizing random patterns for invariants-based identification

Maurizio Pilu

Proceedings Volume 3958, Three-Dimensional Image Capture and Applications III; (2000) https://doi.org/10.1117/12.380052
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

Pseudo-random point configurations can be used for many vision tasks, both active and passive. Examples are the projection of a pseudo-random light pattern to perform stereo matching or depth estimation by triangulation or robot navigation. The use of the local arrangement of the random features is attractive in some situations because labelling can be performed more robustly than by proliferating the feature types with other coding means. In this context the use of projective invariants provides either a classification measure or an indexing tool which is insensitive to surface position and camera geometry, which have proven invaluable to curb the complexity of the search by order of magnitudes. So far no work has been done on analyzing how these pseudo-random patterns should be like to make the use of invariants more effective, in particular with respect to discrimination and noise sensitivity. This paper addresses this problem for the common case of 5-point projective invariants. A stochastic approximation strategy is employed that iteratively adjusts the position of the points in the pattern while trying to maximize a spacing measure between the invariants. The result clearly illustrate the benefits of the approach which makes optimized pseudo random patterns of identical features a valid alternative to other forms of pattern coding for 3D capture.

Citation Download Citation

Maurizio Pilu "Optimizing random patterns for invariants-based identification", Proc. SPIE 3958, Three-Dimensional Image Capture and Applications III, (16 March 2000); https://doi.org/10.1117/12.380052

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available