Bubble tag identification using an invariant-under-perspective signature

We have at our disposal a large database containing images of various configurations of coplanar circles, randomly laid-out, called "Bubble Tags". The images are taken from different viewpoints. Given a new image (query image), the goal is to find in the database the image containing the same bubble tag as the query image. We propose representing the images through projective invariant signatures which allow identifying the bubble tag without passing through an Euclidean reconstruction step. This is justified by the size of the database, which imposes the use of queries in 1D/vectorial form, i.e. not in 2D/matrix form. The experiments carried out confirm the efficiency of our approach, in terms of precision and complexity. © 2010 IEEE

"Bubble tag"-based system for object authentication

Biometric systems are omnipresent nowadays in fields that require user authentication (e.g.: access control, banking operations), due to the main attributes of the biometric characteristics: uniqueness, permanence, collectability. In the products world, a solution that could achieve the same performance as a biometric system could be represented by a "Bubble Tag"-based system. In this article we propose solutions for signature extraction and for "1 to many" authentication protocol applicable to the Bubble Tag. We briefly present a signature extraction method that is invariant under perspective. For the "1 to many" authentication protocol we recommend the use of an LSH (locality sensitive hashing) approach. Tests carried out on randomly computer-generated images gave promising results and indicated leads to be followed for real images. ©2010 IEEE

A parameterless line segment and elliptical arc detector with enhanced ellipse fitting

We propose a combined line segment and elliptical arc detector, which formally guarantees the control of the number of false positives and requires no parameter tuning. The accuracy of the detected elliptical features is improved by using a novel non-iterative ellipse fitting technique, which merges the algebraic distance with the gradient orientation. The performance of the detector is evaluated on computer-generated images and on natural images. © 2012 Springer-Verlag