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

Detection and identification of elliptical structure arrangements in images: theory and algorithms

Cette thèse porte sur différentes problématiques liées à la détection, l'ajustement et l'identification de structures elliptiques en images. Nous plaçons la détection de primitives géométriques dans le cadre statistique des méthodes a contrario afin d'obtenir un détecteur de segments de droites et d'arcs circulaires/elliptiques sans paramètres et capable de contrôler le nombre de fausses détections. Pour améliorer la précision des primitives détectées, une technique analytique simple d'ajustement de coniques est proposée ; elle combine la distance algébrique et l'orientation du gradient. L'identification d'une configuration de cercles coplanaires en images par une signature discriminante demande normalement la rectification Euclidienne du plan contenant les cercles. Nous proposons une technique efficace de calcul de la signature qui s'affranchit de l'étape de rectification ; elle est fondée exclusivement sur des propriétés invariantes du plan projectif, devenant elle même projectivement invariante. ABSTRACT : This thesis deals with different aspects concerning the detection, fitting, and identification of elliptical features in digital images. We put the geometric feature detection in the a contrario statistical framework in order to obtain a combined parameter-free line segment, circular/elliptical arc detector, which controls the number of false detections. To improve the accuracy of the detected features, especially in cases of occluded circles/ellipses, a simple closed-form technique for conic fitting is introduced, which merges efficiently the algebraic distance with the gradient orientation. Identifying a configuration of coplanar circles in images through a discriminant signature usually requires the Euclidean reconstruction of the plane containing the circles. We propose an efficient signature computation method that bypasses the Euclidean reconstruction; it relies exclusively on invariant properties of the projective plane, being thus itself invariant under perspective