title: Searching the sky with CONFIGR-STARS.
abstract: CONFIGR-STARS, a new methodology based on a model of the human visual system, is developed for registration of star images. The algorithm first applies CONFIGR, a neural model that connects sparse and noisy image components. CONFIGR produces a web of connections between stars in a reference starmap or in a test patch of unknown location. CONFIGR-STARS splits the resulting, typically highly connected, web into clusters, or "constellations". Cluster geometry is encoded as a signature vector that records edge lengths and angles relative to the cluster's baseline edge. The location of a test patch cluster is identified by comparing its signature to signatures in the codebook of a reference starmap, where cluster locations are known. Simulations demonstrate robust performance in spite of image perturbations and omissions, and across starmaps from different sources and seasons. Further studies would test CONFIGR-STARS and algorithm variations applied to very large starmaps and to other technologies that may employ geometric signatures. Open-source code, data, and demos are available from http://techlab.bu.edu/STARS/.
authors.affiliation: Center for Adaptive Systems, Boston University, 677 Beacon Street, Boston, MA 02215, USA. gail@cns.bu.edu
authors.forename: Gail A
authors.initials: GA
authors.lastname: Carpenter
authors.forename: Arun K
authors.initials: AK
authors.lastname: Ravindran
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D014785:Vision, Ocular / Q000502:physiology