  Junctions play an important role in the characterization of local geometric
structures in images, the detection of which is a longstanding and challenging
task. Existing junction detectors usually focus on identifying the junction
locations and the orientations of the junction branches while ignoring their
scales; however, these scales also contain rich geometric information. This
paper presents a novel approach to junction detection and characterization that
exploits the locally anisotropic geometries of a junction and estimates the
scales of these geometries using an \emph{a contrario} model. The output
junctions have anisotropic scales --- i.e., each branch of a junction is
associated with an independent scale parameter --- and are thus termed
anisotropic-scale junctions (ASJs). We then apply the newly detected ASJs for
the matching of indoor images, in which there may be dramatic changes in
viewpoint and the detected local visual features, e.g., key-points, are usually
insufficiently distinctive. We propose to use the anisotropic geometries of our
junctions to improve the matching precision for indoor images. Matching results
obtained on sets of indoor images demonstrate that our approach achieves
state-of-the-art performance in indoor image matching.
