  We introduce a bottom-up model for simultaneously finding many boundary
elements in an image, including contours, corners and junctions. The model
explains boundary shape in each small patch using a 'generalized M-junction'
comprising M angles and a freely-moving vertex. Images are analyzed using
non-convex optimization to cooperatively find M+2 junction values at every
location, with spatial consistency being enforced by a novel regularizer that
reduces curvature while preserving corners and junctions. The resulting 'field
of junctions' is simultaneously a contour detector, corner/junction detector,
and boundary-aware smoothing of regional appearance. Notably, its unified
analysis of contours, corners, junctions and uniform regions allows it to
succeed at high noise levels, where other methods for segmentation and boundary
detection fail.
