  In this paper, we reconsider the early computer vision bottom-up program,
according to which higher level features (geometric structures) in an image
could be built up recursively from elementary features by simple grouping
principles coming from Gestalt theory. Taking advantage of the (recent)
advances in reliable line segment detectors, we propose three feature detectors
that constitute one step up in this bottom up pyramid. For any digital image,
our unsupervised algorithm computes three classic Gestalts from the set of
predetected line segments: good continuations, nonlocal alignments, and bars.
The methodology is based on a common stochastic {\it a contrario model}
yielding three simple detection formulas, characterized by their number of
false alarms. This detection algorithm is illustrated on several digital
images.
