  While robotic manipulation of rigid objects is quite straightforward, coping
with deformable objects is an open issue. More specifically, tasks like tying a
knot, wiring a connector or even surgical suturing deal with the domain of
Deformable Linear Objects (DLOs). In particular the detection of a DLO is a
non-trivial problem especially under clutter and occlusions (as well as
self-occlusions). The pose estimation of a DLO results into the identification
of its parameters related to a designed model, e.g. a basis spline. It follows
that the stand-alone segmentation of a DLO might not be sufficient to conduct a
full manipulation task. This is why we propose a novel framework able to
perform both a semantic segmentation and b-spline modeling of multiple
deformable linear objects simultaneously without strict requirements about
environment (i.e. the background). The core algorithm is based on biased random
walks over the Region Adiacency Graph built on a superpixel oversegmentation of
the source image. The algorithm is initialized by a Convolutional Neural
Networks that detects the DLO's endcaps. An open source implementation of the
proposed approach is also provided to easy the reproduction of the whole
detection pipeline along with a novel cables dataset in order to encourage
further experiments.
