Paper Info
Title
Let's take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation.
Abstract
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 DLOu0027s 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.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Spline (mathematics),Graph,Pattern recognition,Convolutional neural network,Computer science,Clutter,Random walk,Segmentation,Pose,Artificial intelligence,Knot (unit)
DocType
Volume
Citations 
Journal
abs/1810.04461
0
PageRank 
References 
Authors
0.34
16
3
Name
Order
Citations
PageRank
Daniele De Gregorio183.55
Gianluca Palli226829.98
Luigi Di Stefano3173288.17