  In this work a system for recognizing grasp points in RGB-D images is
proposed. This system is intended to be used by a domestic robot when deploying
clothes lying at a random position on a table. By taking into consideration
that the grasp points are usually near key parts of clothing, such as the waist
of pants or the neck of a shirt. The proposed system attempts to detect these
key parts first, using a local multivariate contour that adapts its shape
accordingly. Then, the proposed system applies the Vessel Enhancement filter to
identify wrinkles in the clothes, allowing to compute a roughness index for the
clothes. Finally, by mixing (i) the key part contours and (ii) the roughness
information obtained by the vessel filter, the system is able to recognize
grasp points for unfolding a piece of clothing. The recognition system is
validated using realistic RGB-D images of different cloth types.
