Disconnected Skeleton: Shape at its Absolute Scale

TL;DR

This paper introduces a new disconnected skeletal representation for shape recognition that is stable, scale-invariant, and effective at coarse scales, addressing limitations of traditional connected skeletons.

Contribution

The authors propose a novel disconnected skeleton framework that improves stability and robustness in shape recognition by emphasizing shape properties at a coarse scale and using a global Euclidean frame.

Findings

Successful shape matching on diverse 2D shape database

Robustness to scale, position, orientation, and articulation changes

Avoids instability issues of traditional skeletons

Abstract

We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on the stable properties of the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of the skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones.