Bone graphs: Medial shape parsing and abstraction

► A novel shape parsing approach based on the medial axis is presented. ► A shape is decomposed into a set of salient skeletal parts and their relations. ►The parts and relations are captured in a hierarchical abstraction - the bone graph. ► The bone graph avoids the ligature-based instability of a...

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Veröffentlicht in:Computer vision and image understanding Jg. 115; H. 7; S. 1044 - 1061
Hauptverfasser: Macrini, Diego, Dickinson, Sven, Fleet, David, Siddiqi, Kaleem
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.07.2011
Schlagworte:
Bones
C (programming language)
Computer vision
Descriptions
Graph-based object representation
Graphs
Medial shape representation
Recognition
Representations
Shape parsing
Shape parsing
Graph-based object representation
Medial shape representation
ISSN:1077-3142, 1090-235X
Online-Zugang:Volltext
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Zusammenfassung:► A novel shape parsing approach based on the medial axis is presented. ► A shape is decomposed into a set of salient skeletal parts and their relations. ►The parts and relations are captured in a hierarchical abstraction - the bone graph. ► The bone graph avoids the ligature-based instability of a medial axis. ► The bone graph offers improved performance in view-based object recognition. The recognition of 3-D objects from their silhouettes demands a shape representation which is stable with respect to minor changes in viewpoint and articulation. This can be achieved by parsing a silhouette into parts and relationships that do not change across similar object views. Medial descriptions, such as skeletons and shock graphs, provide part-based decompositions but suffer from instabilities. As a result, similar shapes may be represented by dissimilar part sets. We propose a novel shape parsing approach which is based on identifying and regularizing the ligature structure of a medial axis, leading to a bone graph, a medial abstraction which captures a more stable notion of an object’s parts. Our experiments show that it offers improved recognition and pose estimation performance in the presence of within-class deformation over the shock graph.
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ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2010.12.011