Revisiting the Medial Axis for Planar Shape Decomposition

Papanelopoulos, Nikos and Avrithis, Yannis and Kollias, Stefanos (2019) Revisiting the Medial Axis for Planar Shape Decomposition. Computer Vision and Image Understanding . ISSN 1077-3142

Full content URL: https://doi.org/10.1016/j.cviu.2018.10.007

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Abstract

We present a simple computational model for planar shape decomposition that naturally captures most of the rules and salience measures suggested by psychophysical studies, including the minima and short-cut rules, convexity, and symmetry. It is based on a medial axis representation in ways that have not been explored before and sheds more light into the connection between existing rules like minima and convexity. In particular, vertices of the exterior medial axis directly provide the position and extent of negative minima of curvature, while a traversal of the interior medial axis directly provides a small set of candidate endpoints for part-cuts. The final selection follows a prioritized processing of candidate part-cuts according to a local convexity rule that can incorporate arbitrary salience measures. Neither global optimization nor differentiation is involved. We provide qualitative and quantitative evaluation and comparisons on ground-truth data from psychophysical experiments. With our single computational model, we outperform even an ensemble method on several other competing models.

Keywords:shape decomposition, visual parts, convexity, short cut rule, medial axis
Subjects:G Mathematical and Computer Sciences > G450 Multi-media Computing Science
G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:33974
Deposited On:13 Nov 2018 10:38

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