In this paper we propose a new idea to design a measure for shape descriptors based on the concept of Q-convexity. The new measure extends the directional convexity measure defined in [2] to a two-dimensional convexity measure. The derived shape descriptors have the following features: (1) their values range from 0 to 1; (2) their values equal 1 if and only if the binary image is Q-convex; (3) they are invariant by reflection and point symmetry; (4) their computation can be easily and efficiently implemented.

Brunetti, S., Balazs, P., Bodnar, P. (2017). Extension of a one-dimensional convexity measure to two dimensions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.105-116). Cham : Springer Verlag [10.1007/978-3-319-59108-7_9].

Extension of a one-dimensional convexity measure to two dimensions

BRUNETTI, SARA;
2017-01-01

Abstract

In this paper we propose a new idea to design a measure for shape descriptors based on the concept of Q-convexity. The new measure extends the directional convexity measure defined in [2] to a two-dimensional convexity measure. The derived shape descriptors have the following features: (1) their values range from 0 to 1; (2) their values equal 1 if and only if the binary image is Q-convex; (3) they are invariant by reflection and point symmetry; (4) their computation can be easily and efficiently implemented.
2017
9783319591070
Brunetti, S., Balazs, P., Bodnar, P. (2017). Extension of a one-dimensional convexity measure to two dimensions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.105-116). Cham : Springer Verlag [10.1007/978-3-319-59108-7_9].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1022363