New area matrix-based affine-invariant shape features and similarity metrics

Autor(es)

Dionisio, Carlos R. R. ; Kim, Hae Yong ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2006 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO - ICME 2006, VOLS 1-5, PROCEEDINGS; v. N/A, p. 2-pg., 2006-01-01.

Resumo

A near-planar object seen from different viewpoints results in differently deformed images. Under some assumptions, viewpoint changes can be modeled by affine transformations. Shape features that are affine-invariarit (af-in) must remain constant with the changes of the viewpoint. Similarly, shape similarity metrics that are af-in must rate the difference between two shapes, regardless of their viewpoints. Af-in shape features and similarity metrics can be used for the shape classification and retrieval. In this paper, we propose a new set of af-in shape features and similarity metrics. They are based on the area matrix, a structure that contains multiscale information about the shape. Experimental results indicate that the proposed techniques are robust to viewpoint changes and can rate correctly the dissimilarities between the shapes. (AU)

• Processo FAPESP: 03/13752-9 - Projeto de operadores pela aprendizagem e marca dagua de autenticacao no espaco de escala (poamae).
• Beneficiário: Hae Yong Kim
• Modalidade de apoio: Auxílio à Pesquisa - Regular