Continuous Generalized Procrustes analysis

Laura Igual, Xavier Perez-Sala, Sergio Escalera, Cecilio Angulo, Fernando De La Torre

    Research output: Contribution to journalArticleResearchpeer-review

    16 Citations (Scopus)

    Abstract

    Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects. To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA). CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. © 2013 Elsevier Ltd.
    Original languageEnglish
    Pages (from-to)659-671
    JournalPattern Recognition
    Volume47
    Issue number2
    DOIs
    Publication statusPublished - 1 Feb 2014

    Keywords

    • 2D shape model
    • Continuous approach
    • Procrustes analysis

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