TY - GEN

T1 - Discriminative Shape Alignment

AU - Loog, M.

AU - de Bruijne, M.

N1 - Conference code: 21

PY - 2009

Y1 - 2009

N2 - The alignment of shape data to a common mean before its subsequent processing is an ubiquitous step within the area shape analysis. Current approaches to shape analysis or, as more specifically considered in this work, shape classification perform the alignment in a fully unsupervised way, not taking into account that eventually the shapes are to be assigned to two or more different classes. This work introduces a discriminative variation to well-known Procrustes alignment and demonstrates its benefit over this classical method in shape classification tasks. The focus is on two-dimensional shapes from a two-class recognition problem.

AB - The alignment of shape data to a common mean before its subsequent processing is an ubiquitous step within the area shape analysis. Current approaches to shape analysis or, as more specifically considered in this work, shape classification perform the alignment in a fully unsupervised way, not taking into account that eventually the shapes are to be assigned to two or more different classes. This work introduces a discriminative variation to well-known Procrustes alignment and demonstrates its benefit over this classical method in shape classification tasks. The focus is on two-dimensional shapes from a two-class recognition problem.

U2 - 10.1007/978-3-642-02498-6_38

DO - 10.1007/978-3-642-02498-6_38

M3 - Article in proceedings

SN - 978-642-02497-9

VL - 5636/2009

T3 - Lecture notes in computer science

SP - 459

EP - 466

BT - Information Processing in Medical Imaging

PB - Springer

Y2 - 5 July 0009 through 10 July 0009

ER -