title: Discriminative shape alignment.
abstract: 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.
authors.affiliation: Pattern Recognition Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands. m.loog@tudelft.nl
authors.forename: Marco
authors.initials: M
authors.lastname: Loog
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003337:Corpus Callosum / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000473:pathology*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique