Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Shape and the information in medical images: a decade of the morphometric synthesis
Computer Vision and Image Understanding
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Classifier Domains of Competence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Localized maximum entropy shape modelling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
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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.