A fast fixed-point algorithm for independent component analysis
Neural Computation
2D Silhouette and 3D Skeletal Models for Human Detection and Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Discriminative components of data
IEEE Transactions on Neural Networks
Dealing with Non-linearity in Shape Modelling of Articulated Objects
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
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In this paper, we present a Statistical Shape Model for Human Figure Segmentation in gait sequences. Point Distribution Models (PDM) generally use Principal Component analysis (PCA) to describe the main directions of variation in the training set. However, PCA assumes a number of restrictions on the data that do not always hold. In this work, we explore the potential of Independent Component Analysis (ICA) as an alternative shape decomposition to the PDM-based Human Figure Segmentation. The shape model obtained enables accurate estimation of human figures despite segmentation errors in the input silhouettes and has really good convergence qualities.