Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Discriminative Training of Gaussian Mixtures for Image Object Recognition
Mustererkennung 1999, 21. DAGM-Symposium
A Probabilistic View on Tangent Distance
Mustererkennung 2000, 22. DAGM-Symposium
Structured Covariance Matrices for Statistical Image Object Recognition
Mustererkennung 2000, 22. DAGM-Symposium
Probabilistic Object Recognition Using Multidimensional Receptive Field Histograms
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Learning of Variability for Invariant Statistical Pattern Recognition
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Maximum Entropy and Gaussian Models for Image Object Recognition
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Adaptation in Statistical Pattern Recognition Using Tangent Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformation Models for Image Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Basic elements in the modelling of the problem of the image recognition and classification (IRC)
ELECTROSCIENCE'08 Proceedings of the 6th WSEAS International Conference on Applied Electromagnetics, Wireless and Optical
The complexity of the algorithms for the image recognition and classification (IRC)
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
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In this paper, we present a mixture density based approach to invariant image object recognition. To allow for a reliable estimation of the mixture parameters, the dimensionality of the feature space is optionally reduced by applying a robust variant of linear discriminant analysis. Invariance to affine transformations is achieved by incorporating invariant distance measures such as tangent distance. We propose an approach to estimating covariance matrices with respect to image variabilities as well as a new approach to combined classification, called the virtual test sample method. Application of the proposed classifier to the well known US Postal Service handwritten digits recognition task (USPS) yields an excellent error rate of 2.2%. We also propose a simple, but effective approach to compensate for local image transformations, which significantly increases the performance of tangent distance on a database of 1,617 medical radiographs taken from clinical daily routine.