Pattern recognition and image preprocessing

TL;DR: Data preprocessing for pictorial pattern recognition: preprocessing in the spatial domain pictorial data preposessing and shape analysis transforms and image processing in the transform doamin wavelets and wavelet transforms.

Abstract: Pattern recognition: supervised and unsupervised learning in pattern recognition nonparametric decision theoretic classification nonparametric (distribution-free) training of discriminant functions statistical discriminant functions clusteringanalysis and unsupervised learning dimensionality reduction and feature selection. Neural networks for pattern recognition: multilayer perception radial basis function networks hamming net and Kohonen self-organizing feature map the Hopfield model.Data preprocessing for pictorial pattern recognition: preprocessing in the spatial domain pictorial data preposessing and shape analysis transforms and image processing in the transform doamin wavelets and wavelet transforms. Applications: exemplaryapplications. Practical concerns of image processing and pattern recognition: computer system architectures for image processing and pattern recognition. Appendices: digital images image model and discrete mathematics digital image fundamentals matrixmanipulation Eigenvectors and Eigenvalves of an operator notation.