Astrostatistics Image Penn State University Eberly College of Science Center for Astrostatistics Center for Astrostatistics

 
CASt online resources

Pattern recognition
Astronomical Image and Data Analysis
    by Jean-Luc Starck & Fionn Murtagh (2nd ed, 2006). Survey of modern methods of image, signal and data processing with emphasis on astronomical applications. Covers filtering, deconvolution, image compression, multichannel data, catalog analysis, data storage & retrieval, multiresolution methods (wavelet & a trous transform).

Pattern Classification
    by Trevor Hastie, Robert Tibishrani & Jerome Friedman (2001). Well-respected text on computational methods for image and signal analysis with MATLAB software. Includes maximum likelihood and Bayesian estimation (PCA, Gibbs algorithm, EM algorithm, hidden Markov models), nonparametric techniques (density estimation, nearest neighbor), multilayer neural networks, annealing & genetic algorithms, tree methods, machine learning (minimum description length, bootstrap, boosting), unsupervised clustering (k-means, Bayesian classfiers).

Image Processing and Data Analysis: The Multiscale Approach
    by Jean-Luc Starck, Fionn Murtagh and Albert Bijaoui (1988). Presentation of wavelet methods for image analysis with emphasis on astronomical applications. Covers continuous and discrete wavelet transforms, median transforms, multiresolution support and filtering, deconvolution (including Lucy-Richardson algorithm, maximum entropy, and CLEAN), multiresolution regression and forecasting, geometric registration, disparity mapping (including kriging), image compression, source detection, and multiscale vision models.

Pattern Recognition and Neural Networks
    by B. D. Ripley (1996). Readable monograph on issues arising in multivariate and image analysis, machine learning and computer vision. Covers statistical decision theory, linear discriminant analysis, feed-forward neural networks, non-parametric methods, tree-based classifiers, belief networks, and unsupervised methods.
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