Image Processing II: Gaussian Classifiers and Neural Networks

This lecture covers the application of multivariate Gaussian distribution in image processing, including the minimum-error Gaussian classifier and Bayesian classification. It also explores texture classification using wavelets and the evaluation of texture-classification results. Additionally, it delves into special cases of Gaussian classifiers, such as the minimum-distance classifier and the Mahalanobis-distance classifier. The lecture further discusses parameter estimation, supervised learning, and nearest-neighbor classification. It concludes with an overview of discriminant functions, optimization techniques, and the emergence of deep learning, focusing on convolutional neural networks.

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