Gaussian function

Related lectures (32)

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Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.

Explores the Spike-Wigner model, low-rank matrix factorization, and Gaussian matrices.

Covers wireless receivers in the presence of Gaussian noise and error probability estimation.

Explores spectral estimation for Gaussian and binary signals in the spiked matrix estimation problem, analyzing the impact of signal-to-noise ratio.

Explores classic image filters like Moving Average, Gaussian Blur, Sobel filter, and Laplacian operator.

Explores moving average and exponential filters, Gaussian filtering, and linear scale-space concepts in image processing.

Explores random walks, hitting probabilities, recursive structures, and Gaussian processes.

Explores the generation of multivariate Gaussian distributions and the challenges of factorizing covariance matrices.

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Explores kernel methods for nonlinear separation surfaces using polynomial and Gaussian kernels in Perceptron and SVM algorithms.