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Related lectures (18)

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Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Image Processing II: Optimal Transforms and Applications

Explores optimal transforms, image applications, linear algebra, and 2nd-order statistics in image processing.

JPEG 2000: Image Compression

Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Image Processing I

Introduces the basics of image processing, covering point and spatial operators, histogram processing, and transform-domain operations.

Feature Extraction & Clustering Methods

Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.

Image Processing II: Multiresolution Analysis and Wavelet Transform

Explores image pyramids, multiresolution analysis, wavelet transform, filterbanks, and orthogonal wavelet filters.

Image Processing II: B-spline Properties and Gradient Operators

Explores B-spline properties, gradient operators, interpolation, and differentiation filters in image processing.