Lectures related to Image Processing II: Optimal Transforms and Applications

Explores the Gram-Schmidt process to obtain orthogonal vectors and bases.

Explores cross-correlation, signal detection, Hilbert spaces, orthogonal approximation, and image compression using DCT.

Explores wavelets as orthonormal bases for piecewise constant signals over unit intervals.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

Covers signal representations using concepts such as Haar wavelets and FIR filters.

Covers tensor products, symmetric power, and exterior power of vector spaces, including properties and applications.

Introduces the first step in finding an orthogonal/orthonormal base in a vector space.

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.