Image Processing I: Filters and Transformations

This lecture covers the implementation of moving average filters, including recursive implementation in 1D and separable transfer functions. It also discusses symmetric exponential filters, exponential filtering implementation, and linear scale-space concepts. The instructor explains the Central-limit theorem, efficient Gaussian filtering, and the nomenclature of prototypical filters. The lecture concludes with a summary on discrete images, Fourier transforms, digital filtering, and popular image-processing filters.