ADR Awareness Centre

In machine learning, we use data to automatically find dependencies in the world, with the goal of predicting future observations.

This lecture overviews various digital image types: 2D images, 3D images (videos, medical volumes, hyperspectral images). Multichannel images, e.g., colour and multispectral images come next. RGBD images and graphics texture images.

This lecture overviews 2D Digital Filter Design and Implementation that has many applications in digital image filtering, computer vision (template matching) and convolutional neural networks .

This lecture overviews 2D Systems, as they are the primary tools for many image processing and analysis operations.  It covers the following topics in detail: Two-Dimensional Discrete LTI Systems. 2D convolutions. 2D correlation.

This lecture overviews digital image coordinate systems and their mathematical representations (vectors, matrices). Memory allocation issues are presented.

This lecture overviews spatial image frequency content and image sampling.  Rectangular and hexagonal sampling grids are presented. Sampled image frequency content is analyzed and a 2D version of Shannon theorem is presented.