This module dives into feature extraction—quantitative measures that describe image content. Students compute features such as image mass, center, and statistical moments to describe the shape and structure of images. These are implemented both manually and in Excel. The module also explores how to compare images using distance metrics and similarity measures, offering insight into how visual data can be analyzed, categorized, and classified.

Filtering techniques are central to detecting patterns in images. This module introduces learners to 1D and 2D filters, covering foundational concepts like convolution, cross-correlation, and Gaussian smoothing. Through both manual and spreadsheet-based exercises, learners apply various filters (e.g., mean, Laplacian, Sobel) and morphological operations like dilation and erosion. These filtering methods enhance image features, detect edges, and prepare data for further processing.

This module delves into key concepts of camera models and their role in computer vision and photogrammetry. You will learn about the Extrinsic Matrix, exploring how it defines the position and orientation of a camera in 3D space. Understand the Pinhole Camera Model, a simplified optical system that forms the basis for many computer vision applications, alongside the Intrinsic Matrix, which captures the internal parameters of the camera. Epipolar geometry is examined, with a focus on its significance in 3D reconstruction and stereo vision. The module covers the motivation behind epipolar geometry, breaking down its basic components, and explaining the Essential Matrix, which encapsulates the geometric relationship between camera views, as well as the Fundamental Matrix, a core component in epipolar geometry that represents the relationship between two cameras in stereo vision.