Overview

Indicative content includes:

• Image formation, image representation, fundamentals of human luminance and color vision, segmentation, re-sampling. Solving problems using a modern library such as OpenCV,
• Point operations, convolution, linear filters, morphological operators, image histograms, and histogram equalization, 2D image transformations, image pyramids.
• Edge detectors, Hough transform, segmentation, feature detectors, feature descriptors, feature matching.
• Digital camera, display devices. colour calibration, gamma correction, limitations of human visual perception.
• More advanced computer vision topics, e.g. person tracking, trajectory, surveillance, security, controlling processes (e.g., robots), navigation, comp-human interaction (e.g., gestures), automatic inspection. Ethical considerations incl. data collection/management, informed consent, privacy, surveillance.

Learning Outcomes

• Apply image processing filters and operators to achieve given goals of an imaging system.

• Construct software to process image data, both in a general sense and for Computer Vision.

• Effectively use image representations to solve computer vision problems

• Give examples of computer vision applications, associate them with CV algorithms.

• Make informed decisions on the selection of imaging and display technologies for a task at hand.

• Use computer vision techniques to implement feature detection and tracking.