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.

Assessment Strategy

-threshold -Equivalent to 40%.Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

-good -Equivalent to the range 60%-69%.Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

-excellent -Equivalent to the range 70%+.Assemble critically evaluated, relevant areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

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.