Applied Image Processing

Design of industrial machine vision systems - importance of a visual sense for machines, role of understanding in vision, machine vision in context, system design methodology, how can industrial machine vision be justified?; scene constraints - general principles, lighting techniques, lamps, optics, achieving robust solutions; image acquisition - representation of the image data, image transduction (or sensing), digitization; image preprocessing - introduction and theoretical background, point operations, global operations, neighbourhood operations, geometric operations, temporal (frame-based) operations; segmentation - introduction, pixel-based or local methods, region-based or global methods; feature extraction - image features, image codes, boundary-based features, mathematical morphology; pattern classification - introduction, statistical methods, syntatic methods, neural network approaches; image understanding - towards universal capability - image formation and the visual processes, Marr's computational theory of vision, image representations, pragmatic modelling and matching strategies, the beginning of the end?; image processing case histories - industrial machine vision applications, space exploration, astronomy, diagnostic medical imaging, scientific analysis, military guidance and reconnaissance, remote sensing, information technology systems, telecommunications, security surveillance and law enforcement, entertainment and consumer electronics, printing and the graphic arts. Appendices: TV line format; textual dispalys.