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In this course students will be introduced to the field of computer vision. The structure of the course will be to first provide an overview and motivation for each of the stages in a typical computer vision system. Each of these stages will then be addressed in turn, where a selection of problems, techniques and algorithms from each stage will be presented.
Although the content of the course may vary from year to year, the course will have a structure based on the following topics:
1. Introduction (human visual perception, basic principals of industrial machine vision systems).
2. Image formation (lenses, sensors, and signals, the geometry of imaging, camera modelling and calibration).
3. Binary image processing (image representations, segmentation, image moments, mathematical morphology, region representations).
4. Image filtering (convolution, frequency domain representations, noise removal filters).
5. Gradient based edge detection.
6. Selected topics in Image analysis (template matching, image based pattern recognition, shape detection, interest point detection & matching).
The final part of the course will involve students completing a project based assignment whereby they will be required to design and implement a solution to a real-world computer vision problem.