Prerequisites: multi-variable calculus and linear algebra. This course gives an overview of the theoretical and practical foundations of digital image processing, including random field models of images, sampling, quantization, image compression, enhancement, restoration, segmentation, shape description, reconstruction of pictures from their projections, pattern recognition. Applications include biomedical images, time-varying imagery, robotics and optics.