Image Processing with Elements of Learning

Catalog description

This class will provide the students with an introduction to image processing, with applications to science, medicine, and industry. Students will learn methods for 2D image enhancement, sharpening, blurring, noise detection, modeling, and cleaning, as well as edge detection in gray-level images. The students will learn and will be able to implement methods like local statistics, convolution, Laplacian and Gradient operators, Fourier transforms, and the Fast Fourier Transform. Further, the teacher will introduce basic elements of neural networks (NN) and machine learning(ML). At the end of the class, the students will know which gray-level image methods apply to color images. The students will develop skills in independent study, program, experiment, report, and present advanced methods from the field. Some programming skills would be of help. Prerequisites: MATH 2414 . Crosslisted with: CSCI 567 .