Image Analysis and Recognition with Learning

Catalog description

This class will start with a study of the basic color image models. Next, the students will learn about scaling functions and calculus with them. Further will study the basics of wavelets and how to decompose a function to wavelets. Next, the students will learn about convolution-correlation, convolutional neural networks (CNN), and the fundamentals of machine learning (ML) and deep ML(DML). In the following stage, the students will learn basic image segmentation methods based on active contours (In case of time permission deep active contours). Further, they will learn about image and object representation and description, mainly boundary and region description. The following methods will be taught from the field of Recognition: Decision making; feature extraction. 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 569 .