Edward R. Dougherty is a professor in the Department of Electrical Engineering at Texas A&M University in College Station. He holds a Ph.D. in mathematics from Rutgers University and an M.S. in Computer Science from Stevens Institute of Technology. He is author of 11 books and over 100 journal papers. He has also edited four collections. He is an SPIE fellow and served as editor of the Journal of Electronic Imaging for six years. Prof. Dougherty was an originator of the automatic design of morphological operators for image processing and the consequent application of pattern recognition theory to nonlinear image processing.

The key to successful morphological image processing is the selection of structuring elements. The classical approach is to have an imaging scientist use intuition and experience to design algorithms based on erosions, openings, hit-or-miss transforms and other basic morphological operators. This approach can work well if the task can be described in elementary geometric terms and the images under consideration are not too complex. It breaks down in situations where satisfactory filtering might require hundreds, or even thousands, of structuring elements. This talk discusses automatic algorithm design, where morphological operators are designed based on sample data, structural decomposition, and criteria set by the imaging scientist.