Electrical and Computer Engineering Department
University of Missouri-Columbia
Columbia, MO 65211
Phone: (314)882-7339
Fax: (314)882-0397
Email: kellerj@missouri.edu
James M. Keller received the Ph.D. in Mathematics in 1978. He currently holds the rank of Professor of Electrical and Computer Engineering at the University of Missouri-Columbia and is the R. L. Tatum Professor in the College of Engineering. His research interests center on computational intelligence: fuzzy set theory and fuzzy logic, neural networks, and evolutionary computation with a focus on problems in computer vision, pattern recognition, and information fusion including bioinformatics, spatial reasoning in robotics, sensor and information analysis in technology for eldercare, and landmine detection, where he has coauthored over 225 technical papers. Besides the ACM, Dr. Keller is a Fellow of the IEEE and is a past president of the North American Fuzzy Information Processing Society (NAFIPS). He was the editor-in-chief of the IEEE Transactions on Fuzzy Systems and the general chair of the 2003 IEEE International Conference on Fuzzy Systems.
The talk introduces the concepts required to utilize fuzzy set theory and fuzzy logic in practical applications. The discussion will focus on the theoretical basis and practical implementation of the use of fuzzy set theory and fuzzy logic in pattern recognition and control. The talk will step through the processes involved in a fuzzy logic control application. Emphasis will be placed on the computational nature of the events.
This lecture will trace the use of fuzzy set theory and fuzzy logic in the various stages of computer vision: image formation, enhancement, segmentation, feature extraction, object recognition, and high level scene interpretation. Emphasis will be placed on the research performed at the University of Missouri by Keller and his colleagues, particularly in the areas of mid and high level vision.
Why is it so hard to talk to a machine? If only we could communicate in a natural human language with robots, they would be so much more useful. Having machines that can reason spatially and receive and communicate such reasoning linguistically will extend their utility in many more scenarios that are dangerous, tedious, unhealthy, etc. Scene description, involving linguistic expressions of the spatial relationships between image objects, is a major goal of high-level computer vision. People have studied spatial relationships for several years. In a series of papers, we have introduced the use of histograms of forces to produce evidence for the description of relative position of objects in a digital image. Utilizing the fuzzy directional membership information extracted from these histograms within fuzzy logic rule-based systems, we have produced high-level linguistic descriptions of natural scenes as viewed by an external observer. Additionally, we have exploited the theoretical properties of the histograms to match images that may be the same scene viewed under different pose conditions. These linguistic descriptions have then been brought into an ego-centered viewpoint for application to robotics. I describe three activities here: production of linguistic scene description from a mobile robot standpoint, spatial language for human/robot communication and navigation, and understanding of a sketched route map for communicating navigation routes to robots.
In clustering and subsequent knowledge discovery on unknown gene products, the primary features to date are the gene sequence and expression values found following a microarray experiment. One major goal is to determine the function of this gene product and its similarity in function or structure to other up-regulated or down-regulated gene products. Many measures have been proposed to calculate closeness of sequences. However, for many gene products, additional information comes from the set of Gene Ontology (GO) annotations and the set of journal abstracts related to the gene product. For these genes, it is reasonable to include similarity measures based on the terms found in the GO and/or the index term sets of the related documents (MeSH annotations). In both cases we deal with comparing two sets of terms arranged in a taxonomy (GO or MeSH.).
In this talk we propose a fuzzy measure-based similarity (FMS) for computing the similarity of two sets of terms found in a taxonomy (and hence, the two gene products annotated with terms from the taxonomy). The advantage of FMS is that it takes into consideration the context of the whole set when computing the similarity. The initial testing on a group of 194 sequences representing three proteins families shows promising results when the similarity is presented visually and from the standpoint of hierarchical clustering utilizing the FMS. The visual representation of the similarities can help the human curator to assess the consistency of the members of an automated extracted family. In our experiments, for example, we discovered incomplete annotations and substructures indicating potential problems in the family definition. In dealing with large groups of terms and/or documents describing the objects under consideration, not only do we determine the similarity between the document pairs, but, by introducing the Choquet integral, we fuse this partial agreement function on pairs of documents into a single value relating the gene products. The measures for the final integral fusion can be tailored to produce order weighted average (OWA) operators (e.g., "at least two documents must support the connection") or can be based on assessments of the "worth" of individual and subsets of documents towards building the strength of connection.