Browsing by Author "Keller, James M."

Now showing 1 - 3 of 3

Celebrating 25 Years of the IEEE Transactions on Fuzzy Systems
(2018-02-01)
Bezdek, James C.
;
Keller, James M.
;
Pal, Nikhil R.
;
Lin, Chin-Teng
;
Garibaldi, Jonathan (Jon)
Frequency subband processing and feature analysis of forward-looking ground-penetrating radar signals for land-mine detection
(2007-03-01)
Wang, Tsaipei
;
Keller, James M.
;
Gader, Paul D.
;
Sjahputera, Ozy
;
資訊工程學系
There has been significant amount of study on the use of ground-penetrating radar (GPR) for land-mine detection. This paper presents our analysis of GPR data collected at a U.S. Army test site using a new approach based on frequency subband processing. In this approach, from the radar data that have over 2.5 GHz of bandwidth, we compute separate radar images using the one wide (2 GHz) and four narrow (0.6 GHz) frequency subbands. The results indicate that signals for different frequency subbands; are significantly different and give very different performance in land-mine detection. In addition, we also examine a number of features extracted from the GPR data, including magnitude and local-contrast features, ratio between copolarization and cross-polarization signals, and features obtained using polarimetric decomposition. Feature selection procedures are employed to find subsets of features that improve detection performance when combined. Results of land-mine detection, including performance on blind test lanes, are presented.
Possibilistic C-shell clustering with inter-cluster constraints
(2006)
Tsaipei Wang
;
Keller, James M.
;
資訊工程學系
;
Department of Computer Science
This paper describes our analysis of using extra constraint terms regarding relations between cluster prototypes in possibilistic c-shell clustering. The extra constraints are implemented as additional terms in the cost function. This allows users of these algorithms to incorporate additional knowledge regarding properties cluster prototype into the clustering process. Our analysis here focuses on the use of one extra term for locating circles (shell clustering with circular prototypes) with similar radii. An adjustable factor is used to control the strength of this constraint. For possibilistic clustering, this couples the update procedure of the otherwise independent prototypes. Our experiments, using both simulation and real image data, indicate that this is especially useful in locating actual clusters when the available data are noisy.