FAST: parallel airplane pattern recognition

A new feature selection approach is presented for using parallel distributed processing to identify a three-dimensional object from a two-dimensional image recorded at an arbitrary viewing angle and range. One vector of 32 feature variables is used to describe a two-dimensional binary image. The feature variables are based on counts of nearest neighbor conjuncts, which reflect shape and area differences among airplanes. Thirteen standardized airplanes are used in the experiment in order to compare the results with established feature selection approaches. Results based on the new approach compare favorably with results from traditional approaches. In addition, a relatively fast compact parallel hardware design and data structure are presented and compared with traditional algorithms.<>