ARTIFICIAL INTELLIGENCE APPROACHES TO SEGMENTATION AND CONTOUR EXTRACTION FOR AIR AND GROUND DEFENSE APPLICATIONS

TO DETECT AND IDENTIFY AIRPLANS AND GROUND TACTICL TARGETS WITH IMAGE DATA, TOW COMPLEMENTARY ANAYSES ARE REQUIRED (1) CONTOUR EXTRACTION, AND (2) SHAPE/TARGET RECOGNITION. UNTIL VERY RECENTLY, RESEARCH EMPHASIS HAS BEEN PLACED ON SHPE RECOGNITION BASED ON SEGMENTATIONS FROM ODELS AND/OR SIMPLE SCENES. IT APPEARS THAT RESEARCH AND DEVELOPMENT EFFORTS ON SEGMENTATION WITH COMPLEX SCENES HAVE BEEN NEGLECTED. CONSEQUENTLY, THE TOTAL TARGET RECOGNITION SYSTEM SUFFERS FROM A LACK OF SOPHISTICATED CONTOUR EXTRACTION ALGORITHMS IN VIEW OF THE FAWCT THAT THE EFFECTIVENESS OF THE SHAPE RECOGNIER AND TARGET CLASSIFIER IS HIGHLY DEPENDENT ON THE QUALITY OF THE INPUT DATA--THE CONTOURS. THEREFORE, THIS PROPOSED EFFORT IS INTENDED TO FILL THIS RESEARCH GP BY PROPOSING AND TESTING A FAMILY OF AL-BASED ALGORITHMS FOR SEGMENTATION AND CONTOUR EXTRACTION. ACCORDINGLY, TWO SPECIFIC TASKS ARE PROPOSED (1) TO TEST AND FINE-TUNE THE PROPOSED ALGORITHMS USING REAL-WORLD IMAGE DATA; AND (2) TO BEGIN THE PROCESS OF A SUB-SET OF SR&E'S AL-BASED ALGORITHMS IN THE C OR FORTRAN PROGRAMMING LANGUAGE AS SPECIALIZED PROCESSORS FOR AIRPLANE CONTOUR EXTRACTION AND GROUND TARGET IDENTIFICATION. THE SIGNIFICANCE OF THE EFFORT CAN BE ASSESSED FROM THE FACT THAT SUCCESSFUL SEGMENTATION WILL LEAD TO BETTER PERFORMANCE IN TARGET RECOGNITION FOR AIR AND GROUND DEFENSE APPLICATIONS.