Summary form only given. The application of both conventional and neural network methods to actual sensor data for target recognition is addressed. The problem of interest is the detection and classification of object...
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Summary form only given. The application of both conventional and neural network methods to actual sensor data for target recognition is addressed. The problem of interest is the detection and classification of objects in multisensor images. The types of images used are forward looking infrared (FLIR), absolute and relative range, and doppler. First, conventional approaches to target recognition used at the Air Force Institute of technology (AFIT) are reviewed. Next, the application of neural networks to target recognition at AFIT is examined. Two classification tests are reported. In the first, the task is to label an object either target or nontarget. The neural network approach achieved 89% accuracy on the training data and was 75% correct on the test data. In the second test, the network learns to classify objects as either tank, truck, or armoured personnel carrier. An accuracy of 98% on the training data was recorded and 85% on the test set. Improvements on the standard backpropagation training rule are also reported. A method which varies the step size in gradient descent is examined. A second-order method is also examined and compared to the standard backpropagation algorithm.< >
Merging information available from multisensor views of a scene is a useful approach to targetdetection and classification. Development of multisensor information fusion techniques using a data base of real imagery f...
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The United States Navy's worldwide operations on land, at sea, and in the air require it to be capable of performing a wide variety of missions. This paper will attempt to define how Automatic target Recognition (...
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In this paper a review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as targetdetection,...
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In this paper a review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as targetdetection, segmentation, feature computation, classification, etc. are evaluated and several new quantitative criteria are presented. Evaluation approaches are discussed and various problems encountered in the evaluation of algorithms are addressed. Strategies used in the data base design are outlined. New techniques such as the use of contextual cues, semantic and structural information, hierarchical reasoning in the classification and incorporation of multisensors in ATR systems are also presented.
Through IR&D projects and contracts with the Naval Weapons Center, the Image technology Laboratory has developed a software system for automatic targetclassification of ship imagery. The system includes modules f...
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Through IR&D projects and contracts with the Naval Weapons Center, the Image technology Laboratory has developed a software system for automatic targetclassification of ship imagery. The system includes modules for targetdetection, segmentation, feature extraction, classification, and report/display products. Developed from a large infrared image database, the system can receive and process an image (or images) and produce classification results and associated confidence levels along with other optional outputs (e. g. , total classification results and image display products). This article documents the development of the Advanced Anti-Ship targeting Development (AATD) software and summarizes its current capabilities and the results achieved to date.
24 PAPERS ARE CONTAINED IN THIS VOLUME UNDER THE FOLLOWING HEADINGS: MODELING;targetdetection;targetclassification;target TRACKING AND HANDOFF. TECHNICAL AND PROFESSIONAL PAPERS FROM THIS CONFERENCE ARE INDEXED WITH...
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24 PAPERS ARE CONTAINED IN THIS VOLUME UNDER THE FOLLOWING HEADINGS: MODELING;targetdetection;targetclassification;target TRACKING AND HANDOFF. TECHNICAL AND PROFESSIONAL PAPERS FROM THIS CONFERENCE ARE INDEXED WITH THE CONFERENCE CODE NO. 01637 IN THE EI ENGINEERING MEETINGS (TM) DATABASE PRODUCED BY ENGINEERING INFORMATION, INC.
24 PAPERS ARE CONTAINED IN THIS VOLUME UNDER THE FOLLOWING HEADINGS: MODELING; targetdetection; targetclassification;target TRACKING AND HANDOFF. TECHNICAL AND PROFESSIONAL PAPERS FROM THIS CONFERENCE ARE INDEXED WI...
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24 PAPERS ARE CONTAINED IN THIS VOLUME UNDER THE FOLLOWING HEADINGS: MODELING; targetdetection; targetclassification;target TRACKING AND HANDOFF. TECHNICAL AND PROFESSIONAL PAPERS FROM THIS CONFERENCE ARE INDEXED WITH THE CONFERENCE CODE NO. 01637 IN THE EI ENGINEERING MEETINGS (TM) DATABASE PRODUCED BY ENGINEERING INFORMATION, INC.
The proceedings contain 24 papers. The topics discussed include: infraredtarget array development;model for generating synthetic three-dimensional (3D) images of small vehicles;optical communications and laser beam a...
The proceedings contain 24 papers. The topics discussed include: infraredtarget array development;model for generating synthetic three-dimensional (3D) images of small vehicles;optical communications and laser beam acquisition performances;comparison of imaging infrareddetection algorithms;target acquisition and extraction from cluttered backgrounds;image analysis using polarized Hough transform and edge enhancer;intensity correlation techniques for passive optical device detection;eliminating nearest neighbor searches in estimating target orientation;designing for stray radiation rejection;optimal performance limits for detection and classification algorithms;feature analysis for forward looking infrared (FLIR) target identification;automatic ship recognition using a passive radiometric sensor;and infrared ship classification using a new moment pattern recognition concept.
The problem of determining the position of an object in a given field of view and extracting it for subsequent shape analysis is basic to many image processing applications. One such important defense application is t...
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The problem of determining the position of an object in a given field of view and extracting it for subsequent shape analysis is basic to many image processing applications. One such important defense application is to locate airborne targets within terrain or sky back-grounds.
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