&lt;title&gt;Model-based approach to real-time target detection&lt;/title&gt;

Hackett, Jay K.; Gold, Ed V.; Long, Daniel T.; Cloud, Eugene L.; Duvoisin, Herbert A.

doi:10.1117/12.138271

Cited by 2 publications

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“…An edge-based segmentation consists of a scheme which searches for a set of edges that enclose a region [6,7,8]. The enclosed region defines the object and it may be labeled using flashed-fill algorithm or other labeling scheme.…”

Section: Segmentationmentioning

confidence: 99%

<title>Development of automatic target recognition for infrared sensor-based close-range land mine detector</title>

Ngan

Garcia

Cloud³

et al. 1995

SPIE Proceedings

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Infrared (IR) imagery scenes change continuously with environmental conditions. Strategic targets embedded in them are often difficult to be identified with the naked eye. An IR sensor-based mine detector must include Automatic Target Recognition (ATR) to detect and extract land mines from IR scenes. In the course of the ATR development process, mine signature data were collected using a commercial 8-12j spectral range FLIR, model Inframetrics 445L, and a commercial 3-5j staring focal planar array FUR, model Infracam. These sensors were customized to the required field-of-view (FOV) for short range operation. These baseline data were then input into a specialized parallel processor on which the mine detection algorithm is developed and trained. The ATR is feature-based and consists of several sub-processes to progress from raw input JR imagery to a Neural Network (NN) classifier for final nomination of the targets. Initially, image enhancement is used to remove noise and sensor artifact. Three preprocessing techniques namely Model-Based Segmentation, MultiElement Prescreener and Geon Detector are then applied to extract specific features of the targets and to reject all objects that do not resemble mines. Finally, to further reduce the false alarm rate, the extracted features are presented to the Neural Network classifier. Depending on the operational circumstances, one of three NN techniques will be adopted, Back Propagation, Supervised Real-Time Learning or Unsupervised Real-Time Learning. The Close Range JR Mine Detection System is an Army program currently being experimentally developed to be demonstrated in the Army's Advanced Technology Demonstration (ATD) in FY95. The ATR resulting from this program will be integrated into the 21st Century Land Warrior program in which the mine avoidance capability is its primary interest.Key words: automatic target recognition, image processing, mine detector, infrared sensor INTRODUCTIONMine warfare has become increasingly cruel and lethal largely due to modem electronics and explosives technologies. Newly developed smart mines contain fuses that can be intelligently and programmably activated when approached within their lethal proximity. Automatic detection and recognition ofland mines from remotely sensed imagery is of significant interest to ground based infantry. Infrared imaging sensors have been explored as useful tools for reaching these goals, because they offer a standoff distance to the soldiers while locating the threats [1,2]. Our efforts in development of the Automatic Target Recognition (ATR) focused on extracting meaningful features from the JR images and performing a classificationlnomination of targets using neural networks.For this research, mine signature data were obtained using a commercial 8-12j spectral range Forward Looking Infrared (FUR) sensor, model Inframetrics 445L, customized to obtain a field-of-view (FOV) of 28 X 21 required for short range operation. Sinte our ATR is feature-based, one commonly critical issue is that object spatial patte...

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Section: Segmentationmentioning

confidence: 99%

<title>Development of automatic target recognition for infrared sensor-based close-range land mine detector</title>

Ngan

Garcia

Cloud³

et al. 1995

SPIE Proceedings

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“…It is likely that electro-optical imagery will be one of the major sources of data for the vehicle-borne mine detection and clearance systems. Infrared imagery has proved invaluable in detecting the tell-tale thermal signatures of buried land mines [1,2]. Dual-band approaches appear to offer the prospect of more reliable operation [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

Data fusion for vehicle-borne mine detection

McMichael¹

1996

EUREL International Conference. The Detection of Abandoned Land Mines: A Humanitarian Imperative Seeking a Technical Solution

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<title>Model-based approach to real-time target detection</title>

Cited by 2 publications

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<title>Development of automatic target recognition for infrared sensor-based close-range land mine detector</title>

<title>Development of automatic target recognition for infrared sensor-based close-range land mine detector</title>

Data fusion for vehicle-borne mine detection

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