Scale-Insensitive Detection Algorithm for FLIR Imagery

Der, Sandor Z.; Dwan, Chris M.; Chan, Alex; Kwon, Hessung; Nasrabadi, Nasser M.

doi:10.21236/ada392597

Cited by 10 publications

(17 citation statements)

References 3 publications

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“…A detection algorithm described elsewhere [9,10] was applied to the test imagery. The recognition algorithm takes as input the original image and the detection file produced by the detector.…”

Section: Resultsmentioning

confidence: 99%

Automatic Target Recognizer for the Demo III Program

Der¹

2002

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This report describes an algorithm for the recognition of military vehicles in Forward Looking Infrared (FLIR) imagery. The input is a FUR image, and the output of a detector or clutter rejector listing a number of locations in the image for the recognizer to examine. The output is the same list with the decision of the recognizer appended to each location in the list. The algorithm is based on principal component analysis.

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Section: Resultsmentioning

confidence: 99%

Automatic Target Recognizer for the Demo III Program

Der¹

2002

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“…A detection algorithm described elsewhere [12,13] was applied to the test imagery. The recognition algorithm takes as input the original image and the detection file produced by the detector.…”

Section: Resultsmentioning

confidence: 99%

Automatic Target Acquisition of the DEMO III Program

Der¹,

Chan²,

Stolovy³

et al. 2002

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“…The proposed algorithm is also compared with another target detection algorithm, called spatial anomaly detection algorithm (SADA) which was described in [19], for the database yuma9207. The ROC curves are shown in figures 10a-b.…”

Section: Discussionmentioning

confidence: 99%

“…This global-based approach neglects the local variations between the signatures of the target and its surrounding medium. Some researchers have used double-gated filters that exploit these local variations for target detection: see Gregoris et al, (18) and Der et al, (19). In these methods, the authors identify an inner target window surrounded by an outer window.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive target detection in FLIR imagery using the eigenspace separation transform and principal component analysis

et al. 2003

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Sensors and Electron Devices Directorate, ARLApproved for public release; distribution unlimited.ii REPORT DOCUMENTATION PAGE Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)June 2005 ARL-TR-3544 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) U.S. Army Research Laboratory 2800 Powder Mill Road Adelphi, MD 20783-1197 SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited. SUPPLEMENTARY NOTES ABSTRACTIn this paper, an adaptive target detection algorithm for forward-looking infrared (FLIR) imagery is proposed which is based on measuring differences between structural information within a target and its surrounding background. At each pixel in the image a dual window is opened where the inner window (inner image vector) represents a possible target signature and the outer window (consisting of a number of outer image vectors) represents the surrounding scene. These image vectors are then preprocessed by two directional highpass filters to obtain the corresponding image gradient vectors. The target detection problem is formulated as a statistical hypotheses testing problem by mapping these image gradient vectors into two linear transformations, P 1 and P 2 and, via principal component analysis and eigenspace separation transform, respectively. The first transformation P 1 is only a function of the inner image gradient vector. The second transformation P 2 is a function of both the inner and outer image gradient vectors. For the hypothesis H 1 (target): the difference of the two functions is small. For the hypothesis H 0 (clutter): the difference of the two functions is large. Results of testing the proposed target detection algorithm on two large FLIR image databases are presented. SUBJECT TERMStarget detection, eigenvector analysis, eigenspace separation transform, principal component analysis, FLIR imagery, statistical hypotheses testing SECURITY

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Scale-Insensitive Detection Algorithm for FLIR Imagery

Cited by 10 publications

References 3 publications

Automatic Target Recognizer for the Demo III Program

Automatic Target Recognizer for the Demo III Program

Automatic Target Acquisition of the DEMO III Program

Adaptive target detection in FLIR imagery using the eigenspace separation transform and principal component analysis

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