&lt;title&gt;Neural-network-based target detection system for FLIR imagery&lt;/title&gt;

Dwan, Chris M.; Der, Sandor Z.

doi:10.1117/12.304655

Cited by 3 publications

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“…detection scheme [13]; Liu et al proposed a target detection algorithm based on nonlinear PCA neural network [12]; Chan et al proposed an algorithm by combining neural-networkbased PCA and a backpropagation learning algorithm [8].…”

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confidence: 99%

Infrared Small Target Detection Using PPCA

Cao

Liu

Yang

2008

Int J Infrared Milli Waves

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Probabilistic PCA (PPCA) is an extension of PCA which reformulated PCA in a probabilistic framework. In this paper we propose a infrared small target detection algorithm using PPCA analogous to the face detection scheme using PCA, or known as "eigenface". By computing the parameters of PPCA, we map the input vector from the image onto a subspace. After reconstructing the vector, the distance between the original vector and the reconstructed one will indicate the possibility of the input being a target. Experimental results show the effectiveness of this algorithm compared with other methods.

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confidence: 99%

Infrared Small Target Detection Using PPCA

Cao

Liu

Yang

2008

Int J Infrared Milli Waves

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“…Neural networks have also been exploited to classify each pixel in the FLIR imagery as either man-made structures or background (clutter) in order to detect targets. For example, see the articles by Dwan et al, (14) and Ramanan et al, (15).…”

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confidence: 99%

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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