&lt;title&gt;Sequential digital filters for fast detection of targets in FLIR image data&lt;/title&gt;

Pham, Quoc Henry; Brosnan, Timothy Myers; Smith, M.J.T.

doi:10.1117/12.277130

Cited by 14 publications

(11 citation statements)

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“…Currently, most researchers report the performance of algorithms using the experimental result on some test data set, 6,8,16,22,[32][33][34] in terms of some performance measures such as detection rate, false alarm rate, and correct classification rate, or some variations of these. Although the notion of these objective and quantitative measures are widely used, their exact definitions and the ways their values are computed, have not been standardized.…”

Section: Performance Characterization In Atrmentioning

confidence: 99%

“…The joint relaxation penalizes inconsistency and ambiguity in the segmentation process. Pham et al 6 use grayscale morphological operations to enhance the targets to get better thresholding results. Ernisse et al 7 use the hit/miss filter for region of interest ͑ROI͒ identification and the difference of Gaussian filter for segmentation and feature extraction.…”

Section: Traditional Algorithmsmentioning

confidence: 99%

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FLIR ATR using location uncertainty

Liu

Haralick

2000

J. Electron. Imaging

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A model-based FLIR ATR algorithm is described. It utilizes boundary contrast for target detection and recognition. Boundary contrast is related to the location uncertainty at target boundary points. A polygon model is used for deriving target centroid location uncertainty caused by the boundary point location uncertainty. The significance of the work lies in the sound mathematical models used in deriving the relationship between contrast and location uncertainty for the boundary points and the relationship between boundary point location uncertainty and centroid location uncertainty. Experiment results show significantly improved performance in detection, recognition, and localization.

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Section: Performance Characterization In Atrmentioning

confidence: 99%

Section: Traditional Algorithmsmentioning

confidence: 99%

FLIR ATR using location uncertainty

Liu

Haralick

2000

J. Electron. Imaging

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“…6 Morphological methods exploit the assumption of a small target by removing any blobs smaller than a given size. 4,5 An overview of background estimation techniques for target detection, including sparse-window and histogram techniques, is given by Moore and Lowery. 7 Noise in the imagery may cause many false detections to be made by the abovementioned techniques.…”

Section: Introductionmentioning

confidence: 99%

<title>Background modeling and target segmentation via modified Kalman filtering</title>

Searle

2004

SPIE Proceedings

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In target detection and tracking applications with imagery data taken from a moving camera platform, it is necessary to segment potential targets in each image frame. This is typically done by preprocessing individual images to exploit some known attribute about the data. Often these methods make many false detections, particularly in the presence of additive noise, and the results thus require significant post-processing. A means of estimating the background in the imagery sequence under the formalism of the Kalman filter is suggested. This background estimate is then used to recast the segmentation problem as one of outlier detection, and the result of segmentation is used to modify the filter update. Ways of making the technique computationally benign are discussed. The technique is used to analyse a simulated image sequence, and the performance is compared to that of a single-frame background-estimation technique. The feasibility of target segmentation via background tracking is thus demonstrated.

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“…In addition, the false-positive rate is larger than that of the proposed method. Finally, we compared the proposed method with morphology processing, 4 which has been used in various ATR systems for many years. As shown in Fig.…”

Section: -5 Optical Engineeringmentioning

confidence: 99%

“…Many researchers have developed the ATD of FLIR images through approaches such as wavelet transforms, 2 Gabor filters, 3 morphology processing, 4 and a fusion of several algorithms. 5 In general, principal-component analysis ͑PCA͒ and multilayer perceptrons ͑MLPs͒ are used in the developed ATD methods.…”

Section: Introductionmentioning

confidence: 99%

Automatic target detection using binary template matching

2005

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Abstract. This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

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<title>Sequential digital filters for fast detection of targets in FLIR image data</title>

Cited by 14 publications

References 0 publications

FLIR ATR using location uncertainty

FLIR ATR using location uncertainty

<title>Background modeling and target segmentation via modified Kalman filtering</title>

Automatic target detection using binary template matching

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