Improved power-law-detector-based moving small dim target detection in infrared images

Wu, Bin; Ji, Hongbing

doi:10.1117/1.2829771

Cited by 12 publications

(12 citation statements)

References 8 publications

(9 reference statements)

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“…Since each frame in the IR image sequence is consisted of the dim moving IR target, the Gauss noise and the cloud cluster embedded in the background meets the principle of superposition in ideal circumstances, then the frame can be modeled as [7] ( ) …”

Section: Model Of Ir Image Containing the Dim Moving Targetmentioning

confidence: 99%

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Dynamic programming algorithm for detecting dim infrared moving targets

Mao

Xie

2009

SPIE Proceedings

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Infrared (IR) target detection is a key part of airborne infrared weapon system, especially the detection of poor dim moving IR target embedded in complex context. This paper presents an improved Dynamic Programming (DP) algorithm in allusion to low Signal to Noise Ratio (SNR) infrared dim moving targets under cluttered context. The algorithm brings the dim target to prominence by accumulating the energy of pixels in the image sequence, after suppressing the background noise with a mathematical morphology preprocessor. As considering the continuity and stabilization of target's energy and forward direction, this algorithm has well solved the energy scattering problem that exists in the original DP algorithm. An effective energy segmentation threshold is given by a Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter with a regional peak extraction algorithm. Simulation results show that the improved DP tracking algorithm performs well in detecting poor dim targets.

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Section: Model Of Ir Image Containing the Dim Moving Targetmentioning

confidence: 99%

“…There are several potential trajectories determined by energy segmentation threshold based on formula (7). Because the correlation of noise trajectories are poor compared with dim target trajectories, candidate dim target trajectories can be integrated by linked list further.…”

Section: Energy Segmentation Thresholdmentioning

confidence: 99%

Dynamic programming algorithm for detecting dim infrared moving targets

Mao

Xie

2009

SPIE Proceedings

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“…4 Recently, an improved power-law-detector based moving target detection method has been proposed and it is effective for image sequences that in heavy clutter. 22 False alarms caused by sun-glint can be reduced by three plot correlation with temporal filtering. 23 It is also possible to reduce false detection by decision method such as constant false alarm rate (CFAR), which is useful in a severely cluttered background.…”

Section: Introductionmentioning

confidence: 99%

Target attribute-based false alarm rejection in small infrared target detection

Kim

2012

SPIE Proceedings

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Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.

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“…15 Wu and Ji presented an algorithm based on the improved power law detector for the detection of small dim moving targets in IR image sequences. 16 Deng et al proposed a method based on empirical mode decomposition (EMD), in order to detect small targets under complicated sea-sky backgrounds in infrared images. 17 …”

Section: Introductionmentioning

confidence: 99%

Extracting targets from regions-of-interest in infrared images using a 2-D histogram

et al. 2011

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We propose an effective method of extracting targets from a region-of-interest (ROI) in infrared images by using a 2-D histogram, considering intensity values and distance values from a center of the ROI. Existing approaches for extracting targets have utilized only intensity values of pixels or an analysis of a 1-D histogram of intensity values. Because the 1-D histogram has mixed bins containing false-negative bins from the target region as well as false-positive bins from the background region, it is difficult to extract target regions effectively due to the mixed bins. In order to solve the problem of the mixed bins, we propose a novel 2-D histogrambased approach for extracting targets. Experimental results have shown that the proposed method achieves better performance of extracting targets than existing methods under various environments, such as target regions with irregular intensities, dim targets, and cluttered backgrounds. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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Improved power-law-detector-based moving small dim target detection in infrared images

Cited by 12 publications

References 8 publications

Dynamic programming algorithm for detecting dim infrared moving targets

Dynamic programming algorithm for detecting dim infrared moving targets

Target attribute-based false alarm rejection in small infrared target detection

Extracting targets from regions-of-interest in infrared images using a 2-D histogram

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