A single-pixel target detection and tracking system

Wang, G.; Inigo, Rafael M.; McVey, Eugene S.

doi:10.1109/icpr.1990.118072

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…1. p; =po(1_(1_p;1)w1w) 1<i N (5) The backward filter after the forward filter is to deduce the possible target pixel by f and filter out more noise pixels.…”

Section: ) Definition Of Operationsmentioning

confidence: 99%

<title>Detection of moving pixel-sized target based on quasi-continuity-filter</title>

et al. 1996

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A method based on quasi-continuity-filter for the detection of moving pixel-sized target under low SNR is presented in this paper. First each frame of the input sequence is binarized based on Maximum Error Probability(MEP) rule. And then the quasi-continuity-filter is designed to use the continuity property of the target pixels in the adjacent frames and randomness ofnoise pixels to filter out the noise.

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“…1. p; =po(1_(1_p;1)w1w) 1<i N (5) The backward filter after the forward filter is to deduce the possible target pixel by f and filter out more noise pixels.…”

Section: ) Definition Of Operationsmentioning

confidence: 99%

<title>Detection of moving pixel-sized target based on quasi-continuity-filter</title>

et al. 1996

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“…(5) If we detect targets in a single frame of image data by thresholding, then the detection probability and the false alarm probability are given by Pd = 4'((A -11)/cTN) = (SNRj -j') (6) Pfa = (-17/oN) = (i), (7) respectively, where i is a threshold, r' i/o is normalized one, and e_2/2dy (8) is the Gaussian distribution function. The false alarm probability of (7) is for a single pixel.…”

Section: Noise Modelmentioning

confidence: 99%

“…Wang et. al [6] proposed a pipeline method which uses not the above assumption but the frame-to-frame correlation of the targets and can detect non-constant velocity targets: however, since it is essentially a processing of only two frames of image data, the performance is restricted. This paper presents a new method for detection of small moving targets from noisy image sequences using a recursive filter, which contains a local maximum filter in the feedback loop.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, there has been a growing interest in detection of small low-contrast objects from optical or infrared (IR) image sequences [1][2][3][4][5][6][7][8][9][10][11][12]. The problem of detecting small low-contrast objects arises, for example, when we detect small moving targets from an on-board JR sensor [6,12], and when we detect meteors, asteroids and satellites in the night sky from an array of charge coupled device (CCD) sensors on the focal plane of a telescope [1,8,11].…”

Section: Introductionmentioning

confidence: 99%

“…The problem of detecting small low-contrast objects arises, for example, when we detect small moving targets from an on-board JR sensor [6,12], and when we detect meteors, asteroids and satellites in the night sky from an array of charge coupled device (CCD) sensors on the focal plane of a telescope [1,8,11]. …”

Section: Introductionmentioning

confidence: 99%

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<title>Small target detection from image sequences using recursive max filter</title>

1995

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This paper presents a new method for detection of small moving targets from noisy image sequences using a recursive filter, which contains a local maximum filter in the feedback loop. Since all the procedures contained in this method can be implemented by fully parallel algorithm, this method can be realized by a neural network and real-time processing is possible for image sequences at high frame rates. First we investigate the performance of the method on the assumption that the targets move randomly in an image plane and the background noise is white both in time and space. The results show that the proposed method has the ability to suppress the background noise and enhances small moving targets, and that the targets are detected only by thresholding after the processing of the recursive filter. There can be other backgrounds such as clutters and stationary objects. It is also shown that the above method is applicable after preprocessing of conventional time difference method, and is able to suppress these backgrounds. Next we analyze the performance of the method using the property of order statistics. The performance measure is the output signal-to-noise ratio (SNR) and is represented analytically as a function of input SNR and the parameters included in the method. Finally, the recursive filter is optimized using the result of the analysis.

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Image analysis and computer vision: 1990

Rosenfeld

1991

CVGIP: Image Understanding

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A single-pixel target detection and tracking system

Cited by 19 publications

References 6 publications

<title>Detection of moving pixel-sized target based on quasi-continuity-filter</title>

<title>Detection of moving pixel-sized target based on quasi-continuity-filter</title>

<title>Small target detection from image sequences using recursive max filter</title>

Image analysis and computer vision: 1990

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