An Efficient Two-Dimensional Least Mean Square (TDLMS) Based on Block Statistics for Small Target Detection

Bae, Tae-Wuk; Kim, Young-Choon; Ahn, Sang-Ho; Sohng, Kyu‐Ik

doi:10.1007/s10762-009-9530-6

Cited by 36 publications

(15 citation statements)

References 10 publications

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“…Comparative analysis is compared with Top-Hat, TDLMS [14], nonparametric background method [15], anisotropic background prediction (ABP) method [16], single Gaussian (SG) [17], fuzzy running average (FRA) [18], and mixed of Gaussian (MoG) [19]. The three indicators MSE, SSIM, and GSNR are used to evaluate the background prediction effect of the infrared images.…”

Section: Background Prediction Results and Analysismentioning

confidence: 99%

“…A 5 × 5 "square" structure is adopted for Top-Hat. The settings for other methods are referenced from the literature [14][15][16][17][18][19]. The experimental results are listed from Tables 2-5.…”

Section: Background Prediction Results and Analysismentioning

confidence: 99%

“…Representative methods are Top-Hat, TDLMS [14], and so on; Top-Hat is a kind of practical nonlinear background estimation method, which tends to be affected by the structural elements, with poor adaptability as a consequence. In order to enhance the adaptability of the algorithm, some scholars put forward adaptable filtering technology, such as two-dimensional minimum mean square error filter (TDLMS), which requires no understanding of [8,9] Able to quickly locate the region of interest Only adapted to scenes with big differences between the target and the background, and more obvious characteristics for the target Sparse representation The proposed method Able to effectively detect scenes with low SNR (SNR < 3 dB) Requiring large amount of computation prior knowledge for the image and has a simple structure but requires statistic characteristics of the background to be constant or slowly changing, dramatically limiting the application scope.…”

Section: Background Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Fan

Zhang

et al. 2017

Mathematical Problems in Engineering

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In order to detect infrared (IR) dim and small targets in a strong clutter background, a method based on local energy center of sequential image is proposed. This paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HOC). Finally, on the basis of image preprocessing, the paper constructs a sequential image energy center detection algorithm that integrates the neighborhood, continuity, area, and energy and other motion characteristics of the target. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HOC significantly increased the signal-noise ratio of images; when the signal-noise ratio (SNR) is lower than 2.5 dB, the proposed method could effectively eliminate noise and detect targets.

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Section: Background Prediction Results and Analysismentioning

confidence: 99%

Section: Background Prediction Results and Analysismentioning

confidence: 99%

Section: Background Estimationmentioning

confidence: 99%

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Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Fan

Zhang

et al. 2017

Mathematical Problems in Engineering

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“…To efficiently examine small moving targets and remove various sorts of background clutters in IR images simultaneously, numerous algorithms have been developed so far, including filter based methods, mathematical morphology based methods, wavelet based methods, and so on. Filter based methods, the representatives of which are max-mean/max-median filter [6], high-pass filter [7] as well as two-dimensional least mean square (TDLMS) filter [8], utilize fixed templates to suppress clutters according to intensity difference. Although they can meet the requirement of real-time processing, the results are always inaccurate [9,10].…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Wan

Ren

et al. 2017

Applied Sciences

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Abstract:In order to detect both bright and dark small moving targets effectively in infrared (IR) video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the salient regions of the original image is obtained by tuning its amplitude spectrum in the frequency domain. Then, a saliency histogram is constructed by means of averaging the accumulated saliency value of each gray level in the map, through which bins corresponding to bright target and dark target are assigned with large values in the histogram. Next, single-frame detection of candidate targets is accomplished by a binarized segmentation using an adaptive threshold, and their centroid coordinates with sub-pixel accuracy are calculated through a connected components labeling method as well as a gray-weighted criterion. Finally, considering the motion characteristics in consecutive frames, an inter-frame false alarm suppression method based on geometrical invariability is developed to improve the precision rate further. Quantitative analyses demonstrate the detecting precision of this proposed approach can be up to 97% and Receiver Operating Characteristic (ROC) curves further verify our method outperforms other state-of-the-arts methods in both detection rate and false alarm rate.

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“…Since the development of IR-guided missiles, various IR small target detection methods, such as the spatial filter-based method [8]- [10], morphology-based method [11], and temporal-based method [12]- [13] In IR images, objects with different IR radiation normally appear in the form of different changes in gray values. To describe the change in gray values on different objects more clearly, the concept of a "complex degree" was presented [14]- [15].…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Extraction Using Weighted Information Entropy and Adaptive Opening Filter

Bae¹,

Kim²,

Kim³

et al. 2015

ETRI J

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In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

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An Efficient Two-Dimensional Least Mean Square (TDLMS) Based on Block Statistics for Small Target Detection

Cited by 36 publications

References 10 publications

Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Infrared Target Extraction Using Weighted Information Entropy and Adaptive Opening Filter

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