Analysis of selection of structural element in mathematical morphology with application to infrared point target detection

Guo, Jiefeng; Chen, Guilin

doi:10.1117/12.754627

Cited by 11 publications

(6 citation statements)

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“…The estimation of removed background is implemented by the proposed composite kernel regression adopting Gaussian kernel function. Another four detection algorithms are implemented to make a comparison with the proposed algorithm, i.e., morphological top-hat filter [2], Butterworth high-pass filter (BHPF) [4], and directional filters based on LS-SVM [1]. The common kernel regression only utilizing spatial kernel (SKR) in [8] is also performed and compared with the proposed multiplicatively-composite kernel method (CKR).…”

Section: Resultsmentioning

confidence: 99%

“…that is solved by the kernel regression model in (2), and then the residual image is generated according to (10).…”

Section: Infrared Small Target Detectionmentioning

confidence: 99%

“…Therefore, most algorithms for small target detection in infrared imagery, in general, deal with two steps, i.e., background suppression or clutter removal, and detection. Usual background suppression methods are mainly categorized into the following: 1) filtering methods, such as template filter [1], morphological filter [2], [3], high pass filter [4], wavelet based filter [5], etc; and 2) statistical learning methods [6][7][8][9], such as parametric learning [6], facet model fitting [7], nonparametric regression [8], etc. The filter based detection methods basically regard small target as an anomaly point in image scenes and separate target from clutter background, which rely on the differences between background and target in properties.…”

Section: Introductionmentioning

confidence: 99%

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A Composite Kernel Regression Method Integrating Spatial and Gray Information for Infrared Small Target Detection

Wang

Liu

et al. 2010

2010 First International Conference on Pervasive Computing, Signal Processing and Applications

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Small target detection in infrared imagery with complex background is always an important task in infrared target tracking system. Complex clutter background usually results in serious false alarm because of low contrast of infrared imagery. In this paper, a composite kernel regression method is proposed for infrared small target detection. In the proposed method, a nonlinear regression model is firstly built based on a multiplicatively-composite kernel which integrates both spatial and gray information surrounding interesting pixels. Then the composite kernel regression is utilized to estimate the clutter background of image. At last, twoparameter CFAR detection is performed on backgroundremoved infrared image to extract the target. Experimental results prove that the proposed algorithm is effective and adaptable to small target detection with complex background.

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Section: Resultsmentioning

confidence: 99%

“…that is solved by the kernel regression model in (2), and then the residual image is generated according to (10).…”

Section: Infrared Small Target Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Composite Kernel Regression Method Integrating Spatial and Gray Information for Infrared Small Target Detection

Wang

Liu

et al. 2010

2010 First International Conference on Pervasive Computing, Signal Processing and Applications

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“…In general, a high radiant emittance (sending side) corresponds to a high spectral irradiance (receiving side) [31,32]. According to the formula of spectral radiant power [33]: P λ = EA 0 τ 0 , where P λ is the spectral radiation power received by the detector, E is the spectral irradiance, A 0 is the effective area of the optical system, and τ 0 is the transmittance of the optical system. Therefore, when considering the same target and optical system, a higher target temperature leads to increased spectral radiant power and stronger signals captured by the detector, resulting in improved imaging results.…”

Section: Characterization and Imagingmentioning

confidence: 99%

Large-aperture imaging system based on 100 mm all-Si metalens in long-wave infrared

Li,

Duan,

Jia

et al. 2024

J. Opt.

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Metalens, which composed of micro-nano structures at sub-wavelength scale, possessing the ability to achieve the functions of traditional lens elements, paving the way for the development of lightweight and integrated imaging systems. Nowadays, the majority of research efforts in the field of metalens have been concentrated on the exploitation of short optical wavelength band with tiny apertures. Here, we have directed our focus towards the long-wave infrared band, which holds significant importance for crucial applications like infrared imaging and detection. We have prepared a metalens with a remarkable aperture of 100 mm and a high numerical aperture of 0.89 at an operating wavelength of 9.35 μm, which is currently the largest reported aperture in long-wave infrared with high light harvesting capability. And the imaging performance was verified by demonstrating the imaging results of the metalens on targets with different temperatures through thermal imaging tests.

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“…In order to improve the performance, lots of modified and variant algorithms derived from mathematical morphology such as the new top-hat transform [19], toggle contrast operator [20], multiscale center-surround top-hat transform [21], hit-or-miss transform [22], and adaptive morphological top-hat transform [6] are further put forward. Despite these methods being able to effectively ameliorate the quality for detection, the shortage of this technique is that the results are too sensitive to the given structural element that should be consistent with the shape and size of the target [23].…”

Section: Introductionmentioning

confidence: 97%

Robust method for infrared small-target detection based on Boolean map visual theory

Ming

et al. 2014

Appl. Opt.

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In this paper, we present an infrared small target detection method based on Boolean map visual theory. The scheme is inspired by the phenomenon that small targets can often attract human attention due to two characteristics: brightness and Gaussian-like shape in the local context area. Motivated by this observation, we perform the task under a visual attention framework with Boolean map theory, which reveals that an observer's visual awareness corresponds to one Boolean map via a selected feature at any given instant. Formally, the infrared image is separated into two feature channels, including a color channel with the original gray intensity map and an orientation channel with the orientation texture maps produced by a designed second order directional derivative filter. For each feature map, Boolean maps delineating targets are computed from hierarchical segmentations. Small targets are then extracted from the target enhanced map, which is obtained by fusing the weighted Boolean maps of the two channels. In experiments, a set of real infrared images covering typical backgrounds with sky, sea, and ground clutters are tested to verify the effectiveness of our method. The results demonstrate that it outperforms the state-of-the-art methods with good performance.

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Analysis of selection of structural element in mathematical morphology with application to infrared point target detection

Cited by 11 publications

References 0 publications

A Composite Kernel Regression Method Integrating Spatial and Gray Information for Infrared Small Target Detection

A Composite Kernel Regression Method Integrating Spatial and Gray Information for Infrared Small Target Detection

Large-aperture imaging system based on 100 mm all-Si metalens in long-wave infrared

Robust method for infrared small-target detection based on Boolean map visual theory

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