Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans

Klęsk, Przemysław; Kapruziak, Mariusz; Olech, B.

doi:10.1007/s10044-016-0592-5

Cited by 9 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Refraction and diffraction from materials occur, when there is a difference in the dielectric properties. After that, the receiver antenna receives the reflected signal, and then this signal can be processed to determine what it identified depending on the results of the processing method [7].…”

Section: Gpr Techniquementioning

confidence: 99%

See 1 more Smart Citation

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

El-Ghamry¹,

El‐Shafai²,

Abdalla³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and without landmines. Moreover, a neural classifier is used to classify images with cumulative histograms as feature vectors. The second algorithm is based on scale-space analysis with the number of speeded-up robust feature (SURF) points as the key parameter for classification. In addition, this paper presents a framework for size reduction of GPR images based on decimation for efficient storage. The further classification steps can be performed on images after interpolation. The sensitivity of classification accuracy to the interpolation process is studied in detail.

show abstract

Section: Gpr Techniquementioning

confidence: 99%

“…This technique achieved up to 95% detection accuracy. Klesk et al [7] presented landmine detection approach from 3-D scans taken at different levels underground using higher-order statistics. This approach firstly generates integral images, and hence higher-order moments are extracted from these integral images in a constant time.…”

Section: Introductionmentioning

confidence: 99%

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

El-Ghamry¹,

El‐Shafai²,

Abdalla³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Computing a statistical moment µ N of an image f on a VOI Γ p corresponds to computing the integral [19,20]. Since the images are discrete, the moment is approximated by its discrete version (with a slight abuse of notation, we call it µ N as well)…”

Section: Statistical Moments Of An Image Inside a Voimentioning

confidence: 99%

“…. , N, and w the basis function defined in Equation (20). If the interpolation point x ∈ (x k , x k+1 ) with a ≤ k ≤ N − a + 1 and m w > 0, then…”

Section: Error Estimates For the Univariate Interpolationmentioning

confidence: 99%

Oversampling Errors in Multimodal Medical Imaging Are Due to the Gibbs Effect

et al. 2021

View full text Add to dashboard Cite

To analyze multimodal three-dimensional medical images, interpolation is required for resampling which—unavoidably—introduces an interpolation error. In this work we describe the interpolation method used for imaging and neuroimaging and we characterize the Gibbs effect occurring when using such methods. In the experimental section we consider three segmented three-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The experimental results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.

show abstract

“…Computing a statistical moment µN of an image f on a VOI Γp corresponds to computing the integral Hu (1962); Klęsk et al (2017). Since the images are discrete, the moment is approximated by its discrete version (with a slight abuse of notation, we call it µN as well)…”

Section: Statistical Moments Of An Image Inside a Voimentioning

confidence: 99%

Oversampling errors in multimodal medical imaging are due to the Gibbs effect

Poggiali,

Cecchin,

Campi

et al. 2021

Preprint

View full text Add to dashboard Cite

To analyse multimodal 3-dimensional medical images, interpolation is required for resampling which -unavoidably -introduces an interpolation error. In this work we consider three segmented 3-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.

show abstract

Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans

Cited by 9 publications

References 20 publications

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

Oversampling Errors in Multimodal Medical Imaging Are Due to the Gibbs Effect

Oversampling errors in multimodal medical imaging are due to the Gibbs effect

Contact Info

Product

Resources

About