Bidimensional empirical mode decomposition method for image processing in sensing system

Qin, Yao; Qiao, Lihong; Wang, Qifu; Ren, Xiaozhen; Zhu, Chunhua

doi:10.1016/j.compeleceng.2018.03.033

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…Lu et al [21] proposed to improve the long execution time of the BEMD procedure and applied it to the agricultural fruit defect detection system. Qin et al [22] applied BEMD to ground-penetrating radar images to suppress the noise from the BEMD decompositions. Nunes et al [23] used BEMD to extract features at spatial frequencies to detect regional maxima and radial basis functions for surface interpolation.…”

Section: Literature Reviewsmentioning

confidence: 99%

Incorporating Visual Defect Identification and Determination of Occurrence Side in Touch Panel Quality Inspection

2022

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This study explores a visual inspection system incorporating defect detection and the judgment of the defect occurrence side in touch panel fabrication. The surface of a touch panel is transparent glass with conductive lines inside to achieve the purpose of touch control. When touch panels are manufactured, it is the front sides of the products that are primarily processed and are treated with stricter standards. Therefore, it is important to correctly distinguish the defect locations occurring on the front or back side to ensure the quality of the touch panels. We first apply the Hilbert-Huang transform to enhance the contrast between the defects and the background. Second, statistical interval estimation is used to segment background and defects to achieve defect detection. Third, the detected defects on both sides of the substrates are combined and numbered for feature extraction. Fourth, the random forest model is applied to judge where the defects occur on the front or back of the transparent substrates. Experimental results show that the defect detection rate achieves 85.75%, and the false alarm rate is lower to 0.33%. The correct location judgment rate of the defect occurrence is 98.62% on the front and back sides of the touch panels.INDEX TERMS Visual inspection, defect identification, occurrence side judgment, touch panels, Hilbert-Huang transform, random forest model.

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Section: Literature Reviewsmentioning

confidence: 99%

Incorporating Visual Defect Identification and Determination of Occurrence Side in Touch Panel Quality Inspection

2022

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“…Inspired by the merits of BEMD, this work applies it to SAR image feature extraction in order to enhance SAR ATR performance. We set the stop criterion SD to be 0.24 according to the experiential guidance and results from repetitive tests [47]- [49]. The triangle-based cubic spline interpolation is used for the 2D interpolation in Step 2 and the boundary extension is employed to relieve the boundary effect [48].…”

Section: Basic Theory Of Bemdmentioning

confidence: 99%

Bidimensional Empirical Mode Decomposition for SAR Image Feature Extraction With Application to Target Recognition

Chang

You

Cao³

2019

IEEE Access

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This study introduces a target recognition algorithm for synthetic aperture radar (SAR) images based on the features extracted by bidimensional empirical mode decomposition (BEMD). BEMD provides an adaptive and empirical way to process signals, which generates bidimensional intrinsic mode functions (BIMFs) to describe the details of SAR images. Therefore, the generated BIMFs are complementary to the original image and their joint use could probably improve the recognition performance. In order to fully exploit the discrimination of these components, the joint sparse representation (JSR) is employed during the classification. JSR operates as multi-task learning algorithm, which represents each component numerically while considering their inner correlations. The original image together with the generated BIMFs are simultaneously represented by JSR to determine the target label according to the output reconstruction errors. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set demonstrate the validity of the proposed method under different operating conditions. In comparison with some baseline algorithms, the superiority of the proposed method is furtherly validated.INDEX TERMS Synthetic aperture radar (SAR), target recognition, bidimensional empirical mode decomposition (BEMD), joint sparse representation (JSR).

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“…As an adaptive and nonrecursive signal decomposition algorithm, VMD was validated to achieve better efectiveness and robustness than similar algorithms such as wavelet analysis and EMD. [91][92][93]. Specifcally, the related works demonstrated that the VMD algorithm is less sensitive to noise corruption than the EMD-based decomposition ones because Wiener fltering is used to update the decomposed components directly in the Fourier domain.…”

Section: Introductionmentioning

confidence: 99%

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

Weng

2023

Scientific Programming

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A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to jointly represent them. According to the resulted reconstruction errors of different classes, the target label of test sample can be classified. The moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up the standard operating condition (SOC) and several extended operating conditions (EOCs) including configuration variants, depression angle variances, noise corruption, and partial occlusion to test and validate the proposed method. The results confirm the effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR references.

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Bidimensional empirical mode decomposition method for image processing in sensing system

Cited by 11 publications

References 16 publications

Incorporating Visual Defect Identification and Determination of Occurrence Side in Touch Panel Quality Inspection

Incorporating Visual Defect Identification and Determination of Occurrence Side in Touch Panel Quality Inspection

Bidimensional Empirical Mode Decomposition for SAR Image Feature Extraction With Application to Target Recognition

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

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