Bidimensional Empirical Mode Decomposition Algorithm Research Articles

基于二维相关近红外光谱的亚麻籽油掺杂检测

Flaxseed oil is rich in α-linolenic acid and other nutrients, and the adulteration happens frequently because of its high price. To detect the adulteration of flaxseed oil quickly and accurately, a method was proposed based on weighted reconstructed two-dimensional correlation near-infrared(NIR) spectra. The near-infrared spectra of 79 adulterated flaxseed oil samples (adulterated by rapeseed oil with the doping volume ratio 1%-40%) were measured, and the traditional two-dimensional correlation synchronous spectra were calculated. The two-dimensional correlation synchronous spectra of all samples were decomposed into multiple components of different scales by the bi-dimensional empirical mode decomposition algorithm (BEMD). According to the root mean square error(RMSE) values of the adulteration detection sub-models established by each component, the weights of the corresponding components were calculated, and then the two-dimensional correlation spectra of all samples were reconstructed by accumulating the weighted components. A quantitative analysis model of flaxseed oil adulteration was established based on the weighted reconstructed two-dimensional correlation spectra combined with the N-way partial least square(N-PLS)algorithm. Compared with the traditional two-dimensional correlation spectroscopy, the model built by the weighted reconstructed two-dimensional correlation spectra had better performance with the calibration determination coefficient increased by 6.05%, and the prediction determination coefficient increased by 7.5%. The proposed method could effectively enhance the spectral feature information, reduce the spectral noise interference, and hence provide a new idea for the detection of edible oil adulteration.

Evaluation of Three-Dimensional Surface Roughness in Microgroove Based on Bidimensional Empirical Mode Decomposition.

Micromilling is an extremely important advanced manufacturing technology in the micromanufacturing industry. Compared with the traditional milling process, micromilling has stricter requirements on the surface roughness of the workpiece, and the roughness of the microcurved surface is not easy to measure. In order to more accurately characterize the curved surface morphology of the microgrooves obtained by micromilling, this paper proposes a method to extract the reference plane of the curved surface based on the bidimensional empirical mode decomposition algorithm and characterize the three-dimensional surface roughness of the curved surface. First, we synthesize the morphologies of the microgrooves by simulated non-Gaussian rough surfaces and models of textures. Second, the bidimensional empirical mode decomposition algorithm was used to extract the reference planes of the simulated microgrooves. Third, the three-dimensional roughness parameters suitable for the curved surfaces of microgrooves were selected to establish an evaluation system. The results show that the mean squared errors of the reference planes are below 1%, so bidimensional empirical mode decomposition can effectively extract reference planes, and the evaluation system of three-dimensional surface roughness proposed in this paper reflects morphological characteristics of the curved surfaces of microgrooves more thoroughly than that of two-dimensional surface roughness parameters.

Single-shot fringe pattern phase retrieval using improved period-guided bidimensional empirical mode decomposition and Hilbert transform.

Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD - we have considerably increased algorithm's application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data.

Hyperspectral image classification using support vector machines

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

Relative vibration identification of cutter and workpiece based on improved bidimensional empirical mode decomposition

In the process of cutting, the relative vibration between the cutter and the workpiece has an important effect on the surface topography. In this study, the bidimensional empirical mode decomposition (BEMD) method is used to identify such effect. According to Riesz transform theory, a type of isotropic monogenic signal is proposed. The boundary data is extended on the basis of a similarity principle that deals with serious boundary effect problem. The decomposition examples show that the improved BEMD can effectively solve the problem of boundary effect and decompose the original machined surface topography at multiple scales. The characteristic surface topography representing the relative vibration between the cutter and the workpiece through feature identification is selected. In addition, the spatial spectrum analysis of the extracted profile is carried out. The decimal part of the frequency ratio that has an important effect on the shape of the contour can be accurately identified through contour extraction and spatial spectrum analysis. The decomposition results of simulation and experimental surface morphology demonstrate the validity of the improved BEMD algorithm in realizing the relative vibration identification between the cutter and the workpiece.

Bidimensional Empirical Mode Decomposition-Based Diffusion Filtering for Image Denoising

In this paper, a new method is proposed for image denoising inspired by the bidimensional empirical mode decomposition algorithm and the diffusion-based filtering. In the bidimensional empirical mode decomposition technique, the noisy image is decomposed into its respective intrinsic mode functions and the high-frequency and the low-frequency noises are removed with the help of the proposed diffusion filtering method using its parameters such as connectivity, conductance function, gradient threshold and the number of iterations. The image is reconstructed with the help of these denoised intrinsic mode functions. Performance of the proposed technique is evaluated in terms of the peak signal-to-noise ratio, the structural similarity index, the mean square error and is then compared with the already existing techniques on the image denoising. From the experimentation and results obtained, it is clear that the bidimensional empirical mode decomposition method using the diffusion filtering algorithm is efficient for the denoising of the images both qualitatively and quantitatively.

Automatic fringe pattern enhancement using truly adaptive period-guided bidimensional empirical mode decomposition.

Fringe patterns encode the information about the result of a measurement performed via widely used optical full-field testing methods, e.g., interferometry, digital holographic microscopy, moiré techniques, structured illumination etc. Affected by the optical setup, changing environment and the sample itself fringe patterns are often corrupted with substantial noise, strong and uneven background illumination and exhibit low contrast. Fringe pattern enhancement, i.e., noise minimization and background term removal, at the pre-processing stage prior to the phase map calculation (for the measurement result decoding) is therefore essential to minimize the jeopardizing effect the mentioned error sources have on the optical measurement outcome. In this contribution we propose an automatic, robust and highly effective fringe pattern enhancement method based on the novel period-guided bidimensional empirical mode decomposition algorithm (PG-BEMD). The spatial distribution of the fringe period is estimated using the novel windowed approach and then serves as an indicator for the truly adaptive decomposition with the filter size locally adjusted to the fringe pattern density. In this way the fringe term is successfully extracted in a single (first) decomposition component alleviating the cumbersome mode mixing phenomenon and greatly simplifying the automatic signal reconstruction. Hence, the fringe term is dissected without the need for modes selection nor summation. The noise removal robustness is ensured employing the block matching 3D filtering of the fringe pattern prior to its decomposition. Performance validation against previously reported modified empirical mode decomposition techniques is provided using numerical simulations and experimental data verifying the versatility and effectiveness of the proposed approach.

Building Detection in SAR Images Based on Bi-Dimensional Empirical Mode Decomposition Algorithm

This letter proposes a new synthetic aperture radar (SAR) image building detection method based on the bi-dimensional empirical mode decomposition (BEMD) algorithm, well adapted to nonlinear and nonstationary signals. First, the SAR image is decomposed by the BEMD algorithm to generate intrinsic mode functions (IMFs), and the IMFs are combined to extract bright regions and dark regions from the SAR image. Next, the Markov random field (MRF) model is used to cluster the SAR image, and according to the centroid of the dark region, the dark category with a complete edge is located. Finally, the building is detected by combining the bright region, the dark region, and the given shadow direction. Analytical and experimental evidence show that the proposed method has high detection accuracy and has wide applicability for medium, large, and irregular shaped buildings with shadows.

Bi-dimensional empirical mode decomposition (BEMD) algorithm based on particle swarm optimization-fractal interpolation

The performance of interpolation algorithm used in bi-dimensional empirical mode decomposition directly affects its popularization and application. Therefore, the research on interpolation algorithm is more reasonable, accurate and fast. So far, in the interpolation algorithm adopted by the bi-dimensional empirical mode decomposition, an adaptive interpolation algorithm can be proposed according to the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. The parameters involved in the interpolation process are optimized by particle swarm optimization algorithm, and the optimal parameters are obtained, which can solve the problem of low efficiency and low precision of interpolation algorithm used in bi-dimensional empirical mode decomposition. It solves the problem that the image cannot be decomposed to obtain accurate and reliable bi-dimensional intrinsic modal function, and realize the fast decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

Bi-dimensional Empirical Mode Decomposition Algorithm Based on Particle Swarm-Fractal Interpolation

Bi-dimensional Empirical Mode Decomposition Algorithm Based on Particle Swarm-Fractal Interpolation

A pixel-level entropy-weighted image fusion algorithm based on bidimensional ensemble empirical mode decomposition

The bidimensional empirical mode decomposition algorithm is more suitable to handle image fusion than the traditional multi-scale decomposition methods in the image fusion area. Nevertheless, there...

An adaptive method of multi-scale edge detection for underwater image

This paper presents a new approach for underwater image analysis using the bi-dimensional empirical mode decomposition (BEMD) technique and the phase congruency information. The BEMD algorithm, fully unsupervised, it is mainly applied to texture extraction and image filtering, which are widely recognized as a difficult and challenging machine vision problem. The phase information is the very stability feature of image. Recent developments in analysis methods on the phase congruency information have received large attention by the image researchers. In this paper, the proposed method is called the EP model that inherits the advantages of the first two algorithms, so this model is suitable for processing underwater image. Moreover, the receiver operating characteristic (ROC) curve is presented in this paper to solve the problem that the threshold is greatly affected by personal experience when underwater image edge detection is performed using the EP model. The EP images are computed using combinations of the Canny detector parameters, and the binaryzation image results are generated accordingly. The ideal EP edge feature extractive maps are estimated using correspondence threshold which is optimized by ROC analysis. The experimental results show that the proposed algorithm is able to avoid the operation error caused by manual setting of the detection threshold, and to adaptively set the image feature detection threshold. The proposed method has been proved to be accuracy and effectiveness by the underwater image processing examples.

BEMD–SIFT feature extraction algorithm for image processing application

Scale-invariant feature transform (SIFT) algorithm has been successfully applied to object recognition and to image feature extraction, which is a major application in the field of image processing. Nonetheless, the SIFT algorithm has not been solved effectively in practical applications that requires real-time performance, much calculation, and high storage capacity given the framework level and the iterative calculation process in the SIFT Gaussian blur operation. The extraction of image feature information is accelerated using the speeded-up robust features algorithm. However, this algorithm remains sensitive to complicated deformation. To address these problems, in this paper, we proposes a novel algorithmic framework based on bidimensional empirical mode decomposition (BEMD) and SIFT to extract self-adaptive features from images. First, the BEMD algorithm is used to decompose the self-adaptive features of the original image and to obtain multiple BIMF components. Second, the SIFT algorithm optimizes the extraction of parameters that reflect characteristic information on BIMF components. Related parameters are obtained through genetic algorithm optimization. Third, the method for extracting the characteristic information of the BIMF components involves synthesizing all of the accumulated characteristic information in the original image. Comparison results show that the method of calculating image feature extraction speed, accuracy, and reliability has a stronger effect than other methods.

Empirical mode decomposition of digital mammograms for the statistical based characterization of architectural distortion.

Among the different and common mammographic signs of the early-stage breast cancer, the architectural distortion is the most difficult to be identified. In this paper, we propose a new multiscale statistical texture analysis to characterize the presence of architectural distortion by distinguishing between textural patterns of architectural distortion and normal breast parenchyma. The proposed approach, firstly, applies the bidimensional empirical mode decomposition algorithm to decompose each mammographic region of interest into a set of adaptive and data-driven two-dimensional intrinsic mode functions (IMF) layers that capture details or high-frequency oscillations of the input image. Then, a model-based approach is applied to IMF histograms to acquire the first order statistics. The normalized entropy measure is also computed from each IMF and used as a complementary textural feature for the recognition of architectural distortion patterns. For evaluating the proposed AD characterization approach, we used a mammographic dataset of 187 true positive regions (i.e. depicting architectural distortion) and 887 true negative (normal parenchyma) regions, extracted from the DDSM database. Using the proposed multiscale textural features and the nonlinear support vector machine classifier, the best classification performance, in terms of the area under the receiver operating characteristic curve (or Az value), achieved was 0.88.

Multiscale Self-coordination of Bidimensional Empirical Mode Decomposition in Image Fusion

The bidimensional empirical mode decomposition (BEMD) algorithm with high adaptability is more suitable to process multiple image fusion than traditional image fusion. However, the advantages of this algorithm are limited by the end effects problem, multiscale integration problem and number difference of intrinsic mode functions in multiple images decomposition. This study proposes the multiscale self-coordination BEMD algorithm to solve this problem. This algorithm outside extending the feather information with the support vector machine which has a high degree of generalization, then it also overcomes the BEMD end effects problem with conventional mirror extension methods of data processing, . The coordination of the extreme value point of the source image helps solve the problem of multiscale information fusion. Results show that the proposed method is better than the wavelet and NSCT method in retaining the characteristics of the source image information and the details of the mutation information inherited from the source image and in significantly improving the signal-to-noise ratio.

A novel infrared small target detection method based on BEMD and local inverse entropy

Compared to other targets, it is more difficult to detect infrared small targets due to several aspects such as the low signal to noise ratio, low contrast, small size, the lack of shape and texture information of the targets, especially under complex background. In this paper, a novel infrared small target detection method based on peer group filter (PGF), bi-dimensional empirical mode decomposition (BEMD) and local inverse entropy (LIE) is proposed to overcome these difficulties. The PGF is implemented to remove the noise and improve the signal-to-noise ratio of the initial image. Our proposed BEMD algorithm is able to estimate the background effectively and get the target image by removing the background from the original image and segmenting the Intrinsic Mode Functions (IMFs) making use of the local inverse entropy. Experimental results demonstrate that the novel method can extract the small targets validly and accurately.

Denoising and extracting background from fringe patterns using midpoint-based bidimensional empirical mode decomposition.

We propose a 2D generalization to the midpoint-based empirical mode decomposition algorithm (MBEMD). Unlike with the regular bidimensional empirical mode decomposition algorithm (BEMD), we do not interpolate the upper and lower envelopes, but rather directly find the mean envelope, utilizing well-defined points between two extrema of different kinds (midpoints). This approach has several advantages, such as improved spectral selectivity and better time performance over the regular BEMD process. The MBEMD algorithm is then applied to the task of the interferometric fringe pattern analysis, to identify its distinct components. This allows separating the oscillatory pattern component, which is of interest, from the background, noise, and possibly other spurious interferometric patterns. Such an enhancement is meant to aid further phase demodulation and reduce its errors. Flexibility of the adaptive method allows for processing correlation fringe patterns met in the digital speckle pattern interferometry as well as the regular interferometric fringe patterns without any special tuning of the algorithm.

An improved quantitative analysis method for plant cortical microtubules.

The arrangement of plant cortical microtubules can reflect the physiological state of cells. However, little attention has been paid to the image quantitative analysis of plant cortical microtubules so far. In this paper, Bidimensional Empirical Mode Decomposition (BEMD) algorithm was applied in the image preprocessing of the original microtubule image. And then Intrinsic Mode Function 1 (IMF1) image obtained by decomposition was selected to do the texture analysis based on Grey-Level Cooccurrence Matrix (GLCM) algorithm. Meanwhile, in order to further verify its reliability, the proposed texture analysis method was utilized to distinguish different images of Arabidopsis microtubules. The results showed that the effect of BEMD algorithm on edge preserving accompanied with noise reduction was positive, and the geometrical characteristic of the texture was obvious. Four texture parameters extracted by GLCM perfectly reflected the different arrangements between the two images of cortical microtubules. In summary, the results indicate that this method is feasible and effective for the image quantitative analysis of plant cortical microtubules. It not only provides a new quantitative approach for the comprehensive study of the role played by microtubules in cell life activities but also supplies references for other similar studies.

A NEW BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION BY USING RADON TRANSFORM

Based on a detailed analysis for the one-dimensional intrinsic mode function, a model of the bidimensional intrinsic mode function is found firstly. Then a new bidimensional empirical mode decomposition algorithm is developed by using the Radon transform. The existing methods have to locate the maxima and minima in a bidimensional function, and further, produce bidimensional envelopes. Those are usually hard for a real two-dimensional function. The proposed algorithm successfully avoids these difficulties. Experiments show encouraging results.