Texture Classification based on Bidimensional Empirical Mode Decomposition and Local Binary Pattern

This paper presents a new approach of edge detection utilizing bidimensional empirical mode decomposition (BEMD) technique. For this purpose a recently developed fast and adaptive BEMD (FABEMD) is employed to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of the surface interpolation, which enables fast decomposition and well characterized BIMFs. Since the first BIMF provides the highest local spatial variations and/or scales of the image, this BIMF is then processed for obtaining the edge. Binarization and morphological operations are applied as post processing operations to the first BIMF to achieve the desired edge map. The proposed method is compared with two other standard techniques namely, Canny and Sobel edge operators. Simulation results with real images demonstrate the efficacy of the proposed algorithm for edge detection.

To address the issue of spectrum overlapping in Fourier transform profilometry, a new method based on Bi-dimensional Empirical Mode Decomposition (BEMD) is proposed. BEMD is an adaptive data decomposition method, so it does not need filters or basic functions which are important for Fourier transform or wavelet transform. In this paper, the complicated original signal of distorted fringe pattern is decomposed into several Bi-dimensional Intrinsic Mode Functions (BIMFs) as well as the residual component, with which the background component and some other frequency noises of fringe pattern can be eliminated effectively. It is beneficial to extract the first frequency component exactly for the subsequent wrapped phase retrieval in Fourier transform. Simulation and experiments illustrate the feasibility and the exactness of the proposed method.

Slope stability is a key problem for safe submarine channel operation on muddy coasts. The early detection and identification of slope instabilities is important to allow safety measures to be taken in a timely manner. Owing to the difficulties in predicting or providing early forecasts of slope collapse in muddy submarine channels, this study introduces a bidimensional empirical mode decomposition (BEMD) with adaptive time–frequency resolution to identify slope instabilities using sound prints of sub-bottom profile images. The results show that three bidimensional intrinsic mode function (BIMF) images decomposed by BEMD with frequencies ranging from high to low can depict the spatial variation in the sound prints. The second and third BIMF images reveal the spatial relationship between the sound prints and the slope abnormalities at the early stage of slope collapse. This approach effectively identifies the early sound prints and the spatial information of the sub-bottom profile images during the process of slope collapse, which can improve abnormal feature recognition. This method will help in the prediction and early forecast of channel slope instability and collapse.

In digital holography, the speckle noise caused by the coherent nature of the light source and the light scattering generated by the light path system degrade the quality of the reconstructed image seriously. Therefore, in this paper, we propose what we believe to be is a novel noise reduction method combining bidimensional empirical mode decomposition (BEMD) with the variational method, termed BEMDV. The reconstructed image is first decomposed into a series of bidimensional intrinsic mode function (BIMF) components with different frequencies using the BEMD method, and then a certain number of BIMF components are selected for noise reduction by the variational method. An improved particle swarm optimization algorithm is adopted to optimize the key parameters of the proposed method, so as to further improve its noise reduction performance. A reflective off-axis digital holographic imaging system is used to collect the holograms of the coin and optical resolution plate, and the experimental research on noise reduction is carried out. The results with qualitative and quantitative analyses show that the proposed method achieves a better performance on noise reduction and detail preservation than other general methods, enormously enhancing the image quality of holographic reconstruction.

In this paper window empirical mode decomposition (WEMD) is proposed and is used to do texture segmentation. Empirical mode decomposition (EMD) can decompose the nonstationary and nonlinear signals by sifting into a few intrinsic mode functions (IMFs) which represent a simple oscillatory mode of local data. However, the traditional bidimensional EMD (BEMD) has two drawbacks of the gray spots in IMF image and the slow computation speed. WEMD can solve such problems. Based on the characteristic of WEMD and local time/space-frequency analysis of structure multivector, the renovate technique of texture segmentation is also presented. Characterized by the local amplitude and the local frequency of every IMF component, the texture image can be segmented by k-means clustering algorithm. The subsequent experimental results indicate this method's effectiveness.

Empirical mode decomposition (EMD) method based on HHT has exhibited unique advantages such as adaptability and highly efficiency in many nonlinear, nonstationary signals processing applications. It breaks the uncertainty principle limit, but the traditional EMD still has its deficiencies. In this article, we construct a new wavelet which has excellent decomposing-frequency performance and energy concentration, and then an improved EMD method based on this wavelet is presented. Results of numerical simulation show the validity and efficiency of the method proposed in paper are better than traditional one. Furthermore, some foreseeable trends of time-frequency distribution technologies are described. The systems in reality, strictly speaking, tend to non-linear, so most practical signals are non-stationary random signals. Nonlinear, nonstationary signals analysis is a very significant and difficult problem in almost all technical fields such as automation, communication, aerospace- engineering, biomedicine, structural fault diagnosis and so on. Owed to the rapid development of large scale integrated circuit technology and artificial intelligence, the exploration of signal processing theories have got a sharply impetus. A series of new modern signal processing theories and methods have appeared to meet the need of time-frequency joint analysis of nonlinear, non-Gaussian and non-stationary signals, including discrete short-time Fourier transform, wavelet transform, Hilbert-Huang transform and so on. Time-frequency joint analysis can observe the evolution of the signal in the time domain and the frequency domain simultaneously, provide local time-frequency characteristics of the signal.

The complex bidimensional empirical mode decomposition

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier.KeywordsLBPLocal binary patternLRBPLocal roughness binary patternSVMSupport vector machineKNN K-nearest neighbors

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

In seismic exploration, establishing a reliable velocity model is crucial for seismic migration imaging and reservoir characterization. Full-waveform inversion (FWI) is used to recover subsurface physical parameters by iteratively minimizing the misfit between the observed and synthetic data, theoretically offering the highest modeling accuracy. FWI is a combination of migration imaging and tomography. However, for reflection data, the update of the tomographic component is notably weaker than the migration component. When the gradient is dominated by the migration component, FWI can easily fall into local minima, which leads to bad inversion results. Hence, it is necessary to extract the tomographic component from the original FWI gradient to build a robust background velocity model when the initial model is inaccurate. In this paper, we develop to use the bidimensional empirical mode decomposition (BEMD) method to separate the original FWI gradient into the tomography and migration component. The BEMD method first adaptively decomposes the gradient into different-scale subcomponents. Then, the small-scale bidimensional intrinsic mode functions are combined into the migration component, and the large-scale residual component is considered to be the tomographic component. At the beginning of FWI, the background velocity is updated using the tomographic component, and with iterations, small-scale components are gradually added to update the velocity model. Numerical experiments show that the BEMD method can effectively separate the two components. The BEMD-based FWI can reconstruct the background velocity model, improving the FWI accuracy.

Breast cancer causes serious public health problems; it is the most common cancer among women worldwide. Screening and early detection of signs of breast cancer increase the chance of survival. Early diagnosis is a crucial task for radiologists and physicians. Therefore, many Computer-Aided Detection and Diagnosis (CADx) systems are being developed to ensure the survival of radiologists’ decisions. In this paper, we describe a framework to automate assessment of suspicious regions, detected in screening mammography, without having carried out additional examinations, especially unnecessary biopsies in the case where the suspect regions are benign tumors. The setup of the proposed framework is ordered as follows: Regions of interest (ROIs) have been segmented using a modified K-means algorithm; the Bi-dimensional empirical mode decomposition (BEMD) algorithm is applied to derive many layers (Bi-dimensional Intrinsic Mode Function BIMF) from ROIs. Then, textural features are extracted from the obtained ROIs. First, directly from segmented ROI, second from the ROI and its sub-layers (BIMFs + Residue). The features extracted in the second time have been grouped into a bag descriptive of the ROI under consideration. This bag is the input parameter of the classification algorithm based on the support vector machine (which has been confirmed to be beneficial for the classification of breast cancer). The average obtained sensitivity, specificity, accuracy and AUC rates, are, respectively 98.60%, 98.65%, 98.62%, and 98.23%. Generally, the experimental results in: INbreast, DDSM, and MIAS datasets demonstrate the robustness and the efficiency of the developed framework compared to previous works in the literature and have shown a significant advance.

Extracting magnetic anomalies based on an improved BEMD method: A case study in the Pangxidong Area, South China

It is very useful to study the signals radiated from corona discharges for the purposes of high-voltage line monitoring. Time-frequency analysis can clearly reveal the time-varying spectrum characteristics of such signals, which is very useful for analyzing and processing the non-linear and non-stationary weak signals, such as the signals radiated from corona discharges. Several time-frequency analysis methods, such as the Short-Time Fourier Transform (STFT), Wigner-Ville distribution and the Hilbert-Huang Transform (HHT) and so on, are used in this paper. The simulation data with the same and different amplitudes are comparatively analyzed by these time-frequency distribution methods. It can be concluded that the time-frequency analysis method based on HHT is more efficient to identify and suitable for the non-linear and non-stationary weak signals.

Empirical mode decomposition (EMD) is a fully unsupervised and data-driven approach to the class of nonlinear and non-stationary signals. A new approach is proposed, namely PHEEMD, to image analysis by using Peano–Hilbert space filling curves to transform 2D data (image) into 1D data, followed by ensemble EMD (EEMD) analysis, i.e., a more robust realization of EMD based on white noise excitation. Tests’ results have shown that PHEEMD exhibits a substantially reduced computational cost compared to other 2D-EMD approaches, preserving, simultaneously, the information lying at the EMD domain; hence, new perspectives for its use in low computational power devices, like portable applications, are feasible.

PLBP: An effective local binary patterns texture descriptor with pyramid representation