Query: Morphological filtering

title: Early fault detection method for rolling bearing based on multiscale morphological filtering of information entropy threshold
abstract: The scale of structure element is especially important to obtain good filtering results in multiscale morphological filtering (MMF) method. In general, the optimal scale of structure element is set to be a fixed value in traditional morphological filter, therefore it is difficult to extract the fault feature from rolling bearing vibration signal effectively. A novel multiscale morphological filtering algorithm is proposed based on information entropy threshold (IET MMF) for early fault detection of rolling bearing. Compared with traditional MMF method, several optimal scales of structure elements are achieved according to the energy distribution characteristic of different vibration signals. The information entropy theory is applied to quantify the analyzed signals, and the optimal threshold of information entropy is obtained by iterative algorithm to ensure integrity of useful information. The simulation and rolling bearing experimental analysis results show that the IET MMF method can extract fault features of vibration signals effectively.

title: Combination of EEMD and Morphological Filtering for Baseline Wander Correction in EMG Signals
abstract: This paper aims at proposing an effective method for Baseline Wander removal from the EMG signals. Ensemble Empirical Mode Decomposition (EEMD) Algorithm is first applied to the baseline corrupted EMG signals to decompose them into Intrinsic Mode Functions (IMFs) After this step, morphological filtering employing octagon shaped structuring element has been applied to filter out each IMF. Finally, the results of the proposed filtering methodology are compared with those of EMD and EEMD based filtering methods. Simulation results report that the methodology used in this study has eliminated the baseline wander from EMG signals with minimal distortions.

title: Binary Morphological Filtering of Dominant Scattering Area Residues for SAR Target Recognition
abstract: A synthetic aperture radar (SAR) target recognition method is proposed in this study based on the dominant scattering area (DSA) DSA is a binary image recording the positions of the dominant scattering centers in the original SAR image. It can reflect the distribution of the scattering centers as well as the preliminary shape of the target, thus providing discriminative information for SAR target recognition. By subtracting the DSA of the test image with those of its corresponding templates from different classes, the DSA residues represent the differences between the test image and various classes. To further enhance the differences, the DSA residues are subject to the binary morphological filtering, i.e. the opening operation. Afterwards, a similarity measure is defined based on the filtered DSA residues after the binary opening operation. Considering the possible variations of the constructed DSA, several different structuring elements are used during the binary morphological filtering. And a score level fusion is performed afterwards to obtain a robust similarity. By comparing the similarities between the test image and various template classes, the target label is determined to be the one with the maximum similarity. To validate the effectiveness and robustness of the proposed method, experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and compared with several state of the art SAR target recognition methods.

title: Random noise attenuation by planar mathematical morphological filtering
abstract: ABSTRACTImproving the signal to noise ratio (S/N) of seismic data is desirable in many seismic exploration areas. The attenuation of random noise can help to improve the S/N. Geophysicists usually use the differences between signal and random noise in certain attributes, such as frequency, wavenumber, or correlation, to suppress random noise. However, in some cases, these differences are too small to be distinguished. We used the difference in planar morphological scales between signal and random noise to separate them. The planar morphological scale is the information that describes the regional shape of seismic waveforms. The attenuation of random noise is achieved by removing the energy in the smaller morphological scales. We call our method planar mathematical morphological filtering (PMMF) We analyze the relationship between the performance of PMMF and its input parameters in detail. Applications of the PMMF method to synthetic and field post/prestack seismic data demonstrate good performance compar.

title: Mathematical morphological filtering for linear noise attenuation of seismic data
abstract: ABSTRACTLinear coherent noise attenuation is a troublesome problem in a variety of seismic exploration areas. Traditional methods often use the differences in frequency, wavenumber, or amplitude to separate the useful signal and coherent noise. However, the application of traditional methods is limited or even invalid when the aforementioned differences between useful signal and coherent noise are too small to be distinguished. For this reason, we have managed to develop a new algorithm from the differences in the shape of seismic waves, and thus, introduce mathematical morphological filtering (MMF) into coherent noise attenuation. The morphological operation is calculated in the trace direction of a rotating coordinate system. This rotating coordinate system is along the direction of the trajectory of coherent noise to make the energy of the coherent noise distributed along the horizontal direction. The MMF approach is more effective than mean and median filters in rejecting abnormal values and causes fe.

title: Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering
abstract: One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW) Effective methods for suppressing BW include the wavelet based (WT) and the mathematical morphological filtering based (MMF) algorithms. However, the T waveform distortions introduced by the WT and the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WT to overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinical BW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state of the art methods commonly used in the literature. The results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG.

title: Detecting Morphological Filtering of Binary Images
abstract: Morphological operators are widely used in binary image processing for several purposes, such as removing noise, detecting contours or particular structures, and regularizing shapes. In particular, morphological filters are largely adopted in scanned documents to correct the artifacts caused by acquisition and binarization, as well as other processing. In this paper, we propose a novel approach for forensics detection of morphological filtering on binary images. The proposed technique exploits some mathematical properties of the two basic morphologic operators, erosion and dilation, to define an algorithm able not only to detect the application of the filter, but also to estimate the shape of the relevant structuring element. Experimental tests demonstrate that the technique is effective and robust to the most common operations performed on binary image documents.

title: A new strategy of using a time varying structure element for mathematical morphological filtering
abstract: This paper presents a novel signal processing scheme, namely time varying morphological filtering (TMF) for rolling element bearing fault detection. In contrast with the multiscale morphological filtering (MMF) method, the structure element (SE) used in TMF is no longer fixed. It adjusts adaptively according to the extreme points of a signal so that the raw signal can be fit more accurately. In addition, the MMF needs to execute morphological operations multiple times, whereas the TMF can finish the filtering in one time operation. Consequently, TMF has a significant advantage in terms of computation efficiency. Experimental vibration signals collected from a bearing test rig are employed to evaluate the effectiveness of TMF. The results show that the proposed method can extract fault features of defective rolling element bearings with high computational efficiency. Moreover, five SEs are compared for TMF. The results show that the Dolph Chebyshev SE performs best.

title: Research on an enhanced scale morphological hat product filtering in incipient fault detection of rolling element bearings
abstract: Incipient vibration signals of rolling element bearing are usually characterized by weak fault symptoms and multiple interference source components, which imply that it is difficult to recognize effectively the defects of rolling element bearing at an early stage. To address the issue, a novel early fault detection strategy based on an enhanced scale morphological hat product filtering (ESMHPF) is proposed in this paper. Firstly, motivated by the existing morphology theory, the concept of morphology hat product operation (MHPO) is presented to handle the collected weak fault signal, which can extract efficiently periodic impulse characteristics closely linked to the bearing defects. Subsequently, diagonal slice spectra (DSS) are incorporated into morphological analysis, which can achieve the efficacy of noise rejection and feature enhancement. Ultimately, the optimal scale morphological filtering results are determined by using a sensitive index termed as fault feature ratio (FFR) for identifying weak damage feature and completing early fault detection. Simulated signal and two experimental cases of run to failure are performed to assess the efficacy of the proposed algorithm. The analysis results achieved show that the formulated algorithm can identify clearly early fault symptoms immersed in bearing vibration data. Moreover, the availability of superiority of our designed approach is demonstrated by comparing with traditional multiscale morphological filtering and some existing algorithm. This study provides a new idea for the improvement of incipient damage identification of rolling element bearings.

title: Impulsive Noise Removal from Color Images with Morphological Filtering
abstract: This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.