Morphological Gradient Filter Research Articles

A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video Analytics

Detection of the object in the video is a primary task of all video-processing-based applications. It is one of the challenging areas in computer vision. The paper presents a novel MATLAB-based object detection application based on an improved Gaussian Mixture Model. Gaussian Mixture Model with post-processing applied here for segmentation of foreground from background. The application is divided into three modules pre-processing, detection and post-processing. The morphological gradient filter uses here for segmenting the foreground objects from the background. The proposed method was tested for various video sequences with challenges and proved that the approach performs well for the majority video sequences.

An efficient fuzzy deep learning approach to recognize 2D faces using FADF and ResNet-164 architecture

Deep learning using fuzzy is highly modular and more accurate. Adaptive Fuzzy Anisotropy diffusion filter (FADF) is used to remove noise from the image while preserving edges, lines and improve smoothing effects. By detecting edge and noise information through pre-edge detection using fuzzy contrast enhancement, post-edge detection using fuzzy morphological gradient filter and noise detection technique. Convolution Neural Network (CNN) ResNet-164 architecture is used for automatic feature extraction. The resultant feature vectors are classified using ANFIS deep learning. Top-1 error rate is reduced from 21.43% to 18.8%. Top-5 error rate is reduced to 2.68%. The proposed work results in high accuracy rate with low computation cost. The recognition rate of 99.18% and accuracy of 98.24% is achieved on standard dataset. Compared to the existing techniques the proposed work outperforms in all aspects. Experimental results provide better result than the existing techniques on FACES 94, Feret, Yale-B, CMU-PIE, JAFFE dataset and other state-of-art dataset.

Detection and classification of islanding and power quality disturbances in microgrid using hybrid signal processing and data mining techniques

This study presents multi-scale morphological gradient filter (MSMGF) and short-time modified Hilbert transform (STMHT) techniques, respectively, to detect and classify multiclass power system disturbances in a distributed generation (DG)-based microgrid environment. The non-stationary power signal samples measured near the target DG's are processed through the proposed MSMGF and STMHT techniques, respectively, and some computations over them generates the target parameter sets. Depending on the complexity of the overlapping in the target attribute values for different disturbance patterns, fuzzy judgment tree structure is incorporated for multiclass event classification, which proves to be robust for most of the classes. In this regard, an extensive simulation on the proposed microgrid models, subjected to a number of multiclass disturbances has been performed in MATLAB/Simulink environment. The faster execution, lower computational burden, superior efficiency as well as better accuracy in multiclass power system disturbance classification by the proposed judgment tree-based MSMGF and STMHT techniques, respectively, as compared to some of the conventional techniques, is significantly illustrated in the performance evaluation section. Further, as illustrated in this section, the real-time capability of the proposed techniques has been verified in the hardware environment, where the results shown are satisfactory.

Spline Interpolation Sub Pixel Edge Detection Method Based on Improved Morphological Gradient

In order to improve the accuracy of image edge detection.A spline interpolation sub pixel edge detection method based on improved morphological gradient is proposed in the thesis.Firstly,using improved morphological gradient filter operator for image coarse positioning;Then,the cubic spline interpolation method is carried out for pixel-level edge of the image interpolation so that the image edge locates in sub-pixel level.Have a simulation experiment to improved methods by Matlab, results show that the improved method can accurately detect the edge of the image, edge detection is fine, the precision of positioning is hight and the result of detection is good.

An Improved Multi-Scale Morphological Undecimated Wavelet Method and its Application in Rolling Bearing Fault Feature Extraction

For the purpose of extract the fault feature hidden by strong noise background in rolling bearing fault signal, a morphological undecimated wavelet method was proposed. A undecimated wavelet decomposition operator called gradient filter was presented based on an open-closed and closed-open mixed filter. The morphological gradient filter was used to extract the impulse feature of signal. The type and the length of structure elements used in these filters were alterable adapt to the signal. The method was applied to analyze the simulated data and measured vibration signals from the bearing with fault. The results confirm that the proposed method is feasible in impulse feature extraction of signal, and it is more effective than other traditional morphological undecimated wavelet methods.

Gear fault detection using adaptive morphological gradient lifting wavelet

Wavelet transform is one of the most acceptable tools to analyze vibration signals for gear fault detection. However, there are still some limitations of the traditional wavelet transforms due to the utilization of fixed linear filters. This investigation presents an adaptive morphological gradient lifting wavelet (AMGLW) to remedy the shortcomings of traditional wavelet transform schemes. A novel nonlinear filter, named morphological gradient filter, is designed for enhancing the impulsive features of the original signal. Then the adaptability of AMGLW is implemented by selecting between two filters, namely the average filter and the morphological gradient filter, to update the approximation signal dependent upon the local gradient of the analyzed signal. This new scheme is evaluated on a simulated signal and a practical vibration signal measured from a gearbox. Experimental results demonstrate that the presented AMGLW outperforms the traditional linear wavelet (LW) transform obviously for detecting gear defects. Furthermore, the computational cost of AMGLW is much less than the traditional LW. Thus the AMGLW scheme is quite suitable for the online condition monitoring of gears.

An adaptive morphological gradient lifting wavelet for detecting bearing defects

This paper presents a novel wavelet decomposition scheme, named adaptive morphological gradient lifting wavelet (AMGLW), for detecting bearing defects. The adaptability of the AMGLW consists in that the scheme can select between two filters, mean the average filter and morphological gradient filter, to update the approximation signal based on the local gradient of the analyzed signal. Both a simulated signal and vibration signals acquired from bearing are employed to evaluate and compare the proposed AMGLW scheme with the traditional linear wavelet transform (LWT) and another adaptive lifting wavelet (ALW) developed in literature. Experimental results reveal that the AMGLW outperforms the LW and ALW obviously for detecting bearing defects. The impulsive components can be enhanced and the noise can be depressed simultaneously by the presented AMGLW scheme. Thus the fault characteristic frequencies of bearing can be clearly identified. Furthermore, the AMGLW gets an advantage over LW in computation efficiency. It is quite suitable for online condition monitoring of bearings and other rotating machineries.

A weighted multi-scale morphological gradient filter for rolling element bearing fault detection

This paper presents a novel signal processing scheme, named the weighted multi-scale morphological gradient filter (WMMG), for rolling element bearing fault detection. The WMMG can depress the noise at large scale and preserve the impulsive shape details at small scale. Both a simulated signal and vibration signals from a bearing test rig are employed to evaluate the performance of the proposed technique. The traditional envelope analysis and a multi-scale enveloping spectrogram algorithm combining continuous wavelet transform and envelope analysis (WT-EA) are also studied and compared with the presented WMMG. Experimental results have demonstrated the effectiveness of the WMMG to extract the impulsive components from the raw vibration signal with strong background noise. We also investigated the classification performance on identifying bearing faults based on the WMMG and statistical parameters with varied noise levels. Application results reveal that the WMMG achieves the same or better performance as EA and WT-EA. Meanwhile, the WMMG requires low computation cost and is very suitable for on-line condition monitoring of bearing operating states.