Mathematical Morphology Theory Research Articles

Object tracking method based on edge detection and morphology

With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\ imes 3$$\\end{document} is improved to a convolution kernel of 2×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\ imes 2$$\\end{document} in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate.

Time–frequency multiresolution of fault-generated transient signals in transmission lines using a morphological filter

The ongoing transformation of electrical power systems highlights the weaknesses of the protection schemes of traditional devices because they are designed and configured according to traditional characteristics of the system. Therefore, this work proposes a new methodology to study the fault-generated high frequency transient signals in transmission lines through multiresolution analysis. The high frequency components are determined by a new digital filtering technique based on mathematical morphology theory and a spectral energy index. Consequently, wide spectra of signals in the time–frequency domain are obtained. The performance of this method is verified on an electrical power system modeled in ATP-Draw, where simulation and test signals are developed for different locations, fault resistances, inception angles, high frequency noises, sampling frequencies, types of faults, and shapes of the structuring element. The results show the characteristics of the fault such as the traveling wave frequency, location, and starting time.

Stray Light Suppression of Wide-Field Surveillance in Complicated Situations

Wide field and long exposure time can effectively improve the ability of space surveillance telescope to detect weak space targets. However, this is easily affected by stray light, resulting in the effective target submerged in the background of stray light. Based on the basic theory of Mathematical morphology, this paper proposes an accurate and highly robust method (ETH) for stray light suppression of space surveillance telescope, which is called the enhanced Top-Hat transform. Firstly, we define the Generalized Top-Hat transform according to the traditional Top-Hat transform. Secondly, we improve the background estimation of the traditional opening, introduce the closing to realize the multi-scale micro adjustment, and analyze the influence of the multi-scale micro adjustment on the stray light suppression and space weak targets segmentation. Finally, we introduce the noise suppression factor to reduce the residual noise in the stray light. In the field experiment, the method can effectively eliminate the interference of stray light, and greatly improves the local signal-to-noise ratio of space targets. It also shows that in a very low signal-to-noise ratio, this method can accurately and effectively segment space weak targets, and has strong robustness.

CMYK channel modification to optimize optical yarn color mixing effects for multicolored Jacquard artwork reproduction

Multicolored Jacquard artwork reproduction has been limited by the current setting of weaving machinery. Novel weaving applications have been introduced to overcome these current restrictions. The subtractive cyan, magenta, yellow and black system used for color printing has been important in optical yarn color mixing of Jacquard color production, because a wide scope of weave color production is possible with a small number of weft yarns. Previously, cyan, magenta, and yellow channels have been modified to resolve current restrictions in reproducing saturated black and secondary colors, but these experiments have not been successful. However, the generation of secondary color ranges is possible by mixing a pair of cyan, magenta, and yellow color yarns. In addition, it is feasible to control chroma levels of primary and secondary colors by mixing with a black yarn. Therefore, the potential of using four weft yarn colors is re-examined for the reproduction of multicolored artworks in relation to cost and production efficiency. Based on a mathematical morphology theory, cyan, magenta, and yellow color channels are altered in the use of image processing tools offered by Adobe Photoshop. A pair of the three color channels is combined under mathematical functions and they are modified through four steps. As a result, new cyan, magenta, and yellow color channels are created to optimize optical yarn color mixing effects. This study introduces details of the cyan, magenta, and yellow channel modification process and experiment results that examine the significance of the newly developed cyan, magenta, and yellow color channels.

Photovoltaic Power Station Electromagnetic Environment Complexity Evaluation Utilizing Logarithmic Morphological Gradient Spectrum

In this paper, a feature extraction method for evaluating the complexity of the Electromagnetic Environment (EME) of the photovoltaic power station is presented by using logarithmic morphological gradient spectrum (LMGS) based on the mathematical morphological theory. We use LMGS to evaluate electromagnetic environment signals. We also explored the impact of structure element (SE) on the MS, MGS, and LMGS. Three types of SE, mean the line SE, square SE and diamond SE, are utilized and compared for computing the LMGS. EME signals with four complexity degrees are simulated to evaluate the effectiveness of the presented method. The experimental results have shown that the feature extraction scheme proposed in this paper is a reasonable method to classify the complexity of EME.

Next generation antivirus endowed with bitwise morphological extreme learning machines

Background and ObjectiveEvery second, on average, 8 (eight) new malware are created. So, our goal is to propose an antivirus, endowed with artificial intelligence, able of identifying malwares through models based on fast training and high-performance neural networks. MethodsOur NGAV (Next Generation Antivirus) is equipped with an authorial ELM (Extreme Learning Morphological) machine. Our bmELMs (Bitwise-Morphological ELMs) are inspired by the image processing theory of Mathematical Morphology. We claim that bmELMs are able to adapt in any machine learning dataset. Inspired by Mathematical Morphology, our bmELMs are capable of modeling any form present at the decisions boundaries of neural networks. ResultsOur bmELMs results are compared with classical ELMs and evaluated through widely used classification metrics. Our antivirus, provided with Bitwise-Morphology, achieves an average accuracy of 97.88%, 93.07%, 93.07% and 91.74% in malware detection of PE (Portable Executable), Java, JavaScript and PHP, respectively. ConclusionsOur NGAV enables high performance, large capacity of parallelism, and simple, low-power architecture with low power consumption. We concluded that our Bitwise-Morphology assists to the main requirements for the proper operation and confection of antivirus in hardware.

The pixel crack reconstruction method: From fracture image to crack geological model for fracture evolution simulation

In this work, we propose a pixel crack reconstruction method based on digital image processing and mathematical morphology theory. To ensure that the obtained crack data are suitable for fracture evolution simulation, a coarsening coefficient is introduced. Different crack geological models are then established to characterize the heterogeneous characteristics of different specimens. The completed simulation models are carried out in combination with the cohesive element method. The comparison of the numerical simulation results with the corresponding experimental results demonstrates the feasibility of the pixel crack reconstruction method. The influence of the coarsening coefficient on the crack visualization and fracture evolution simulation is then discussed, and the application prospects, limitations, and solutions of the pixel crack reconstruction method are described. The results indicate that the selection of the coarsening coefficient must comprehensively consider its influence on the reconstruction accuracy and numerical simulation efficiency. The reconstruction accuracy could be improved by increasing the resolution of the obtained fracture image. In addition, the fracture evolution is readily affected by possible heterogeneous characteristics. The method proposed herein is of great significance for fracture evolution simulation in heterogeneous materials.

Automatic detection of microcalcification based on morphological operations and structural similarity indices

In this paper, a new method for automatic detection of microcalcifications in digitized mammograms is proposed. Based on mathematical morphology theory to deal with the problem of low contrast between microcalcifications and their surrounding pixels, it uses various structuring elements of different sizes to reduce the sensibility to microcalcification diversity sizes. The obtained morphological results are converted to a suspicion map based on an image quality assessment metric called structural similarity index (SSIM). This continuous map is, then, locally analyzed using superpixels to automatically estimate threshold values and finally detect potential microcalcification areas. The proposed method was evaluated using the publicly-available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to state-of-the-art methods.

MMSparse: 2D partitioning of sparse matrix based on mathematical morphology

Sparse matrix is any matrix with enough zeros that it pays to take advantage of them. The computational efficiency of sparse matrix–vector multiplication (SpMV) is significantly influenced by the distribution of non-zero elements in sparse matrix, which is not fully exploited by traditional one-dimensional and two-dimensional partitioning approaches. In this paper, we present a novel two-dimensional partitioning method based on mathematical morphology theory. The matrix is partitioned into dense and sparse areas utilizing some basic morphological transformations, including dilatation, filling, opening, and skeletonization. These dense areas are classified as rectangle, triangle, and diagonal according to their morphological features. We also propose a new MMSparse (Mathematical Morphological Sparse) format that stores each type of shapes in the most efficient format instead of one format for an entire matrix. We select different types of matrices from the SuiteSparse Matrix Collection and conduct a series of experiments on NVIDIA GTX 1080Ti GPU. The experimental results show that our approach achieves average speedup 2.47x over the best performance of the cuSPARSE library kernel, 2.54x over BCSR, 1.48x over yaSpMV, 1.85x over CSR5, 1.35x over the one-dimensional partition.

Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter

Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.

Multimedia animation filtering simulation based on image extraction

With the rapid development of computer technology and network technology, a large number of audio, video, text and other multimedia information are pouring in, but only part of the information is of practical value to Internet users. Therefore, in the information explosion society, timely screening and reasonable pushing of a large number of information has become an urgent technical problem. This paper will study and analyse the research point of multimedia animation filtering in Internet environment. Based on the basic principles of image sequential edge detection algorithm and watershed image detection algorithm, and combined with mathematical morphology theory, this paper creatively proposes an improved character class edge detection and filtering algorithm. Based on the image filtering algorithm proposed in this paper, the coordination and optimization of edge detection accuracy and anti-noise ability can be achieved. Finally, simulation experiments are carried out for the above algorithms. The experimental results show that the proposed optimization algorithm has excellent edge detection effect.

Diamond tool cutting edge measurement in consideration of the dilation induced by AFM probe tip

Measurement of diamond cutting edge is of great importance in ultra-precision machining. Atomic force microscope (AFM) that takes advantage of its three-dimensional (3D) imaging in nanoscale becomes an indispensable means to acquire the 3D surface morphology of the diamond tool cutting edge. However, the dilation induced by AFM probe tip in diamond tool cutting edge measurement is a key error source in cutting edge evaluation, especially for the very sharp edge. In this paper, analysis and elimination method of this dilation effect are presented based on the principle of AFM imaging in terms of mathematical morphology theory. Our work effectively improves the evaluation accuracy of diamond tool cutting edge, which is beneficial to the diamond tool manufacture and selection in ultra-precision machining.

Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit

In order to achieve accurate identification of a shearer cutting state, infrared thermal images were creatively adopted in this paper. As the position of a shearer cutting unit is constantly changing, and the temperature in the vicinity is obviously distinct, mathematical morphology theory was used to detect the cutting unit in an infrared thermal image. Furthermore, a target tracking method is put forward to achieve cutting unit tracking based on the combination of morphology and a spatio-temporal context (STC) algorithm. Then, the temperature field features of this tracking area were extracted, and an intelligent classifier based on a support vector machine (SVM) was constructed to identify the cutting state of the shearer. Some experiments are presented, and the results indicate the feasibility and superiority of the proposed method.

Phenomenological behavior coupling hydrodynamics and electrode kinetics in a flow electrochemical reactor. Numerical analysis and experimental validation

Parallel plate electrochemical reactors are frequently used in industry because its high range of applications in several electrochemical processes related with electrosynthesis, environment electrochemistry and energy storage systems. In such sense, the aim of this work is deal with the reaction environment characterization of a parallel plate flow reactor having a net-like spacer, by the employment of original experimental and theoretical techniques in order to evaluate the influence of transport phenomena on overall reactor performance. Flow distribution is analyzed by the use of automatic measurement of flow paths using image processing techniques, based on mathematical morphology theory. On the other hand, a theoretical model of current-potential distribution under intermediate kinetics control (tertiary current-potential distribution) based on numerical solution of turbulent diffusion-convection and binary electrolyte theory is proposed. In this case, concentration at electrode surface was determined in terms of dimensionless Damkhöler number, obtained from a mass balance at electrode-electrolyte interface in steady state. RTD curves obtained by image processing techniques describes correctly the fluid dynamics behavior in electrochemical reactor. Meanwhile, correspondence between calculated and experimental polarization curves using the oxidation of chloride ions to chlorine as model electrochemical reaction was made with an error values lesser than 7%, indicating a good agreement between theoretical model and experimental tests. Results founded in the parallel plate flow electrochemical reactor used here, evidenced that the methodology proposed to analyze the hydrodynamic and electrochemical behavior are adequate to its use in other electrochemical devices, particularly, when they work under intermediate kinetic and limiting current conditions.

New colour morphological operators on hypergraph

New colour morphological operators on hypergraph are proposed to avoid the loss of details caused by fixed structure element effectively. Hypergraph, the most general structure in discrete mathematics, is also a subset of a finite set. Being a structured representation of information, the ordinary image can be transformed into a hypergraph model, which can integrate hypergraph theory with mathematical morphology theory. As hypergraphs have good performance in structuring information, first of all, this study designs a reasonable algorithm for turning colour images into hypergraph space. Then based on hypergraph theory and colour distance, new colour morphological operators on hypergraph are defined. Experiments show that using the new operator can avoid the loss of detail information and the destruction of colour topology, which improve the precision of image processing.

Short critical area model and extraction algorithm based on defect characteristics in integrated circuits

In the process of nano-electronic circuit design, there are defects inevitably. Due to the variety of defects and nets on circuits, the extraction of the critical area leading to the short circuit fault has become the bottleneck of layout optimization and of integrated circuit yield estimation. This paper proposes a new model of short critical area and extraction algorithm based on short circuit faults caused by redundant defects, combining the redundant defect features and mathematical morphology theory. Firstly, defect feature sizes and the nets ranges of the circuit are extracted. Secondly, according to the definition of short critical area and utilizing the expansion operation of mathematical morphology a new model of short critical area of regular nets is derived. Finally, extraction algorithm of short critical area is designed and implemented in accordance with the new model. The new model and extraction algorithm have the characteristics of being more generic and being independent of the shape features of defects and nets. Compared with the existing models, the new model has the similar performance however the extraction algorithm has significantly raised time efficiency. Furthermore, the extraction algorithm is more efficient than the previous algorithm which is based on mathematical morphology and non-model method. The experimental results on the real layouts of synthesized OpenSparc circuit and MUSB L70 show that the proposed method is accurate and effective.

Weak signal detection using multiscale morphology in microseismic monitoring

Microseismic events caused by hydraulic fracturing are usually very weak. The magnitude range of microseismic signals is usually from −3 to 1Mw. Processing techniques such as band-pass filtering, are widely adopted to improve the signal-to-noise (S/N) ratio of microseismic data, while with a degradation of signal quality. We propose a multi-scale morphological method to detect weak micro-seismic signals. This approach decomposes data set into multi-scale components based on the mathematical morphology theory using structuring element that is similar to the wavelet basis in the well-known wavelet decomposition. The method can help us obtain more information by detecting more waves, like P-wave, S-wave and their reflections, which can be much more valuable in processing and interpretation of microseismic data during microseismic monitoring. The proposed approach is not amplitude preserving and not mathematically reversible. It can offer enhancement of arrivals for picking (and thus can subsequently offer benefits for event detection and location) but at the expense of estimates of magnitude or moment-tensor inversion.

The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing

Bearing performance degradation assessment is a key step of condition-based maintenance. In this paper, a novel indicator of bearing performance degradation assessment is presented based on the mathematical morphology (MM) theory for higher efficiency. Because the traditional mathematical morphology particle (MMP) is unable to exactly describe the bearing's performance degradation, we extended its definition to the general space to address the limited ability of the former definition. On the basis of the innovative definition, we used the morphology erosion operation instead of the morphology open operation to calculate the general mathematical morphology particle (GMMP) of the bearing's whole life data. The analysis of the simulation and practical application demonstrated that the proposed index is feasible and effective to indicate the performance degradation of the bearing.

A method for low-frequency noise suppression based on mathematical morphology in microseismic monitoring

The frequency of microseismic data is higher than that of conventional seismic data. The range of effective frequency is usually from 100 to 500 Hz, and low-frequency noise is a common disturbance in downhole monitoring. Conventional signal analysis techniques, such as band-pass filters, have their limitation in microseismic data processing when the useful signals and noise share the same frequency band. We have developed a novel method to suppress low-frequency noise in microseismic data based on mathematical morphology theory that aims at distinguishing useful signals and noise according to their tiny differences of waveform. By choosing suitable structure elements, we have extracted low-frequency noise from a original data set. We first developed the fundamental principle of mathematical morphology and the formulation of our approach. Then, we used a synthetic data example that was composed of a Ricker wavelet and low-frequency noise to test the feasibility and performance of the proposed approach. Our results from the synthetic example indicate that the proposed approach can effectively suppress large-scale low-frequency noise while slightly decreasing the small-scale signals. Finally, we have applied the proposed approach to field microseismic data and obtained very encouraging results.

Research on Dynamic Monitoring Algorithm of Visual Safety Distance in Highway

To develop the dynamic monitoring algorithm of visual safety distance in highway, by using the highway video traffic monitoring system, the research platform of four kinds of terrain environment in plateau, mountainous area, plain and coastal area is established. Results show that through the contrast between the sample data and visibility train of thought, based on the theory of mathematical morphology, expressway visibility dynamic monitoring image information system can be established. Based on the theory of the measurement of the basic formula of visibility, the dynamic model of the optimization is established, set up 200 meters distance visual observation target system, research visual range detection algorithm process.