Mathematical Morphology Method Research Articles

Adaptive feature extraction of underwater target signal based on mathematical morphology feature enhancement

This paper proposes an adaptive feature extraction method based on mathematical morphology enhancement to extract effective and stable features under strong ocean noise. Firstly, the traditional mathematical morphology method is improved and a new mathematical morphology filtering method is proposed for feature enhancement of underwater target signals. Secondly, the artificial fish swarm algorithm (AFSA) is improved using weighted power spectral kurtosis (WPSK) to achieve parameter adaptivity. Five measured underwater target signals are used to validate the extracted method, and cosine similarity (CS), signal to noise ratio (SNR), and refined composite multiscale fluctuation dispersion entropy (RCMFDE) are used as evaluation metrics to assess the results and verify the effectiveness of the proposed method. Compared with average filtering (AVGF) and closing opening filtering (COF), the proposed method shows better capability in adaptive feature extraction. Therefore, the proposed mathematical morphological filtering can enhance underwater target features and is very important for adaptive feature extraction of underwater targets.

Tracking dynamics characteristics of tidal flats using landsat time series and Google Earth Engine cloud platform

Tidal flats serve as vital spatial resources within coastal wetlands and play crucial roles in biological habitat preservation, environmental conservation, and human development. However, accurately delineating their extent from instantaneous remote sensing images poses challenges, owing to the dynamic interplay between seawater and land. Therefore, this study utilized the consistency of remote sensing satellites and developed a multi-feature decision approach based on the long-term remote sensing datasets and the Google Earth Engine (GEE) cloud platform for determining the spatial extent, classifying types, and mapping the spatial-temporal distribution of the tidal flats. Initially, a multi-temporal image stack was compiled from the filtered remote sensing images. Subsequently, the modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) were calculated to establish the maximum and minimum water surfaces using the extreme value composite algorithm (EVCA). The Otsu algorithm and spatial overlay analysis method were then utilized to accurately delineate the spatial extent of the tidal flats. Additionally, the Normalized Difference Vegetation Index (NDVI) was utilized to accurately identify tidal flat types. Post-processing involves elevation data and mathematical morphological methods to mitigate the impact of permanent water bodies. Finally, the accuracy of the extracted spatial extent and types of tidal flats was evaluated. The efficacy and practicality of the proposed method were validated using the Zhoushan Archipelago and Landsat time series in China. The results revealed the following: (1) the acquired tidal flat information exhibited evident boundaries and accurate types, achieving an overall accuracy (OA) of 94.80 % and a Kappa coefficient of 0.89; (2) the area of tidal flats in the Zhoushan Archipelago increased from 2,182.19 ha in 1985 to 3,761.21 ha in 2020, with an average annual increase of 45.12 ha and an average annual increase rate of 1.57 %; and (3) over 35 periods, the area transformed from other land types to tidal flats amounted to 2,895.74 ha, while the area converted from tidal flats to other land types totaled 1,651.90 ha. The three islands with the most rapid growth in tidal flat area were Liuheng Island, Dachangtu Island, and Daishan Island, with changes of 675.68 ha, 380.47 ha, and 362.63 ha, respectively. This method can offer an automated, swift, and accurate approach for tidal flat extraction, demonstrating high precision and practical effectiveness, thereby providing robust technical support for coastal resource surveys.

Multi-scale flexibility assessment of electrical, thermal and gas multi-energy systems based on morphological decomposition

The rapid development of renewable energy has also had an impact on the flexibility of multi energy systems such as electricity, heat, and gas. To analyze the flexible characteristics of multi energy systems at multiple time scales, a multi-scale flexibility evaluation method based on morphological decomposition is proposed. The net load curve is decomposed using mathematical morphology methods, and a multi-scale energy storage configuration method based on the flexibility of electric heating systems is proposed. The analysis data shows that the probability of insufficient upward flexibility, margin expectation, and insufficient expectation of the scale weighted flexibility index are 1.12%, 3.98%, and 1.16%, respectively, while the probability of insufficient downward flexibility, margin expectation, and insufficient expectation are 0.73%, 4.54%, and 0.56%, respectively. The introduction of energy storage and controllable load simultaneously results in an overall downward flexibility index of 0.92% for the system. The results indicate that controllable load can improve the economy of system peak shaving, providing more options for energy storage and configuration in multi energy systems.

A de-noising algorithm for bridge cable force monitoring data based on mathematical morphology

A mathematical morphological filter-based de-noising method is developed in this study for bridge cable force monitoring data. Structure elements, one of the most important parameters in the mathematical morphology, dominate de-noising effects. The de-noising effects subject to single structure element and multi-structure element filters are discussed based on the simulation signals. The results indicate that the de-noising effects by using the spherical structure element are better than using the straight line or rhombic structure element. Moreover, the multi-structure element filter outperforms the single one. Through simulation analysis, the de-noising performance of the low-pass filter, wavelet filter and morphological filter is compared. The results show that the performance of the wavelet and morphological filters is better than that of the low-pass filter. For low signal-to-noise signals, the performance of the wavelet filter is superior. With the increase of signal-to-noise ratio, the morphological filters show more advantages. Taking the cable force monitoring data of the 3rd Nanjing Yangtze River Bridge as an example, the de-noising performance of the wavelet and morphological filters is discussed. The results show that both the wavelet filters and morphological filters have satisfactory de-noising effects. The mathematical morphology method can provide an optional and effective de-nosing choice, which enriches the means of de-noising for bridge monitoring data.

An informative teaching model of English-Chinese translation in universities based on the perceptual machine model

Abstract With the development of globalization, there is an increasing demand for English-Chinese translation talents in the country and society. In this paper, a text classifier based on multilayer perceptron is proposed, an MLP structure containing two layers of feedforward neural network is designed for the characteristics of English-Chinese translation corpus, the training samples are selected with text height mainly below 60 pixels, and the best sliding window of 13×13 is selected by comparison, supplemented by the smoothing method of mathematical morphology. The constructed MLP-based information-based teaching model is then applied to teaching English-Chinese translation in colleges and universities and is mainly evaluated in terms of student evaluation and teaching effectiveness. There were 28.48 percentage points more English-Chinese translation students who thought the class was more interesting than the original, while 20.56% and 20.50% more students evaluated the class as more helpful and less helpful, respectively. The average score of English-Chinese translation students increased by 15.88% compared to the original one through the comparison test between traditional and informational teaching modes. The MLP-based informatization teaching can mobilize students’ enthusiasm to participate in English-Chinese translation learning, strengthen the guidance of translation methods, and improve students’ translation levels.

EVALUATION OF NATURAL RISK CHANGES WITHIN EROSIONAL THERMOKARST PLAINS UNDER THE IMPACT OF CLIMATIC TRENDS

The relevance of assessing risk to engineering structures from geohazards is determined by changing natural conditions in the permafrost zone. Risk assessment by statistical methods directly is accompanied by certain difficulties. Accumulation of statistics requires a significant amount of time, and this time is often comparable to the time of engineering structure operation. The most difficult stage of risk assessment is the assessment of the probability of damage to engineering structures caused by a hazardous geological process. The aim of the study was to substantiate the method for solving this problem on the territory of thermokarst plains with fluvial erosion for linear engineering structures using the methods of mathematical landscape morphology. Mathematical landscape morphology allows us to proceed to the quantitative risk assessment. The first step of applying methods of mathematical landscape morphology is to divide the area into homogenous by the process development sections. The method is based on the model that was proposed by the authors for the development of the morphological structure of thermokarst plains with fluvial erosion under conditions of an asynchronous start, which is realized according to empirical verification in most of cases. The model of risk assessment was applied to 4 key areas located under different physico-geographical conditions, but having morphological homogeneity. Using satellite images of different periods, the delineation of thermokarst lakes and khasyreys was carried out. The average density of the number of lesions in different areas varies from 0.00026 year–1 to 0.00104 year–1, and there is no clear trend of change with time. According to quantitative experimental data, there has been a site-differentiated change in the risk of damage to linear structures within the thermokarst plains with fluvial erosion over the past few decades (46–56 years). The probability of damage to linear engineering structures has changed significantly in plot 35 (Yano-Indigirskaya lowland), which confirms the chi-square test. The probability of defeat increased by 6–9 times according to the estimates obtained. This situation is observed when the lengths of linear structures are 3–4 km. However, in most of the key areas, there is no significant change in the probability of damage, despite ongoing climate change.

An adaptive feature extraction technique for ship-radiated noise based on weighted multiscale mathematical morphological filtering

Aiming at the problem of feature extraction of ship-radiated noise in complex marine environment, an adaptive weighted multiscale mathematical morphological filtering method is proposed. The weight coefficients of the structure elements of different scales in multiscale morphological filtering are determined adaptively by an improved particle swarm optimization algorithm, and the Teager energy kurtosis is used as the evaluation index of the filtered optimal signal to provide optimized weights for each scale. Finally, the optimized multiscale mathematical morphology filter with selective adaptive weights is bound and applied to the feature extraction of ship-radiated noise. The analysis results of simulated signal and measured ship-radiated noise show that this method has strong noise suppression and feature extraction ability, and the calculation is simple and fast, which provides an effective method for ship feature extraction.

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-to-day lives is to interpret text from documents. In this context, to help these people, the objective of this work is to develop an efficient text recognition system that allows the isolation, the extraction, and the recognition of text in the case of documents having a textured background, a degraded aspect of colors, and of poor quality, and to synthesize it into speech. This system basically consists of three algorithms: a text localization and detection algorithm based on mathematical morphology method (MMM); a text extraction algorithm based on the gamma correction method (GCM); and an optical character recognition (OCR) algorithm for text recognition. A detailed complexity study of the different blocks of this text recognition system has been realized. Following this study, an acceleration of the GCM algorithm (AGCM) is proposed. The AGCM algorithm has reduced the complexity in the text recognition system by 70% and kept the same quality of text recognition as that of the original method. To assist visually impaired persons, a graphical interface of the entire text recognition chain has been developed, allowing the capture of images from a camera, rapid and intuitive visualization of the recognized text from this image, and text-to-speech synthesis. Our text recognition system provides an improvement of 6.8% for the recognition rate and 7.6% for the F-measure relative to GCM and AGCM algorithms.

Experimental and Numerical Research on Water Transport during Adsorption and Desorption in Cement-Based Materials

The durability of cement-based materials is related to water transport and storage in their pore network under different humidity conditions. To understand the mechanism and characteristics of water adsorption and desorption processes from the microscopic scale, this study introduces different points of view for the pore space model generation and numerical simulation of water transport by considering the “ink-bottle” effect. On the basis of the pore structure parameters (i.e., pore size distribution and porosity) of cement paste and mortar with water-binder ratios of 0.3, 0.4 and 0.5 obtained via mercury intrusion porosimetry, randomly formed 3D pore space models are generated using two-phase transformation on Gaussian random fields and verified via image analysis method of mathematical morphology. Considering the Kelvin-Laplace equation and the influence of “ink-bottle” pores, two numerical calculation scenarios based on mathematical morphology are proposed and applied to the generated model to simulate the adsorption-desorption process. The simulated adsorption and desorption curves are close to those of the experiment, verifying the effectiveness of the developed model and methods. The obtained results characterize water transport in cement-based materials during the variation of relative humidity and further explain the hysteresis effect due to “ink-bottle” pores from the microscopic scale.

Metode Otsu dan Mathematical Morphology Dalam Segmentasi Region Karakter Plat Nomor Kendaraan

The problem that affects the character segmentation step is the step before character segmentation, namely preprocessing character segmentation or called preprocessing. This poses are strongly influenced by plate lighting conditions, shadows against plates, plate image impurities, plate image resolution, character cutting accuracy, and speed in recognizing characters. In general, identification consists of 3 stages, namely detection, segmentation and recognition. In this study, the use of the otsu method is expected to detect the region on the vehicle license plate, the region in question is the first region to show the regional code, the second region for the registration number and the third region for the sub-region code. In the process, the results of the vehicle number plate detection trial to get a segmentation of 3 regions of the vehicle number plate character did not get the expected results. The results trial obtained the identification of the entire character of the vehicle number plate so that the characters on the vehicle number plate could not be distinguished between the front letter, number, and the back letter. So to maximize the desired results so that getting 3 regions of the otsu method segmentation process needs to be improved using the mathematical morphology method. This mathematical morphology method serves to read the character value of each pixel in the digital image which produces a comparison between the pixels in the image, so morphology techniques are appropriate when used to perform image processing in obtaining the region of the vehicle number plate. From the improvement of the otsu method, the results of the trial were improved. Of the 100 data samples tested, 96 data samples passed and 4 sample data failed, the accuracy value using MSE measurements from the tested data samples received a very high increase, which was 96%.

Optimization of automatic extraction procedure for particles in asphalt mixture towards superior robustness and accuracy

Three-dimensional image reconstruction plays a pivotal role in microstructure information extraction and virtual mechanical experiments of asphalt mixture. However, the traditional pixel-based reconstruction algorithm tends to be powerless in processing three-dimensional (3-D) mesostructure. Therefore, this paper aims to develop a robust and accurate algorithm for high-quality aggregate particle extraction. To this end, the global thresholding and three-dimensional mathematical morphological methods were conducted directly based on voxel operation. Then the calculated weight-based gradation was utilized to develop the robustness and accuracy analysis map (RAAM), which assesses and compares the traditional and novel reconstruction algorithms. As a sequence, the voxel-based method had higher accuracy and lower perturbation sensitivity than the existing procedures. Additionally, the computed efficiency was improved dramatically by breaking tight bounds and conservatism. Finally, the inevitable algorithm defects caused by multiple morphological treatments in the traditional method have disappeared in the novel one.

Detecting the Sun’s active region using image processing techniques

The term ‘space weather’ refers to adverse conditions on the Sun that could affect the structures of spaceor terrestrial technology and risk human health or life. Flares produce effects that span the electromagneticspectrum. During a flash, they emit x-rays and ultraviolet radiation, resulting in extremely high temperatures.The purpose of this work is to create a new algorithm for the identification of solar flares and activeregions of the Sun, thus minimizing harm to spacecraft operations in orbit. An algorithm was developedfor the automatic detection of active regions of the Sun (sunspots, solar flares), based on methods of imageenhancement, segmentation, pattern recognition and mathematical morphology. The sun’s surface alsodisplays visible sunspots located in regions of the Sun that are magnetically active and whose number isan indication of the Sun’s magnetic activity. In tracking and predicting solar activity, the identification andclassification of sunspots are useful techniques. The main objective of this paper is to detect sunspots usingimages from the Solar Dynamics Observatory. Key words: solar flares, sunspots, active region.

Generalized Mathematical Morphological Method for 3D Shape Analysis of Geological Boundaries: Application in Identifying Mineralization-Associated Shape Features

Generalized Mathematical Morphological Method for 3D Shape Analysis of Geological Boundaries: Application in Identifying Mineralization-Associated Shape Features

Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice floes is identified using a mathematical morphology method. Hausdorff distance is used to measure the similarity of segmented ice floes. It is tolerant to perturbations and deficiencies of floe shapes, which enhances the density of retrieved sea ice motion vectors. The PHD algorithm can be applied to sequential images from different sensors, and was tested on two combined image mosaics consisting of Sentinel-1 and Sentinel-2 data acquired over the Fram Strait; the PHD algorithm successfully produced pairs of matched ice floes. The matching result has been verified using shape and surface texture similarity of the ice floes. Moreover, the present method can naturally be extended to the problem of multi-source sea ice image registration.

Extraction of Text From Image

Picture Text is the content data implanted or written in picture of various structure. Picture text can be found in caught pictures, filtered records, magazines, papers, banners and so on These picture messages are profoundly accessible these days and they are vital in addressing, depicting and moving data which help people groups in correspondence, tackling issues, accessibility, formation of new sorts of occupations, cost viability, efficiency, globalization and social hole and so forth The data from these picture archives would give higher proficiency and straightforward entry on the off chance that it is changed over to message structure. The cycle by which Image Text changed over into plain content is Text Extraction. Text Extraction is helpful in data recovering, looking, altering, recording, filing or detailing of picture text. In any case, variety of these writings because of contrasts in size, direction style, and arrangement, text is installed in complex hued archive pictures, corrupted reports picture, inferior quality picture, as well as low picture differentiation and complex foundation make issue text extraction incredibly troublesome what's more, testing one. Various strategies like Connected Component Method, Mathematical Morphology Method, Edged Based Method and Texture Based Method have been utilized beforehand, however those all have their own constraints when estimated by various boundaries like exactness, review and f- score. In this paper, text extraction from picture reports, utilizing blend of the two amazing techniques Connected Component and Edge Based Method, to improve execution and exactness of text extraction is talked about and execution is finished by incorporated MATLAB code with MATLAB/Simulink device and the proposed framework is tried by Digital Image Binarization Competition (DIBCO) 2017 dataset. At long last, the separated and perceived is changed over to discourse for legitimate use for outwardly hindered individuals.

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic spline interpolation is proposed for O-ring dimension measurement. After image pre-processing of the O-ring graphics, the whole-pixel-level O-ring edges are obtained by using a noise-resistant mathematical morphology method, and then the sub-pixel edge contours are obtained using a sub-pixel edge detection algorithm based on cubic spline interpolation. Finally, the edge curve is fitted with the least squares method to obtain its inner and outer diameter as well as the size of the wire diameter. The experimental data show that the algorithm has a mean square error of 4.8 μm for the outer diameter and 0.18 μm for the wire diameter. The outer diameter error is kept within ±100 μm and the wire diameter error can be kept within ±15 μm. Compared with the whole pixel algorithm, the measurement accuracy has been greatly improved.

Breast Mass Detection in Mammography Based on Image Template Matching and CNN.

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method.

Whole cardiac segmentation in chest CT images is important to identify functional abnormalities that occur in cardiovascular diseases, such as coronary artery disease (CAD) detection. However, manual efforts are time-consuming and labor intensive. Additionally, labeling the ground truth for cardiac segmentation requires the extensive manual annotation of images by the radiologist. Due to the difficulty in obtaining the annotated data and the required expertise as an annotator, an unsupervised approach is proposed. In this paper, we introduce a semantic whole-heart segmentation combining K-Means clustering as a threshold criterion of the mean-thresholding method and mathematical morphology method as a threshold shifting enhancer. The experiment was conducted on 500 subjects in two cases: (1) 56 slices per volume containing full heart scans, and (2) 30 slices per volume containing about half of the top of heart scans before the liver appears. In both cases, the results showed an average silhouette score of the K-Means method of 0.4130. Additionally, the experiment on 56 slices per volume achieved an overall accuracy (OA) and mean intersection over union (mIoU) of 34.90% and 41.26%, respectively, while the performance for the first 30 slices per volume achieved an OA and mIoU of 55.10% and 71.46%, respectively.

Research on current harmonic suppression of PMSM based on mathematical morphology

Due to the existence of power converter dead time, flux harmonics and other nonlinear factors, the motor phase current contains a large number of high-order harmonics, so the electromagnetic torque generated by the interaction of current and flux also contains various orders of harmonics. In order to eliminate current harmonics, mathematical morphology filter is proposed to suppress current harmonics. However, the single-scale morphological filtering has shortcomings in preserving information details. In order to make up for the deficiency of the traditional single-scale morphological filtering in preserving information details, a weighted multi-scale mathematical morphology method is proposed to suppress the current harmonics.

Application of morphological processing methods in computer stereo vision

Computer vision algorithms are important for many areas of human activity. In particular, the number of applications related to the need to process images of real-world objects with computerized tools and the subsequent use of descriptive information in a variety of interactive and automated decision-making systems is increased. An important tool for analyzing real-world scenes are approaches to the application of stereo vision algorithms. The important step of many stereo matching algorithms is a disparity map. Depending on the content of the observed scene, part of the values on the disparity map can be immediately attributed to background values on a certain basis, or form a "natural" background, which is characterized by loss of informative data due to unacceptable error of subsequent resultant distance values. The calculated disparity map of any algorithm may contain some shortcomings in the form of discontinuities of continuous information areas caused by the complexity of shooting conditions, the impact of noise of various natures, hardware imperfections, and so on. An approach to mitigating the undesirable influence of negative factors on the resulting disparity is the use of mathematical morphology operations to process disparity maps at the post-processing stage. This paper presents information technology for increasing the content of disparity maps based on the mathematical morphology methods. The technology is based on a combination of morphological operations of erosion and dilation, which eliminates the typical problems of discontinuities of monotone regions and erroneous values on disparity maps. The proposed approach allows reducing the impact of common problems that arise during the operation of stereo matching algorithms, as well as increase the overall informativeness of disparity maps for images of real objects in the absence of partial or complete initial data on the characteristics of the observed scene. The results of testing morphological operations with disparity maps for real objects allow us to conclude about the possibility of partial restoration of areas of disparity maps with gaps in continuous information areas, as well as to reduce the impact of random anomalous values on the overall content of the disparity maps.