Global Threshold Segmentation Research Articles

Remote sensing dynamic monitoring of the flood season area of Poyang Lake over the past two decades

Drought and flood disasters occur frequently in the Poyang Lake basin during the flood season, making it significantly important to monitor the lake's flood season area using time-series remote sensing. This research employed multisource remote sensing datasets from Landsat, ALOS, and Sentinel-1 across the time span of 2000–2022. Using the MNDWI method and adaptive global threshold segmentation, the flood season area of Poyang Lake was extracted. The spatiotemporal variation characteristics of the flood season area were analysed, and correlations with precipitation and temperature were assessed. Moreover, this study explored the characterization of its response to drought and flood events. The results revealed a strong fluctuation in the lake during the flood season, with the maximum and minimum area differences exceeding 3000 ​km2, and spatial changes were mainly concentrated in the southwestern lake region. There is a significant positive correlation between area changes and precipitation and a significant negative correlation with temperature. By analysing the response characteristics of the flood season area changes to drought and flood events, in years when the flood season area of Poyang Lake exceeds 4500 ​km2, extreme flood disasters usually occur. Areas between 3900 ​km2 and 4500 ​km2 are prone to floods, areas between 2000 ​km2 and 3000 ​km2 are prone to drought events, and areas below 2000 ​km2 typically experience extreme drought disasters.

Brain tumor detection model based on CNN and threshold segmentation

Brain tumours pose a substantial global health issue, emphasising the criticality of timely and precise identification to ensure optimal treatment outcomes. This research introduces an innovative methodology for the identification of brain tumours by employing a fusion of Convolutional Neural Networks (CNNs) with threshold segmentation techniques. The objective of the suggested model is to improve the precision and effectiveness of brain tumour identification in medical imaging, namely Magnetic Resonance Imaging (MRI) scans. Fast and precise diagnosis is necessary in the medical profession for effective treatment, but current technologies lack this capability. For successful therapy, it is therefore necessary to develop an effective diagnosis application. Global threshold segmentation for pre-processing is used in this study. Image capture and de-noising were completed in the first stage, while classification and regression were completed in the second stage using ML approaches. A computer-aided automated identification method is the computational technique used in this study. This investigation uses one hundred twenty (120) brain scans from a real-time MRI brain database, of them 15 normal and 105 abnormal. According to performance metrics, the accuracy of training and testing pictures was 99.46%. Comparing this method to recently published methods, it is determined that LR-ML with the threshold segmentation has a rapid, precise brain diagnostic system.

Research on Crop 3D Model Reconstruction Based on RGB-D Binocular Vision

Taking maize seedlings as the object, the implementation of crops 3D reconstruction based on RGB-D binocular vision and the selection of some key parameters are investigated in this research. First, multiple images are taken from different angles around the target. By mapping the maize seedling region coordinate values after the Otsu algorithm and global threshold segmentation to the corresponding depth image, the depth data of the maize seedling region can be obtained accurately. An improved mean filter is proposed to adaptively fill the holes in the depth image. Then, the different point clouds with the fixed step angle of the maize seedling are registered and fuzed. Finally, after the fusion point cloud is simplified, the 3D model of crops can be reconstructed. Experimental results show that the simplification effect of the octree algorithm is better than that of the voxel grid filter. Among all the step angles, the reconstruction error of the step angle with 60° is the smallest. Under this condition, the height error between the model and the maize seedling is 2.22%, and the error in stem diameter is 11.67%.

A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block

As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in the extraction of water bodies. However, there is a great deal of speckle noise in SAR images, which seriously affect the extraction accuracy of water. At present, most of the processing methods are filtering methods, which will cause the loss of detailed information. Based on the characteristic of side-looking SAR, this paper proposed a self-adaptive thresholding approach for automatic water extraction based on an OTSU algorithm and distance block. In this method, the whole images were firstly divided into uniform image blocks through a distance layer which was produced by the distance to the orbit. Then, a self-adaptive processing was conducted for merging blocks. The OTSU algorithm was used to obtain a threshold for classification and the Jeffries–Matusita (JM) distance was calculated with the classification result. The merge processing continued until the separability of image blocks reached the maximum. Subsequently, we started from the next block to repeat the merger, and so on until all blocks were processed. Ten study areas around the world and the local Dongting Lake area were applied to test the feasibility of the proposed method. In comparison with five other global threshold segmentation algorithms such as the traditional OTSU, MOMENTS, MEAN, ISODATA and MINERROR, the proposed method obtains the highest overall accuracy (OA) and kappa coefficient (KC), while this approach also demonstrates greater robustness in the analysis of time series. The findings of this study offer an effective method to improve water detection accuracy as well as reducing the influence of speckle noise and retaining details in the image.

Automatic batch recognition of rock deformation areas based on image segmentation methods

Image recording and analysis is an important but time-consuming method for understanding the rock mechanics mechanism. In this paper, a method for automatic batch identification of rock deformation areas is proposed. We crop the original image to remove irrelevant background. And we use adaptive threshold segmentation, region growth segmentation and global threshold segmentation and combine the characteristics of the image to identify the rock deformation area. Finally, we use image morphology processing to make the recognition result more accurate. For validation, 359 images of the rock samples of the uniaxial compression test were quickly identified. The identification time was approximately 5′56.83′. The average relative error of the method in the X and Y directions is 10.88% and 8.60%, respectively. In addition, using the identification results and the stress-strain curve, it was found that the water content and initial crack length of rock increase, and the compressive strength decreases; the effect of the initial fracture inclination on the compressive strength of the rock is not obvious.

Evaluating reclaimed asphalt mixture homogeneity using force chain transferring stress efficiency

Numerous studies have demonstrated a direct link between the homogeneity of asphalt mixture and the rutting performance, water stability, tensile strength, and service life of the asphalt pavement. Due to the existence of RAP clusters, poor homogeneity of the recycled asphalt mixtures is more severe than natural ones. However, previous studies have mainly focused on analyzing the distribution of aggregates, ignoring the transfer of the internal stress in the mixture. This paper proposed a novel approach for the analysis of the homogeneity of recycled asphalt mixture using the efficiency of force chain transferring stress. Therefore, to prepare gyratory compacted samples, diabase in green color was used as the virgin aggregate and marble in white color as the RAP aggregate. Moreover, to obtain the vertical section images, the samples were cut and processed by digital image processing (DIP). This included distinguishing the virgin and RAP aggregates by global gray threshold segmentation, adding asphalt film around the aggregates, and constructing a force chain network relied on the aggregate contact information. Also, the intensity of the force chain was determined using the finite element method (FEM). In addition, the efficiency of force chain transferring vertical stress (ETVS) was introduced and the homogeneity index D of the recycled asphalt mixture was proposed based on the variation coefficient of ETVS. Furthermore, uniaxial compression tests were carried out to mathematically analyze the homogeneity index D proposed in this paper and the homogeneity index D′ proposed based on the aggregate area ratio in previous studies. The obtained result showed that the homogeneity index D increased first and then decreased with the increase of RAP content. Moreover, the correlation analysis demonstrated the compressive performance of the mixture to be mainly reflected by the homogeneity index D. The correlation coefficients of the compressive strength and the compressive resilient modulus were found to be 0.9010 and 0.8464, respectively. Finally, the homogeneity index D′ was observed to mainly reflect the stability of the compressive performance of the mixture, with the correlation coefficients of the variation coefficient of the compressive strength and the compressive resilient modulus of 0.8745 and 0.8869, respectively.

Photometric Stereo-Based 3D Reconstruction Method for the Objective Evaluation of Fabric Pilling

Fabric pilling evaluation has been considered as an essential element for textile quality inspection. Traditional manual method is still based on human eyes and brain, which is subjective with low efficiency. This paper proposes an objective evaluation method based on semi-calibrated near-light Photometric Stereo (PS). Fabric images are digitalized by self-developed image acquisition system. The 3D depth information of each point could be obtained by PS algorithm and then mapped to 2D grayscale image. After that, the non-textured image could be filtered by using the Gaussian low-pass filter. The pilling segmentation is conducted by using global iterative threshold segmentation method, and then K-Nearest Neighbor (KNN) is finally selected as a tool for the grade classification of fabric pilling. Our experimental results show that the proposed evaluation system could achieve excellent judging performance for the objective pilling evaluation.

Study on the Extraction of CT Images with Non-Uniform Illumination for the Microstructure of Asphalt Mixture.

An adaptive image-processing method for CT images of asphalt mixture is proposed in this paper. Different methods are compared according to the error analysis calculated between the real gradation and 3D reconstruction gradation. As revealed by the test results, the adaptive image-processing method was effective in carrying out different brightness homogenization processes for each image. The Wiener filter with 7 × 7 size filter was able to produce a better noise reduction effect without compromising image sharpness. Among the three methods, the adaptive image-processing method performed best in the accuracy of coarse aggregate recognition, followed by the ring division method and the global threshold segmentation method. The error of the gradation extracted by the adaptive image-processing method was found to be lowest compared with the real gradation. For a variety of engineering applications, the developed method helps to improve the analysis of CT images of asphalt mixtures.

Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete

In recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which directly reflect the stress of the lining, which is very important for the study of tunnel diseases. In view of the current detection status and detection requirements, this article has carried out research work on a vehicle-mounted tunnel lining crack detection system based on image processing. Due to the grayscale difference between the cracks on the lining surface and the lining background, these differences lead to significant crack edge features and relatively stable detection. Therefore, this article designs an intelligent edge algorithm system for cracks on the lining surface to detect the edges of the image, extract the edges of cracks, and remove useless interference information in the lining background. The experiment proves that the paired sample t-test can find that after the experiment is over, the P value of different edge detection operators for global threshold segmentation is less than 0.05, which has a significant difference. The Canny, Deriche, and Lanser filters are relatively strong, and the extracted crack edge noise is relatively small. Finally, the parameter values of the crack image are calculated, and the calculated values of the crack parameters provide a scientific and reliable basis for tunnel safety evaluation.

Research on water seepage detection technology of tunnel asphalt pavement based on deep learning and digital image processing

To improve the safety of road tunnel pavement, the research established road tunnel pavement water seepage recognition models based on deep learning technology, and a water seepage area extraction model based on image processing technology to finally achieve accurate detection of water seepage on tunnel pavements. First, the deep learning models EfficientNet water seepage recognition model and MobileNet water seepage recognition model were built, the models were trained with the self-collected pavement seepage data set, and the F1 score was introduced to evaluate the accuracy and comprehensive performance of the two models in predicting different categories of water seepage characteristics. Then three grayscale processing methods, the cvtColor function, mean method and maximum method, six global threshold segmentation methods, Otsu thresholding method, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO and THRESH_TOZERO_INV, three filtering methods, namely Gaussian filtering, median filtering and morphological open operation, as well as small connected domain removal, were used to reduce the noise of the images. Finally, the seepage area image calculation method was proposed based on the processed images to predict the actual pavement seepage area. The results show that the recognition accuracy of the EfficientNet water seepage recognition model is 99.85% and 97.53% in the training and validation sets respectively, which is 2.85% and 0.76% higher than the 97% and 96.77% of the MobileNet model. The average F1 score of the EfficientNet model is 95.22%, which is 5.05% higher than that of the MobileNet model, for the four types of seepage feature images: point seepage, line seepage, surface seepage and no seepage. The cvtColor function for grayscale processing, THRESH_BINARY for threshold segmentation and a combination of median filtering and morphological open operation for image noise reduction can effectively extract the seepage features. The area calculation is performed by the seepage area image calculation method, and the average error between the predicted value and the actual seepage area is 8.30%, which can better achieve the accurate extraction of the seepage area.

High-resolution X-ray computed tomography simulations of synthetically-generated volume porosity in continuous carbon fibre-reinforced polymer samples

ABSTRACT The fast and correct determination of porosity in carbon fibre–reinforced polymers and other polymeric material is an important application area of X-ray computed tomography (CT). In this CT simulation study, microstructures such as individual carbon fibres, micro-voids, as well as the polymer matrix including mesoscale voids, were taken into account to generate CT data synthetically with well-known porosity. It was found that the ratio of total surface area (SA) of the voids divided by the total volume (V) of the voids is suitable to describe and differentiate individual porosity samples. Our investigations revealed that the ratio SA/V can be used to estimate a minimal necessary voxel size for proper porosity segmentation by a simple ISO50 threshold. Under certain conditions, using an adapted ISOxx threshold value at a resolution of (10 µm)3 voxel size, the porosity in a specimen can be determined with an average measurement error below 10%. As long as the CT resolution is not high enough to completely resolve all void structures, using global threshold segmentation is always a compromise between over-segmentation of the macro-voids and under-segmentation of micro-voids.

On Acquisition Parameters and Processing Techniques for Interparticle Contact Detection in Granular Packings Using Synchrotron Computed Tomography.

X-ray computed tomography (XCT) is regularly employed in geomechanics to non-destructively measure the solid and pore fractions of soil and rock from reconstructed 3D images. With the increasing availability of high-resolution XCT imaging systems, researchers now seek to measure microfabric parameters such as the number and area of interparticle contacts, which can then be used to inform soil behaviour modelling techniques. However, recent research has evidenced that conventional image processing methods consistently overestimate the number and area of interparticle contacts, mainly due to acquisition-driven image artefacts. The present study seeks to address this issue by systematically assessing the role of XCT acquisition parameters in the accurate detection of interparticle contacts. To this end, synchrotron XCT has been applied to a hexagonal close-packed arrangement of glass pellets with and without a prescribed separation between lattice layers. Different values for the number of projections, exposure time, and rotation range have been evaluated. Conventional global grey value thresholding and novel U-Net segmentation methods have been assessed, followed by local refinements at the presumptive contacts, as per recently proposed contact detection routines. The effect of the different acquisition set-ups and segmentation techniques on contact detection performance is presented and discussed, and optimised workflows are proposed.

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.

An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component decomposition and multi-scale thresholding segmentation. Firstly, the polarimetric characteristics of the port water are analyzed using modified three-component decomposition. Secondly, the volume scattering power and the power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are used to extract the port water. Water and land are first separated by a global thresholding segmentation of the volume scattering power, in which the sampling region used for the threshold calculation is automatically selected by a proposed homogeneity measure. The interference water regions in the ports are then separated from the water by segmenting the PRDV using the multi-scale thresholding segmentation method. The regions of interest (ROIs) of the ports are then extracted by determining the connected interference water regions with a large area. Finally, ports are recognized by examining the area ratio of strong scattering pixels to the land in the extracted ROIs. Seven single quad-polarization SAR images acquired by RADARSAT-2 covering the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America are used to test the proposed method. The experimental results show that all ports are correctly and quickly detected. The false alarm rates are zero, the intersection of union section (IoU) indexes between the detected port and the ground truth can reach 75%, and the average processing time can be less than 100 s.

Small-Sized Ship Detection Nearshore Based on Lightweight Active Learning Model with a Small Number of Labeled Data for SAR Imagery

Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can achieve a much better detection performance than traditional methods, it is difficult to achieve satisfying performance for small-sized ships nearshore due to the weak scattering caused by their material and simple structure. Another difficulty is that a huge amount of data needs to be manually labeled to obtain a reliable CNN model. Manual labeling each datum not only takes too much time but also requires a high degree of professional knowledge. In addition, the land and island with high backscattering often cause high false alarms for ship detection in the nearshore area. In this study, a novel method based on candidate target detection, boundary box optimization, and convolutional neural network (CNN) embedded with active learning strategy is proposed to improve the accuracy and efficiency of ship detection in nearshore areas. The candidate target detection results are obtained by global threshold segmentation. Then, the strategy of boundary box optimization is defined and applied to reduce the noise and false alarms caused by island and land targets as well as by sidelobe interference. Finally, a lightweight CNN embedded with active learning scheme is used to classify the ships using only a small labeled training set. Experimental results show that the performance of the proposed method for small-sized ship detection can achieve 97.78% accuracy and 0.96 F1-score with Sentinel-1 images in complex nearshore areas.

Stellar map centroid positioning based on dark channel denoising and feasibility of jitter detection on ZiYuan3 satellite platform

Stellar map denoising and centroid positioning, which directly determine the postpositioning accuracy of star trackers, are key technologies in stellar map processing. Due to the influence of a complex starry sky background, there is often a large amount of noise in stellar maps, which makes it difficult to accurately locate the stellar centroid. A stellar map processing method based on dark channel denoising and continuous multiframe stellar map centroid positioning combined with centroid trajectory constraints is proposed. First, a dark channel noise template is used for denoising, and the single-point and multipoint noises in the denoising result are filtered. Second, in the process of stellar map positioning, if the maximum gray value of the stellar is not unique, it is constrained by the previous stellar positioning result, an adaptive window is established, and the gray-scale centroid weighting method is used to locate the centroid. Then, the star angular distance is used to analyze the precision of the centroid positioning. Finally, the jitter frequency of the satellite platform based on the continuous multiframe centroid positioning result is used to detect satellite attitude. The experimental results show that the performance of dark channel denoising, which can solve many strip noise and background noise problems in stellar maps, is better than that of the existing stellar map denoising method. The centroid positioning results improves the star angular distance by 18.85 arc sec compared with the Gaussian filter and by 8.03 arc sec compared with the global threshold segmentation method, significantly improving the accuracy of stellar map centroid positioning and laying a foundation for improving the star tracker positioning accuracy. A jitter frequency of 0.67 Hz was detected on the ZiYuan3 satellite platform based on the centroid positioning results. It enriches the jitter detection methods and provides a theoretical and technical basis for the design of future high-resolution ground observation remote sensing satellite platforms and geometric accuracy compensation.

Development of Integrated Multi-Station System to Precisely Detect and Mitigate Surface Damage on Fused Silica Optics

In high power laser facility, irreversible damage on fused silica optics, induced by laser irradiation or processing, seriously affects the service life of optics. Therefore, the work of inhibiting damage growth has been carried out in various countries. In our work, an integrated multi-station system is designed to detect and mitigate surface damage on fused silica. The process of processing fused silica optics include UV laser conditioning, surface damage detection and surface damage mitigation with CO2 laser. UV laser conditioning pre-initiates surface damage on fused silica optics with the laser flux less than Laser-Induced Damage Threshold (LIDT). Images of surface damage acquired from camera are processed by improved global threshold segmentation algorithm to extract damage information. Finally, CO2 laser is applied to process the damage with specific morphology to enhance the laser damage resistance. This integrated multi-station system saves the repeated optics installation time between the workstations with the positioning accuracy of 20 μm. Furthermore, the damage with diameter of 10 μm is mitigated to prolong service life of processed fused silica optics. The efficient and accurate integrated multi-station system is of great significance for off-line detecting and mitigating surface damage of fused silica optics in high power laser facility.

Detection Algorithm of Porosity Defect on Surface of Micro-precision Glass Encapsulated Electrical Connectors

A miniature precision glass encapsulated electrical connectors introduced by glass powder and metal wires through a special complicated process. Aiming at the porosity defects on the surface, a defect detection algorithm propose based on threshold segmentation and feature extraction. Pre-operation, global threshold segmentation processing and feature extraction (based on area, circularity aspect ratio, compactness, and contour length) are preformed to detect the defects. Experimental results show that the algorithm can accurately identify porosities defects.

Improved skin lesions detection using color space and artificial intelligence techniques

Background: Automatic skin lesion image identification is of utmost importance to develop a fully automatized computer-aided skin analysis system. This will be helping the medical practitioners to provide skin lesions disease treatment more efficiently and effectively.Material and method: In this article, two image processing techniques for accurate detection of skin lesions have been proposed. In first technique, the optimization of edge detection has been carried out by using a branch of artificial intelligence called nature inspired algorithm. Ant colony optimization (ACO) is used to increase effectiveness of edge detection in skin lesion. The second technique deals with the color space-based split-and-merge process in combination with global thresholding segmentation and edge smoothing operations.Result: The performance of both techniques has been measured by entropy performance evaluation parameter. The results show remarkable improvement in output images obtained by Canny edge detection technique optimized by ACO in comparison with ACO-Sobel, ACO-Prewitt and Edge Smoothing-Color Space techniques.Conclusion: ACO-Canny Edge detection technique shows far better effieciency for skin lesion detection as compared to ACO-Sobel, ACO-Prewitt and Edge Smoothing Color Space technique.

Characterization of Aluminium Oxide Nanoporous Images using different Segmentation Techniques

The characterizations of the nanoporous membrane require appropriate segmentation of the pores. The segmentation technique used for one category or class of image may not be suitably applied to the other class of images. Selecting the ubiquitous segmentation technique that can be applied to all image types is a challenging issue. In spite of several decades of research, there is no ubiquitously accepted method for image segmentation and therefore it remains a challenge in digital image processing. The objective of the present study is to automate the system with five different segmentation techniques; global thresholding, active contour, K-means, region growing and watershed to extort the pore from the experimental Al2O3 FESEM images, synthesized using different anodizing parameters. The geometrical features; nanopore wall thickness, pore size and porosity are computed for all the experimental images with the proposed segmentation techniques and are compared with the manual results. The variation in the proposed and manual results, referred to as error is computed. It is observed that the average error for the segmented pore characteristics using global thresholding, active contour, K-means, region growing and watershed are 7%, 14%, 13%, 18% and 20% respectively. The analysis predicts, error in using global thresholding segmentation technique is least, as compared to other methods and thus, it is considered the most appropriate segmentation technique among the five methods mentioned in the present study to segment the experimental Al2O3 FESEM images.