Entropy Threshold Segmentation Research Articles

Online Measurement of Melt-Pool Width in Direct Laser Deposition Process Based on Binocular Vision and Perspective Transformation.

Direct laser deposition (DLD) requires high-energy input and causes poor stability and portability. To improve the deposited layer quality, conducting online measurements and feedback control of the dimensions, temperature, and other melt-pool parameters during deposition is essential. Currently, melt-pool dimension measurement is mainly based on machine vision methods, which can mostly detect only the deposition direction of a single melt pool, limiting their measurement range and applicability. We propose a binocular-vision-based online measurement method to detect the melt-pool width during DLD. The method uses a perspective transformation algorithm to align multicamera measurements into a single-coordinate system and a fuzzy entropy threshold segmentation algorithm to extract the melt-pool true contour. This effectively captures melt-pool width information in various deposition directions. A DLD measurement system was constructed, establishing an online model that maps the melt-pool width to the offline deposited layer width, validating the accuracy of the binocular vision system in measuring melt-pool width at different deposition angles. The method achieved high accuracy for melt-pool measurements within certain deposition angle ranges. Within the 30°-60° measurement range, the average error is 0.056 mm, with <3% error. The proposed method enhances the detectable range of melt-pool widths, improving cladding layers and parts.

Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor

This study was aimed at enhancing pothole detection by combining sigmoid calibration function and entropy thresholding segmentation on UAV multispectral imagery. UAV imagery was acquired via the flying of the DJI Matrice 600 (M600) UAV system, with the MicaSense RedEdge imaging sensor mounted on its fixed wing. An endmember spectral pixel denoting pothole feature was selected and used as the base from which spectral radiance patterns of a pothole were analyzed. A field survey was carried out to measure pothole diameters, which were used as the base on which the pothole area was determined. Entropy thresholding segmentation was employed to classify potholes. The sigmoid calibration function was used to reconfigure spectral radiance properties of the UAV spectral bands to pothole features. The descriptive statistics was computed to determine radiance threshold values to be used in demarcating potholes from the reconfigured or calibrated spectral bands. The performance of the sigmoid calibration function was evaluated by analyzing the area under curve (AUC) results generated using the Relative Operating Characteristic (ROC) technique. Spectral radiance pattern analysis of the pothole surface revealed high radiance values in the red channel and low radiance values in the near-infrared (NIR) channels of the spectrum. The sigmoid calibration function radiometrically reconfigured UAV spectral bands based on a total of 500 sampled pixels of pothole surface obtained from all the spectral channels. Upon successful calibration of UAV radiometric properties to pothole surface, the reconfigured mean radiance values for pothole surface were noted to be 0.868, 0.886, 0.944, 0.211 and 0.863 for blue, green, red, NIR and red edge, respectively. The area under curve (AUC) results revealed the r2 values of 0.53, 0.35, 0.71, 0.19 and 0.35 for blue, green, red, NIR and red edge spectral channels, respectively. Overestimation of pothole 1 by both original and calibrated spectral channels was noted and can be attributed to the presence of soils adjacent to the pothole. However, calibrated red channel estimated pothole 2 and pothole 3 accurately, with a slight area deviation from the measured potholes. The results of this study emphasize the significance of reconfiguring radiometric properties of the UAV imagery for improved recognition of potholes.

3-D visual analysis and quantitative characterization on coal matrix based on Micro-CT

Coal matrix, the storage space for adsorbed gas, is highly related to gas recovery and gas outburst disasters. However, existing research predominantly relies on theoretical assumptions, lacking experimental validation. In this study, we employ X-ray computed tomography (CT) to obtain high-resolution (13.45–15.157 μm) internal structure images of various coal samples. The three-dimensional model of coal matrix elements is reconstructed using entropy threshold segmentation and watershed segmentation. A systematic quantification of the size, shape, and spatial arrangement of these matrix elements is then performed. The results reveal that coal matrix element equivalent diameters span 148.45–2800.35 μm, with volume-weighted average equivalent diameters ranging from 1320.82 to 1720.87 μm. Additionally, we explore particle size distribution (PSD) models to characterize size-quantity relationships and identify the Jaky model based on the logarithmic-exponential equation as the optimal PSD model. Observation of the matrix element models shows that large matrix elements typically exhibit flattened morphologies with rough surfaces, while smaller ones tend to be blocky with smooth surfaces. The dimensionless shape factor results suggest that, when scale effects are not considered, the shape of larger matrix elements is more conducive to matrix-fracture gas mass transfer. Based on this, a functional relationship between the matrix shape factor and size, considering changes in matrix morphology, is established. Moreover, we algorithmically determine that the average number of adjacent matrix elements per individual matrix element falls between 13 and 15. For matrix clusters with large volume fractions, the size ratio of the central matrix element to adjacent matrix elements primarily falls within the range of 0.4–0.6, approximately following a Weibull distribution. According to this feature, a method for building a matrix cluster model used for simulations is proposed.

A Method for Extracting Contours of Building Facade Hollowing Defects Using Polarization Thermal Images Based on Improved Canny Algorithm

During the service process of high-rise buildings, hollowing defects may be produced in the decorative layer, which not only affect the appearance, but also create a safety hazard of wall covering and shattered plaster peeling. Numerous studies have shown that hollowing can be detected using infrared thermal imagery under normal conditions. However, it is difficult to detect the edge and calculate the area of the hollowing on an exterior facade accurately because of the low contrast and fuzzy boundaries of the obtained infrared thermal images. To address these problems, a method for extracting the contours of building facade hollowing defects using polarization thermal images based on an improved Canny algorithm has been proposed in this paper. Firstly, the principle of thermal polarization imaging was introduced for hollowing detection. Secondly, considering the shortcomings of the Canny edge detection algorithm and the features of polarization thermal images, an improved Canny edge detection algorithm is proposed, including adaptive bilateral filtering to improve noise reduction ability while ensuring defect edges are not virtualized, Laplacian sharpening and histogram equalization to achieve contour sharpening and contrast enhancement, and eight-direction gradient templates for calculating image gradients, which make interpolation with non-maximum suppression more accurate, and the Tsallis entropy threshold segmentation algorithm based on the OTSU algorithm verification makes the image contour information more complete and accurate. Finally, a long-wave infrared polarization thermal imaging experimental platform was established and validation experiments were conducted. The experimental results demonstrate that the distinct, smooth, and precise location edges of the hollowing polarization infrared thermal images can be obtained, and the average error of the detected hollowing area is about 10% using the algorithm proposed in this paper.

Adaptive granulation Renyi rough entropy image thresholding method with nested optimization

The rough entropy based image thresholding algorithm can effectively deal with the uncertainty information in an image. Nevertheless, the existing rough entropy based thresholding methods have two limitations. One is that there is no generalized rough entropy definition. The other is that the granule size cannot be selected automatically in combination with the image information in the granulation process. In order to address these two problems, an adaptive granulation Renyi rough entropy image thresholding method with nested optimization is proposed in this paper. First, a definition of generalized rough entropy with parameter based on Renyi entropy form is proposed to describe the uncertainty information of complex images. Second, in order to select the granule size in combination with the specific image information, a granule size selection method is proposed by maximizing the uniformity of the segmented image regions. Finally, a nested optimization adaptive granulation Renyi rough entropy thresholding segmentation algorithm is proposed. In the experiments, the Renyi rough entropy is compared with the existing four rough entropy in image thresholding. The new parametric rough entropy can obtain better segmentation results than the other existing rough entropy. The novel adaptive granulation Renyi rough entropy thresholding segmentation algorithm is compared with the state-of-the-art image segmentation methods on several different image data sets, which verifies the effectiveness of the novel algorithm.

Machine vision based insulator image chip drop fault recognition method

Abstract The paper studies the method of insulator image chip drop fault recognition based on machine vision to improve the recognition effect of insulator chip drop fault.Zhang’s calibration method was used to calibrate the internal parameters of binocular vision camera to improve the results in image acquisition. Insulator images were captured by binocular vision camera after calibration. The color image is converted to digital image and the insulator target image is extracted from the saturation component of a digital image by the optimal entropy threshold segmentation method. The nonlinear function is used to correct the high-frequency subband coefficients in different scales and different directions in the insulator target image, and the coefficients are used to extract the high-frequency information of different scales and directions.. The improved one-dimensional Hough transform detection algorithm is used to identify the ellipse in the edge image, that is, the insulator. The decision conditions of fault identification are designed to complete the fault identification of insulator drop. Experiments show that this method can clearly collect insulator images under different weather conditions, effectively segment insulator target images and accurately extract insulator edge information. The method can accurately identify the insulator drop fault based on the ellipse detection results. When the contrast and brightness factors are different, the f1-score value of fault recognition is higher and the fault recognition effect is better.

Robust Crater Detection Algorithm Based on Maximum Entropy Threshold Segmentation

For future lunar exploration and planetary missions, the digital elevation model (DEM) of the target object may not be well prepared before the mission, so developing a new robust crater detection algorithm (CDA) without prepared high-precision DEM is needed to meet the requirements of a high-reliability and high-precision detection and navigation system. In this paper, we presented a new robust lunar CDA method based on maximum entropy threshold segmentation. By calculating the entropy distribution of the ternary image, the threshold for retaining the maximum amount of image information is selected adaptively, a variety of evaluation indicators are proposed, and a multiple-indicator constraint matrix is constructed to realize the extraction and fitting of the craters. The proposed method has the following advantages: (1) it has strong robustness and is capable of extracting complete craters under multiple illumination conditions, which makes it suitable for the extraction of large-scale planetary and lunar images; (2) the extracted crater edges are clear and complete and do not merge with the surrounding environment edge; and (3) it avoids the problem of parameter sensitivity that is present in a traditional CDA algorithm. The proposed method was verified using an image taken by the Chang’e-2 lunar probe, and a comparison with the traditional method based on morphology and adaptive Canny edge detection shows that the number of craters detected increases by more than 35%, while the computational efficiency is improved by more than 40%.

Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT

Wheat ear counting is a prerequisite for the evaluation of wheat yield. A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation. The frequency domain decomposition of wheat ear image is completed by multiscale support value filter (MSVF) combined with improved sampled contourlet transform (ISCT). Support Vector Machine (SVM) is the classic classification and regression algorithm of machine learning. MSVF based on this has strong frequency domain filtering and generalization ability, which can effectively remove the complex background, while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears. In order to improve the level of wheat yield prediction, MSVF-ISCT method is used to decompose the ear image in multi-scale and multi direction in frequency domain, reduce the interference of irrelevant information, and generate the sub-band image with more abundant information components of ear feature information. Then, the ear feature is extracted by morphological operation and maximum entropy threshold segmentation, and the skeleton thinning and corner detection algorithms are used to count the results. The number of wheat ears in the image can be accurately counted. Experiments show that compared with the traditional algorithms based on spatial domain, this method significantly improves the accuracy of wheat ear counting, which can provide guidance and application for the field of agricultural precision yield estimation.

Investigation of Spatter Characteristics in Selective Laser Melting Based on Maximum Entropy Threshold Segmentation Algorithm

Investigation of Spatter Characteristics in Selective Laser Melting Based on Maximum Entropy Threshold Segmentation Algorithm

Application of Multiple Random Forest Algorithm in Image Segmentation of Nanoparticles

Because of its large specific surface area, small particle size, high surface energy, and unique nanoeffect, the morphological characteristics of nanoparticles are the key factors affecting the properties of materials. How to detect and evaluate the morphological characteristics of nanoparticles is the first problem to be solved in the preparation and application of nanomaterials. The main purpose of this paper is to use TEM to recognize the image features of nanoparticles and introduce the transmission electron microscope and image edge segmentation method and random forest algorithm. A method integrating the in situ characterization of modern electron microscopy and the measurement of the electrical properties of nanomonomers was developed. In this paper, a multielectrode TEM in situ electrical measurement platform is prepared, which improves the contact during the integration of nanomaterials and improves the electrical measurement accuracy of the TEM in situ electrical method. In this paper, based on the random forest algorithm, a multirandom forest algorithm is proposed. Due to the different gray levels of images referenced by the multirandom forest algorithm, the segmentation results are processed by FCM clustering algorithm. Experimental results show that in terms of image segmentation accuracy, the minimum Jaccard coefficient obtained by multiple random forest algorithm is 89% and 95%, respectively, which is obviously better than watershed segmentation method and maximum entropy threshold segmentation. In the aspect of automatic image segmentation of nanoparticles, the image segmentation accuracy is the highest when the sample block size and the number of sample blocks selected in the multiple random forest algorithm are 5∗5, 7500, and 35, respectively. Therefore, the multirandom forest algorithm has achieved high accuracy in image segmentation of nanoparticles, which provides valuable information for the preparation and application of nanomaterials. A new type of TEM dark‐field imaging diaphragm was prepared, which greatly improved the imaging quality of weak‐phase bulk materials represented by graphene and nonspiral biological samples represented by intracellular polyvesicles.

A Novel Video Copyright Protection Scheme Based on Blockchain and Double Watermarking

With the continuous development of multimedia, more and more digital works such as videos are spread, stored, and used in the network. In recent years, digital copyright infringement disputes have occurred frequently. The traditional copyright protection system has some problems, such as difficulty confirming copyright, monitoring infringement, and obtaining evidence for rights protection. To this end, we have designed and implemented a novel video copyright protection scheme based on the blockchain and double watermarking technology. We use the image correlation coefficient method to extract video keyframes. And we combine with Contourlet Transform domain, QR decomposition, and SIFT algorithm to improve the robustness of watermark against geometric attacks on the premise of invisibility. After that, we use Arnold Transformation (Cat Map) based on the Maximum Entropy Threshold Segmentation to encrypt the robust watermark and strengthen the security. Moreover, based on the characteristics of the fragile watermarking, we accurately locate the attacked video’s tamper position and complete the integrity authentication of the watermarked video. In addition, the hash digest of the video watermark and the user ID of the copyright owner is signed by SM2 and uploaded to the blockchain. The user can register the copyright after passing the identity authentication. We conduct tests and security analysis on the blockchain performance of the system, the performance of the commercial cryptography algorithm, and the security of the watermarking system. The experimental results show that the blockchain used in this system conforms to the industry standard, the performance of SM2 and SM3 is better than ECC-256 and SHA-256, and the system security is well guaranteed.

Bayesian prediction of the stress concentration effect on high-strength wires with corrosion pits

This paper investigated the relationship between the morphology of corrosion pits and the stress concentration factor. Several images of corroded wire with different corrosion durations were obtained based on corrosion-accelerated test under the coupling effect of alternating load and salt-spray environment, then the morphology and size of pits were identified by the maximum entropy threshold segmentation method. A finite-element model of high-strength wire with corrosion pits was constructed and analyzed to explore the stress concentration factor close to the corrosion pits. Subsequently, a Bayesian prediction model of the stress concentration effect on wires with corrosion pits was proposed. In conclusion, the morphology of corrosion pits was described as “ellipsoidal”, with its length and width obeying the lognormal distribution and its depth obeying the Weibull distribution. Bayesian analysis indicated that the stress concentration factors around a corrosion pit were affected by the length–width and width-depth ratios of the pit. The stress concentration effect close to corrosion pits with narrow or deep appearance was more dramatic than that of other pits.

A Method for Segmentation of Transformer Oil Level Region Based on Infrared Image

With the rapid development of computer and artificial intelligence technology, infrared images are playing a higher and higher value in the state monitoring of electrical equipment in substations. Aiming at the detection of the oil level area of the oil conservator in the substation, in order to accurately obtain the oil level information, this paper proposes a transformer oil level detection method combining maximum entropy threshold segmentation algorithm and SLIC (simple linear iterativeclustering) segmentation algorithm. First, the original infrared image is pre-processed by image cutting technology to obtain the infrared image of the oil pillow area; the green channel image is extracted, and then the infrared image is segmented by the maximum entropy threshold segmentation algorithm to remove the background interference and obtain the target area of the electrical equipment. Finally, SLIC is used to super the pixel segmentation algorithm performs segmentation to obtain the oil level area. Experiments show that the algorithm can clearly segment the oil level area of the oil pillow, and it has certain practical value.

Surface defect identification of Citrus based on KF-2D-Renyi and ABC-SVM

In allusion to the problems of citrus surface defect identification such as blurred edges, unclear images, more interference and difficulty in defect identification, surface defect identification of citrus based on KF-2D-Renyi and ABC-SVM was proposed in this paper. First, the method based on the dark channel prior (DCP) was used to defog the citrus images collected. Then, the firefly algorithm based on Kent chaos was used to optimize two-dimensional Renyi entropy threshold segmentation algorithm (2D-Renyi). The citrus surface defects were segmented, and the image features were extracted. Finally, the image feature vectors were input into the ABC-SVM classifier to determine the citrus defect types. We selected 8 kinds of citrus surface defects to carry on the experiment. In testing the segmentation algorithms, compared with the traditional threshold segmentation algorithms, the KF-2D-Renyi segmentation algorithm has a great improvement. The recognition rates for the defects whose features are obvious such as Sooty mould and Anthracnose could reach 100%. The recognition rates for the defects which are difficult to identify such as Thrips scar, Oleocellosis and Scale injury reached 95.18%, 96.37% and 98.43% respectively. In testing the classification algorithms, compared with the standard SVM classifier, the PSO-SVM classifier and the neural network classifiers, the average recognition rate of the ABC-SVM classifier reached 98.45%. The experimental results show that the method in this paper can effectively detect and classify citrus surface defects.

Image Characteristic Extraction of Ice-Covered Outdoor Insulator for Monitoring Icing Degree

Serious ice accretion will cause structural problems and ice flashover accidents, which result in outdoor insulator string operating problems in winter conditions. Previous investigations have revealed that the thicker and longer insulators are covered with ice, the icing degree becomes worse and icing accident probability increases. Therefore, an image processing method was proposed to extract the characteristics of the icicle length and Rg (ratio of the air gap length to the insulator length) of ice-covered insulators for monitoring the operation of iced outdoor insulator strings. The tests were conducted at the artificial climate room of CIGELE Laboratories recommended by IEEE Standard 1783/2009. The surface phenomena of the insulator during the ice accretion process were recorded by using a high-speed video camera. In the view of the ice in the background of the picture of fuzzy features and high image noise, a direct equalization algorithm is used to enhance the grayscale iced image contrast. The median filtering method is conducted for reducing image noise and sharpening the image edge. The maximum entropy threshold segmentation algorithm is put forward to extract the insulators and its surface ice from the background. Then, the modified Canny operator edge detection algorithm is selected to trace the boundaries of objects through the extraction of information about attributes of the endpoints of edges. After we obtained the improved Canny edge detection image for both of the ice-covered insulators and non-iced insulators, the icing thickness can be obtained by calculating the difference between the edge of the non-iced insulators image and the edge of the iced insulator image. Besides, in order to identify the icing degree of the insulators more accurately, this paper determines the location of icicles by using the region growth method. After that, the icicle length and Rg can be obtained to monitor the icing degree of the insulator. It will be helpful to improve the ability to judge the accident risk of insulators in power systems.

Retinal Vessel Segmentation Based on Multiscale Matched Filtering

Retinal vascular function is complex, morphological structure varies from person to person, and is susceptible to vascular diseases and systemic vascular diseases. Its accurate segmentation is of great significance for disease diagnosis and identification. In this paper, a multi-scale matching filtering algorithm is proposed for the uneven size of retinal blood vessels. On the basis of the traditional singlescale Gaussian matching filter, multiscale Gaussian matched filters with two sizes are used to enhance grayscale images. Enhancement is performed, and the superimposed image is binarized using a twodimensional maximum entropy threshold segmentation algorithm. The algorithm is tested in the DRIVE database with sensitivity, specificity and accuracy of 0.803, 0.959, 0.981, respectively. Comparing with the traditional algorithm, the algorithm has high sensitivity, fast running speed and rich details of segmentation results.

Information Entropy Multi-Decision Attribute Reduction Fuzzy Rough Set for Dust Particulate Imagery Characteristic Extraction

High-precision extraction of particulate characteristic modes is essential for dust explosion safety measurements, such as particulate concentration and size distribution. A new solution based on information entropy multi-decision attribute reduction fuzzy rough set is proposed to analyse the particulate morphology characteristics, which effectively avoids the shortcomings of traditional technology (low accuracy, stochasticity, etc.). The proposed approach consists of three stages: membership function modelling, attribute reduction, and maximum information entropy threshold segmentation. The membership coefficient was determined with a multi-segment function by developing the fuzzy degree of the membership model for dust image pixels. The fuzzy dependence of the conditional attribute was determined to extract the fuzzy attribute reduction. Finally, the model of coal dust particulates with information entropy was improved to extract the maximum segmentation threshold, which is significant for classification. The proposed methods were evaluated over a sequence of 30 image sets. The unclassified rate evaluation reached 0.978 for particle sizes $\ge200~\mu \text{m}$ , 0.958 for [75 $\mu \text{m}$ , 200 $\mu \text{m}$ ] and 0.950 for particle sizes $ . The proposed reduction approach offered a performance improvement in terms of more important attribute implementation. The paper demonstrated that the maximum information entropy reduction model can remove the redundant attributes without compromising the precision.

Adaptive Kaniadakis entropy thresholding segmentation algorithm based on particle swarm optimization

Kaniadakis entropy is a kind of generalized entropy based on the $$ \kappa $$ probability distribution, which has a good ability to deal with the distribution of long tail. The image thresholding algorithm based on Kaniadakis entropy can effectively segment images with long-tailed distribution histograms, such as nondestructive testing image. However, Kaniadakis entropy is a generalized information entropy with parameter. How to choose appropriate parameter $$ \kappa $$ is a problem to be solved. In this paper, we proposed an adaptive parameter selection Kaniadakis entropy thresholding algorithm. Based on a clustering effectiveness evaluation index, we transform the parameter selection problem into an optimization problem, then use particle swarm optimization search algorithm to optimize it and finally obtain the segmentation threshold under the optimal parameter. The presented algorithm can adaptively select parameters according to different images and obtain the optimal segmentation images. In order to show the effectiveness of the proposed method, the segmentation results are compared with several existing entropy-based thresholding algorithms. Experimental results both qualitatively and quantitatively demonstrate that the proposed method is effective.

Image thresholding segmentation method based on minimum square rough entropy

Image thresholding based on rough entropy is an efficient image segmentation technique. The optimal thresholds of the existing exponential and logarithmic rough entropy thresholding segmentation algorithms have unclear physical meaning. In this paper, a new form of square rough entropy is defined to measure the roughness in an image, and the corresponding image thresholding segmentation algorithm is proposed. The novel square rough entropy has good properties and simple computation. In the proposed thresholding algorithm, the optimal threshold is at the boundary between the object and the background of an image. This process is consistent with the expectation of image bi-level thresholding segmentation. To effectively granulate the image, a granule size selection method based on the homogeneity histogram is proposed, which is helpful in taking care of small objects and local variations of the images. The proposed thresholding method is evaluated by comparison with other three existing rough entropy based thresholding methods and three state-of-the-art image thresholding methods both qualitatively and quantitatively, using the natural images, the non-destructive testing images and an infrared video sequence from the OTCBVS Benchmark Data set. The comparison confirms the effectiveness of the proposed algorithm that not only is simple in form and clear in meaning, but also has good segmentation effect.