Prewitt Operator Research Articles

Automatic recognition system for concrete cracks with support vector machine based on crack features

Cracks are a common problem in concrete surfaces. With the continuous optimization of machine vision-based inspection systems, effective crack detection and recognition is the core of the entire system. In this study, support vector machine (SVM) was used to distinguish cracks from other regions. To complete the recognition system of the SVM, a framework consisting of an image processing and recognition model was proposed. An algorithm combining the Prewitt operator with the Otsu threshold was proposed for image segmentation. The binary image processed by the new algorithm combined with mathematical morphology can result in a more complete crack zone and fewer interference regions. After the initial parameter extraction, most of the impurity areas were screened by preliminary discrimination, removing 99% of the impurities. This processing step ensured the balance and effectiveness of the samples. To establish an automatic identification model based on SVM with a radial basis function, compactness, occupancy rate, and length–width ratio were selected as input parameters after comparing these three features with all six features of the crack. The recognition accuracy of this system reaches 97.14%, demonstrating that the proposed method is effective and satisfies practical requirements.

GraphMriNet: a few-shot brain tumor MRI image classification model based on Prewitt operator and graph isomorphic network

Brain tumors are regarded as one of the most lethal forms of cancer, primarily due to their heterogeneity and low survival rates. To tackle the challenge posed by brain tumor diagnostic models, which typically require extensive data for training and are often confined to a single dataset, we propose a diagnostic model based on the Prewitt operator and a graph isomorphic network. Firstly, during the graph construction stage, edge information is extracted from MRI (magnetic resonance imaging) images using the Prewitt filtering algorithm. Pixel points with a gray value intensity greater than 128 are designated as graph nodes, while the remaining pixel points are treated as edges of the graph. Secondly, the graph data is inputted into the GIN model for training, with model parameters optimized to enhance performance. Compared with existing work using small sample sizes, the GraphMriNet model has achieved classification accuracies of 100%, 100%, 100%, and 99.68% on the BMIBTD, CE-MRI, BTC-MRI, and FSB open datasets, respectively. The diagnostic accuracy has improved by 0.8% to 5.3% compared to existing research. In a few-shot scenario, GraphMriNet can accurately diagnose various types of brain tumors, providing crucial clinical guidance to assist doctors in making correct medical decisions. Additionally, the source code is available at this link: https://github.com/keepgoingzhx/GraphMriNet.

Hybrid A-Star Path Planning Method Based on Hierarchical Clustering and Trichotomy

Aiming to improve on the poor smoothness and longer paths generated by the traditional Hybrid A-star algorithm in unstructured environments with multiple obstacles, especially in confined areas for autonomous vehicles, a Hybrid A-star path planning method based on hierarchical clustering and trichotomy is proposed. This method first utilizes the Prewitt compass gradient operator (Prewitt operator) to identify obstacle boundaries and discretize boundaries. Then, it employs a single linkage hierarchical clustering algorithm to cluster obstacles based on boundaries. Subsequently, the clustered points are enveloped using a convex hull algorithm, considering collision safety for vehicle expansion. This fundamentally addresses the ineffective expansion issue of the traditional Hybrid A-star algorithm in U-shaped obstacle clusters. Finally, the expansion strategy of Hybrid A-star algorithm nodes is improved based on the trichotomy method. Simulation results demonstrate that the improved algorithm can search for a shorter and smoother path without significantly increasing the computational time.

Building footprint extraction from aerial images using an edge-aware YOLO-v8 network

Abstract. Building footprint extraction is a critical indicator for assessing urban infrastructure, and extracting building footprints from remote sensing imagery can have significant practical applications. However, achieving rapid and accurate extraction of building footprints remains highly challenging, especially in scenarios with complex scenes, dense building distributions, and small targets. The instance segmentation models of the YOLO series offer strong real-time performance, reducing considerable time and effort in practical applications. Therefore, we propose building footprint extraction based on an enhance YOLO-v8 network. This study focuses on three enhancements to the YOLO-v8 network to improve extraction accuracy. Building upon the YOLO-v8 framework, we have incorporated the Feature Pyramid Network (FPN) module into feature maps at all scales to efficiently propagate high-level semantic information. Additionally, we introduce the Triple Feature Encoder (TFE) module, which integrates spatial detail information from feature maps at three different scales to enhance the network's ability to extract multi-scale information. Finally, we explore the integration of the Prewitt model, a conventional edge detection operator, to assist in extracting edge features in target regions of feature maps. This integration aims to reduce the jagged edges frequently seen in the outcomes of the original YOLO-v8. Furthermore, the Prewitt operator's noise suppression capability helps mitigate the influence of non-target areas in the feature maps. The proposed framework achieves an instance segmentation accuracy of mAP50 is 84.6% and mAP50:95 is 51.4% on public datasets, outperforming the original YOLO-v8 network.

Multi-modal medical image fusion using improved dual-channel PCNN.

This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a novel improved dual-channel pulse-coupled neural network (IDPCNN) model to fuse the high-pass sub-images, whereas the Prewitt operator is combined with maximum regional energy (MRE) to construct the fused low-pass sub-image. First, the gray-scale image and luminance of the pseudo-color image are decomposed using NSST to find the respective sub-images. Second, the low-pass sub-images are fused by the Prewitt operator and MRE-based rule. Third, the proposed IDPCNN is utilized to get the fused high-pass sub-images from the respective high-pass sub-images. Fourth, the luminance of the fused image is obtained by applying inverse NSST on the fused sub-images, which is combined with the chrominance components of the pseudo-color image to construct the fused image. A total of 28 diverse medical image pairs, 11 existing methods, and nine objective metrics are used in the experiment. Qualitative and quantitative fusion results show that the proposed method is competitive with and even outpaces some of the existing medical fusion approaches. It is also shown that the proposed method efficiently combines two gray-scale images.

Identification of asphalt pavement transverse cracking based on 2D reconstruction of vehicle vibration signal and edge detection algorithm

Transverse cracking is related to asphalt pavement structural performance. However, the method for transverse cracks identification by vehicle vibration signal has hardly been applied because of an extremely low recognition accuracy. The study aims to precisely identify the pavement transverse cracks by vehicle vibration response. A two-dimensional reconstruction method that transformed the vehicle vibration signal into an image was proposed. Five edge detection operators were used to generate the features of vibration reconstruction images. Besides, this study combined two basic characteristics, Euler number, and total target area. Eight classification models based on the above two features were trained by the RUSBoost integrated classifier. The results showed that the classification model based on the Prewitt operator eight connected domain Euler number and the total target area performed excellently. The testing accuracy can reach 93.5%. The classification model presented in this study has an excellent effect on pavement transverse crack identification and feasibility.

Phantom study of layered sensor module for photon-counting BMD detector.

In this study, we perform bone mineral density (BMD) calculation by designing a layered sensor module (LSM) that divides high- and low-energy spectra from a single shot of X-rays. Gamma-ray evaluation supports this mechanism; low-energy gamma rays are absorbed in the front detector, whereas high-energy gamma rays are absorbed in the rear detector. In this phantom study, LSM divides a single shot of X-ray into two spectra with different distributions of energy, thereby affording X-ray images with different properties, such as contrast and gray scale. The region of interest (ROI) is classified by the Prewitt operator to sort the pixels for BMD calculation or Rs value. The calculated final value is 1.2051g/cm2 with a standard deviation (SD) of 0.3690g/cm2, as obtained from our previous study. An improved SD results from the layered structure with two channels for signal processing, the introduction of Rs value, and the use of Prewitt filter to sort reliable data. Overall, this study displays the feasibility of LSM for BMD calculation with a small error, thereby enabling the diagnosis of osteoporosis with novel mechanism.

Insulator Crack Detection Using Soft Computing Techniques

The edge extraction technique in image processing was used to identify and classify micro-cracks on the composite insulator material in order to extract the shed's micro-cracks. The composite insulator has a significant impact on the operation and even power lines, so faults in the composite insulator must be identified. From the standpoint of image processing, the proposed approach for this project includes an image of a composite insulator, including micro-cracks. To extract the micro-crack on the insulator, first preprocess the image to extract the insulators after graying and edge-detecting. Image analysis refers to the process of altering the features of an image by executing various adjustments or alterations utilizing various functions. The Sobel operator, the Canny operator, the Prewitt operator, and the Laplace operator are all edge extraction operators that use this concept. Finally, images of composite insulators were extracted using different methodologies and compared, and the images were assessed using composite insulator features.

Study on a method for suppressing influence of nonuniform surface emissivity of material in eddy current thermography

The nonuniform emissivity of material surface will adversely affect the detection results of the thermal imaging technology. Aiming at the influence of uneven emissivity on the dynamic detection mode of eddy current thermography, a suppression method based on edge detection is proposed in this paper. Since the traditional threshold selection methods are not ideal, a local adaptive threshold selection (LATS) method is applied in this study. This method can automatically obtain the optimal threshold for edge detection to suppress the influence of uneven emissivity. Roberts, Sobel, Prewitt, and Canny operators are tested by using LATS. The results show that Roberts, Sobel, and Prewitt operators can achieve the suppression effect, but the effect of Sobel and Prewitt are similar and better than Roberts.

A Backprojection-Based Autofocus Imaging Method for Circular Synthetic Aperture Radar

Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information through 360° observation of the target. However, CSAR is often subject to motion errors due to the sensor’s accuracy limitations and the unique trajectory of the system. The autofocusing back projection algorithm based on the optimization of image sharpness compensates for motion errors through phase-error estimation. This method can attain relatively good performance while assuming the same error for all pixels; it ignores the spatial variance of motion errors. In order to solve this problem, this paper proposes an autofocusing method based on the Prewitt operator and particle swarm optimization (PSO), which first extracts the subaperture image feature points as a new dataset using the Prewitt operator, estimates the radar slope error on the new dataset using PSO, and subsequently compensates the subaperture image. Finally, the subaperture images are synthesized using an image-alignment method. The results of both the simulated experiments and the measured data validate the effectiveness of this method.

A Fault Warning Method for Hotline Tap Clamp Infrared Images Based on Hybrid Segmentation

Substation equipment faults are typically related to the heating of equipment components. The hotline tap clamps of substation are critical components for carrying load currents and thermal fault potential. As a result, a new hybrid early warning approach for hotline tap clamp faults in substation equipment is presented. A two-dimensional Otsu algorithm is used to coarse-segment infrared images to minimize the subsequent complexity. Since the Chan–Vese (CV) model is insufficiently accurate for image segmentation with uneven grayscale, then the differential data obtained by the Prewitt operator to identify the goal edges are combined with the CV model to improve segmentation accuracy. The improved CV model achieves excellent segmentation of the hotline tap clamp in the substation. The temperature statistics are utilized for the segmented images, and the hotline tap clamp fault warning is realized based totally on the relative temperature difference. Finally, the experiments exhibit that the method can enhance the segmentation impact of infrared images and obtain the goal of fault warning.

3DCNN landslide susceptibility considering spatial-factor features

Effective landslide disaster risk management contributes to sustainable development. A useful method for emergency management and landslide avoidance is Landslide Susceptibility Mapping (LSM). The statistical landslide susceptibility prediction model based on slope unit ignores the re-lationship between landslide triggering factors and spatial characteristics. It disregards the influence of adjacent image elements around the slope-unit element. Therefore, this paper proposes a hardwired kernels-3DCNN approach to LSMs considering spatial-factor features. This method effectively solved the problem of low dimensionality of 3D convolution in the hazard factor layer by combining Prewitt operators to enhance the generation of multi-level 3D cube input data sets. The susceptibility value of the target area was then calculated using a 3D convolution to extract spatial and multi-factor features between them. A geospatial dataset of 402 landslides in Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province, China, was created for this study. Nine landslide trigger factors, including topography and geomorphology, stratigraphic lithology, rainfall, and human influences, were employed in the LSM. The research area’s pixel points’ landslide probabilities were then estimated by the training model, yielding the sensitivity maps. According to the results of this study, the 3DCNN model performs better when spatial information are included and trigger variables are taken into account, as shown by the high values of the area under the receiver operating characteristic curve (AUC) and other quantitative metrics. The proposed model outperforms CNN and SVM in AUC by 4.3% and 5.9%, respectively. Thus, the 3DCNN model, with the addition of spatial attributes, effectively improves the prediction accuracy of LSM. At the same time, this paper found that the model performance of the proposed method is related to the actual space size of the landslide body by comparing the impact of input data of different scales on the proposed method.

Comparative Analysis of Image on Several Edge Detection Techniques

Edge detection of an image is needed to obtain information related to the size and shape of an image. There are numerous methods for detecting edges, including the Prewitt, Laplace, and Kirsch operators. Each edge detection method has different performance and results. Therefore, this study aims to analyze the performance comparison of the Prewitt, Laplace, and Kirsch operators. The analysis process is carried out using MSE, PSNR and Image Contrast values. Based on the experiments that have been carried out, the best edge detection is produced by the Prewitt operator. The average MSE and PSNR values obtained were 4.63 and 41.79 dB. The Laplace operator is good in the contrast value of 17.77. However, the contrast value only serves as a supporting parameter to clarify the differences in the results of each edge detection operator. So it can be concluded that the Prewitt edge detection method is the best method among the other two methods.

SAR minimum entropy autofocusing based on Prewitt operator.

Current autofocus algorithms utilizing image criteria impose a significant computational burden. Therefore, this paper proposes a computationally efficient autofocus algorithm combined with SAR image feature points, employing the Prewitt operator to obtain the SAR image features. The range cell with the number of feature points in the front row as the input of the autofocus method to perform motion error estimation and compensation on SAR imagery. Our method's key feature is to optimize the selection criteria of range cells by acquiring the feature points of SAR images,reduces the number of input range cell,reduce the computational complexity of the autofocus algorithm and ultimately enhance the focusing effect of SAR images. Trials involving simulation and measured data demonstrate the effectiveness of the developed method.

Automatic acquisition method for evaluating the on-orbit modulation transfer function based on lunar targets.

In view of the existing method having a great subjectivity for the lunar edge selection, we propose an automatic knife-edge detection method based on the Hough transform to evaluate the on-orbit modulation transfer function (MTF) of the satellite remote sensor. This novel method avoids the dependence of the on-orbit MTF measurement on the edge selection location, overcoming the limitation of the traditional method needing to identify the lunar shape and fix its edge area. On basis of two different edge detection algorithms (Sobel operator and Prewitt operator), the binary edge images of the moon are acquired, thus obtaining a series of edges satisfying the determination requirement by the Hough transform, and the MTFs corresponding to each knife-edge are calculated to obtain the optimal MTF. The automatic knife-edge detection method greatly improves the accuracy of the lunar edge selection, and the MTF obtained by the novel method is obviously better than that of the traditional method. In order to verify the effectiveness of the novel method, the long time series of the on-orbit MTFs for the FY-2G and FY-2E satellite measurements are given, indicating that the FY-2E observation has higher stability and better performance compared with that of the FY-2G satellite. This study has an important practical significance for evaluating the on-orbit stability of the satellite its optical imaging quality.

Visual Object Recognition System

This article introduces the problem of object detection and recognition. The potential mobility of this solution, ease of installation and ease of initial setup, as well as the absence of expensive, resource-intensive and complex image collection and processing systems are presented. Solutions to the problem are demonstrated, along with the advantages and disadvantages of each. The selection of contours by a filter based on the Prewitt operator and a detector of characteristic points is an algorithm of the system, developed within the framework of object recognition techniques. The reader can follow the interim and final demonstrations of the system algorithm in this article to learn about its advantages over traditional video surveillance systems, as well as some of its disadvantages. A webcam with a video frame rate of 25 frames per second, a mobile phone and a PC with the Matlab2020 programming environment installed (due to its convenience and built-in image processing functions) are required to illustrate how the system works.

Image Edge Detection Capture Zoom for Facial Recognition Using Gradient Operators

Imagery on edge detection is a process that will display the edges of an image. Basically, edge detection is one of the techniques for the analysis of image quality in the spatial domain and is also one of the initial process in digital image processing. Edge detection serves to detect the border line of an object contained in the image. This study aims to identify and recognize face pattern objects in the capture zoom image. To perform face identification begins with collecting image data, image processing, image edge detection, thinning of the image, and identification process using the template matching method. The method used in edge detection uses 3 methods, namely Sobel, Roberts and Prewitt which are gradient operators to detect edges in facial images. The dataset used is image capture zoom. The trial was carried out in two stages, namely the identification of the face shape and the identification of the edges of the face. The conclusion of the study is that the Roberts operator is the operator that finds the least edge patterns in facial images than the other two operators, namely Prewitt and Sobel. Meanwhile, the Sobel operator produces edge patterns that are better in quality and quantity than using the Roberts and Prewitt operators.

Image Edge Detection Methods in Perimeter Security Systems Using Distributed Fiber Optical Sensing.

This paper aims to evaluate detection algorithms for perimeter security systems based on phase-sensitive optical time-domain reflectometry (-OTDR). Our own designed and developed sensor system was used for the measurement. The main application of the system is in the area the perimeter fencing intrusion detection. The system is unique thanks to the developed motherboard, which contains a field-programmable gate array (FPGA) that takes care of signal processing. This allows the entire system to be integrated into a 1U rack chassis. A polygon containing two different fence types and also cable laid underground in a plastic tube was used for testing. Edge detection algorithms using the Sobel and Prewitt operators are considered for post-processing. The comparison is made based on the signal-to-noise ratio (SNR) values calculated for each event. Results of algorithms based on edge detection methods are compared with the conventional differential method commonly used in -OTDR systems.

Edge detection on noisy images using Prewitt operator and fractional order differentiation

Edge detection on noisy images using Prewitt operator and fractional order differentiation

Identifying the Lineament Structure Cooperatively Using the Airborne Gravimetric, Magnetic, and Remote Sensing Data: A Case Study From the Pobei Area, NW China

Identification of lineament structure plays a vital role in determining the metallogenic area and distribution of the geologic structure. Edge detection methods are mostly used to recognize the lineaments and define the geologic boundaries. Cooperatively using edge detection results of the gravity, magnetic and remote sensing data to recognize lineaments would obtain more geologic information. In this paper, new edge detectors of potential field derivatives are proposed to determine the sources’ boundary, named second tilt derivative, tilt of vertical derivative, and normalized second vertical derivative, respectively. Presented approaches are characterized by producing zero amplitude over sources’ edges and equalizing anomalies from different depths. Compared with original edge detection techniques including other second derivative methods, synthetic examples reveal significant superiorities of suggested approaches in providing more accurate and sharper edges and are especially effective in distinguishing superimposed anomalies. The experiments also demonstrate that the normalization to the edge detectors will make images cleaner and geologic edges more easily captured. Applied to airborne gravimetric and magnetic data in the Pobei area (NW China), the proposed methods display more geologic details and lineaments. Canny, Sobel, and Prewitt operators are applied to extract boundaries of remote sensing image. Lineaments picked by the three different types of data are combined collectively to get a comprehensive lineaments structure interpretation.