Sobel Operator Research Articles

Hybrid Features of Mask Generated with Gabor Filter for Texture Analysis and Sobel Operator for Image Regions Segmentation Using K-Means Technique

To make the image easily represented for more analysis and processing the segmentation procedure is required, where the image is portioned into its formed regions using some segmentation techniques based on features extraction. In this paper, a proposed procedure for finding the regions that formed the image is achieved based on hybrid features in two different components of different two colors spaces L*a*b* and RGB segmented by the k-means method. The hybrid features which comprise the mask segmentation are a combination of texture image characterization extracted by the Gabor filter and gradient image intensity by the Sobel operator after image quality enhancement by applying wiener filter noise reduction and contrast enhancement using Contrast limited adaptive equalization (CLAHE). Some statistical metrics are used for evaluating the performance of the proposed work stages.

Fast Watershed Segmentation for Breast Cancer Detection

Within engineering and computer specializations, image processing is the most important study topic. It is one of today's fastest-growing technologies, with applications in a variety of biological sectors, including cancer sickness. According to the latest data from throughout the world, breast cancer is the most lethal of all cancer kinds. It is the most frequent cancer in women and the second leading cause of cancer mortality in women. In this study, we advocate using a watershed transformation to create a fast segmentation technique. This allows for the blending of updated information about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique. The method requires a mechanism to express the test picture in terms of the amount of change around every given pixel before it can begin the watershed modification. Each pixel in the greyscale representation of the original picture is subjected to the Sobel operator. According to the form, the tumors identified are round or semicircular and the light of the tumor dims as we travel away from its core. The complement for this previous data may be seen as a local minimum that necessitated the start of the watershed process. As a result, each tumor picture may be represented as a lake, with the center in the complement tumor picture being the least value. The identification of tumor percentage gets more reliable after using the approach. As a consequence, our computer-aided diagnostic method for mammographic breast cancer detection has improved significantly thanks to the novel methodology. The method was written in MATLAB and tested on a Windows computer. The strategy was put to the test using photos from MIAS (Mammogram Image Analysis Society, UK), which offers a consistent categorization system for mammographic examinations. In this study, we advocate using a watershed transformation to create a rapid segmentation technique. This allows for the blending of data about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique.

Performance Comparison of Edge Detection Method for Extracting Images of Lutjanus spp. Contour

Lutjanus spp. is one of the 72 genera of fish species which includes to the Lutjanidae family around the world. The Lutjanus spp. is healthy for consumption and suitable for fishing tourism. The production volume increased by 6.21% between 2001 and 2011 to produce over 118608 tons. However, about 10 species are experiencing population decline. This is because the data of captured fisheries production is still limited, making it challenging to identify the species resulting in overfishing of certain species. The process of identifying the species is based on morphometric characteristics using edge detection. This method involves a pattern identification technique by detecting outlines at the boundaries of objects in the image. In this research, several edge detection algorithms were conducted following many steps to clarify contour extraction of Lutjanus spp. (L. argentimaculatus, L. bohar, L. carponatatus, L. fulviflamma, and L. sebae). The steps included image preprocessing, shape extraction with edge detection algorithms involving the Sobel, Canny, and Laplacian operations, and visual evaluation. The results showed that the three algorithms could be used to extract the contours of Lutjanus spp. The Laplacian algorithm produced the best performance because it could extract the contours with a success rate of 89.88% without noise or broken contours.

Determination of River Hydromorphological Features in Low-Land Rivers from Aerial Imagery and Direct Measurements Using Machine Learning Algorithms

Hydromorphology of rivers assessed through direct measurements is a time-consuming and relatively expensive procedure. The rapid development of unmanned aerial vehicles and machine learning (ML) technologies enables the usage of aerial images to determine hydromorphological units (HMUs) automatically. The application of various direct and indirect data sources and their combinations for the determination of river HMUs from aerial images was the main aim of this research. Aerial images with and without the Sobel filter, a layer of boulders identified using Yolov5x6, and a layer of direct measurements of depth and streamflow velocity were used as data sources. Three ML models were constructed for the cases if one, two, or three data sources were used. The ML models for HMU segmentation were constructed of MobileNetV2 pre-trained on ImageNet data for the feature extraction part and U-net for the segmentation part. The stratified K-fold cross-validation with five folds was carried out to evaluate the performance of the model due to the limited dataset. The analysis of the ML results showed that the measured metrics of segmentation using direct measurements were close to the ones of the model trained only on the combination of boulder layer and aerial images with the Sobel filter. The obtained results demonstrated the potential of the applied approach for the determination of HMUs only from the aerial images, and provided a basis for further development to increase its accuracy.

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.

PCB defect detection system based on image processing

Under the background of intelligent manufacturing, the quality inspection of printed circuit board (PCB), as the carrier of electronic products, plays an important role. In view of the shortcomings of existing traditional detections, this paper has put forward a PCB detection method based on machine vision, focusing on image preprocessing algorithm and detection analysis. The median filter algorithm was used to remove the noise in the image. Logarithmic transformation was used to enhance image contrast. Sobel operator was used for edge detection to obtain the geometric center of the defect area. On this basis, the PCB image was registered by template matching method, and the difference between the preprocessed PCB image to be tested and the standard PCB image was calculated to get the difference map. Subsequently, morphological algorithm was used to remove the slight differences and obtain the defect map. In the end, the simulation results have shown that the proposed algorithm has good detection effect and high precision in PCB defect detection.

Image Clustering Using an Augmented Generative Adversarial Network and Information Maximization.

Image clustering has recently attracted significant attention due to the increased availability of unlabeled datasets. The efficiency of traditional clustering algorithms heavily depends on the distance functions used and the dimensionality of the features. Therefore, performance degradation is often observed when tackling either unprocessed images or high-dimensional features extracted from processed images. To deal with these challenges, we propose a deep clustering framework consisting of a modified generative adversarial network (GAN) and an auxiliary classifier. The modification employs Sobel operations prior to the discriminator of the GAN to enhance the separability of the learned features. The discriminator is then leveraged to generate representations as to the input to an auxiliary classifier. An objective function is utilized to train the auxiliary classifier by maximizing the mutual information between the representations obtained via the discriminator model and the same representations perturbed via adversarial training. We further improve the robustness of the auxiliary classifier by introducing a penalty term into the objective function. This minimizes the divergence across multiple transformed representations generated by the discriminator model with a low dropout rate. The auxiliary classifier is implemented with a group of multiple cluster-heads, where a tolerance hyper-parameter is used to tackle imbalanced data. Our results indicate that the proposed method achieves competitive results compared with state-of-the-art clustering methods on a wide range of benchmark datasets including CIFAR-10, CIFAR-100/20, and STL10.

Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing

In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, camera angle, and rotation degrades the accuracy of License Plate region detection, which in turn causes inaccurate segmentation and recognition of the license plate characters hence leading to low accuracy of the LPR systems. Therefore, it is vital to consider the most promising algorithm or technique for LP detection. In this paper, we will be analyzing and comparing five different methods for license plate detection: Morphological reconstruction, Sobel Operator, Top Hat Transform, Histogram processing, and Canny Edge detection. We will be experimentally applying these techniques on real-time captured vehicle images, using the Bounding Box algorithm for character segmentation, performing license plate character recognition using Template matching, and subsequentially evaluating and demonstrating the LPR system that promises the most accurate and efficient results.

HS-FRAG: An open source hybrid segmentation tool to delineate agricultural fields in fragmented landscapes

Fragmented croplands with small and irregular fields pose formidable challenges to represent agricultural field boundaries. Existing freeware do not consider novel segmentation algorithms, thus restrict their application to relatively homogeneous, large landholdings. We developed an open source QGIS plug-in ‘HS-FRAG’ to delineate agricultural fields of a fragmented landscape from a multi-spectral imagery using an object-based, hybrid segmentation algorithm. ‘HS-FRAG’ uses Sobel operator to extract the edges from individual spectral bands, followed by watershed algorithm to close the polygons. Three metrics, namely geometric evaluation (GE), quantitative completeness (QC), and spatial correctness (SC) were considered to evaluate segmentation performance. Robustness of the tool was demonstrated on a highly fragmented, heterogeneous cropland in south India using Sentinel-2 (10 m spatial) and Cartosat-2 (1.6 m spatial) imagery. Both Sentinel-2 and Cartosat-2 have resulted in over-segmentation (OS) with a generalization of 1.30, 1.37 and an overall accuracy (OA) of 38.06 %, 75.61 % respectively. Segmentation with Cartosat-2 has outperformed in representing the geometry, number, and extent of the reference polygons. A user guide with working examples is provided for quick acquaintance of ‘HS-FRAG’ tool and its application to other fragmented regions.

Prediction of Residual Strains Due to In-Plane Fibre Waviness in Defective Carbon-Fibre Reinforced Polymers Using Ultrasound Data

Residual strains affect the properties and performance of composite components, therefore measuring and predicting them are important. The prediction of residual strains from a model can be achieved by two steps: the generation of a geometric ply map and the modelling based on that to predict 3D residual strains. A novel method for identifying the most effective algorithm for characterising fibre orientation for the geometric ply map using ultrasound C-scan data has been developed. Finite element models were generated based on the fibre-orientation data from three different algorithms: the Radon transform, 2D fast Fourier transform, and Sobel filter. The models were used to predict residual strains due to three different severities of in-plane fibre waviness induced in a set of 18 specimens. Stratified leave-one-out cross validation was applied to obtain optimum parameters for the three characterisation algorithms and to update the values of the coefficient of thermal expansion for the material. Residual strains on the surface of the specimens were obtained from calculations based on the out-of-plane displacements measured using a digital image correlation system. The predicted and measured residual strain maps were decomposed into feature vectors using orthogonal polynomials to reduce data dimensionality and make quantitative comparisons. The measured residual strains and the predictions based on models using optimised parameters showed good agreement. The differences in performance were quantified based on the accuracy of the predicted residual strains, which showed that the Radon transform performed best.

An Algorithm to Extract the Boundary and Center of EUV Solar Image Based on Sobel Operator and FLICM

An algorithm to extract the disk boundary and center of EUV solar image using the Sobel operator, Fuzzy Local Information C-Means Clustering algorithm (FLICM), and the least square circle fitting method is proposed in this paper. The Sobel operator can determine the solar disk boundary preliminarily, and then the image is processed further using the FLICM algorithm. After the background is removed based on the clustered image and the boundary points can be highlighted, these points are fitted using the least square circle fitting method as the final boundary circle. The solar data used in this paper was from the observation of the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA) instrument. The 2523 19.3 nm solar images covering solar minimum, moderate solar activity, and more active suns were calculated using the proposed algorithm to analyze the accuracy statistically. The statistical comparison results demonstrate that the method is accurate and effective. This method can support the processing of solar EUV images and serve the operational system of a space weather forecast.

A Learnable Gradient operator for face presentation attack detection

Face presentation attack detection (PAD) aims to protect the security of face recognition systems. The existing depth-supervised method using stacked vanilla convolutions cannot explicitly extract efficient fine-grained information (e.g., spatial gradient magnitude) for the distinction between bona fide and attack presentations. To address this issue, the Sobel operator has been demonstrated effective to acquire gradient magnitude due to the fast calculation capacity for high-frequency information. However, the Sobel operator is hand-crafted so cannot deal with complex textures. Differently, we develop a learnable gradient operator (LGO) to adaptively learn gradient information in a data-driven way, which is a generalization of existing gradient operators and effectively captures detailed discriminative clues from raw pixels. In parallel, we propose an adaptive gradient loss for better optimization. Extensive experimental comparisons with the state-of-the-art methods on the widely used Replay-Attack, CASIA-FASD, OULU-NPU, and SiW datasets demonstrate the superior performance of the proposed approach.

EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection

The generation of superpixels is becoming a critical step in SAR image segmentation. However, most studies on superpixels only focused on clustering methods without considering multi-feature in SAR images. Generating superpixels for complex scenes is a challenging task. It is also time consuming and inconvenient to manually adjust the parameters to regularize the shapes of superpixels. To address these issues, we propose a new superpixel generation method for SAR images based on edge detection and texture region selection (EDTRS), which takes into account the different features of SAR images. Firstly, a Gaussian function is applied in the neighborhood of each pixel in eight directions, and a Sobel operator is used to determine the redefined region. Then, 2D entropy is introduced to adjust the edge map. Secondly, local outlier factor (LOF) detection is used to eliminate speckle-noise interference in SAR images. We judge whether the texture has periodicity and introduce an edge map to select the appropriate region and extract texture features for the target pixel. A gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) are combined to extract texture features. Finally, we use a novel approach to combine the features extracted, and the pixels are clustered by the K-means method. Experimental results with different SAR images show that the proposed method outperforms existing superpixel generation methods with an increase of 5–10% in accuracy and produces more regular shapes.

Edge Detection Based Autofocus Algorithm to Detect Accurate Camera Working Distance

In the last 10 years, industrial image processing has been used in many areas such as automotive, medicine and aviation. Especially for sensitive measurement applications, camera control systems are preferred due to their speed and quality advantages. In this study, a method is proposed to determine the optimum camera position at the image acquisition phase for industrial cameras. In the proposed approach, images are captured while the camera and telecentric lens, which are connected to the servo motor, are moving up and down. The edge informations of the captured images are extracted in the x and y directions and the amplitude value is calculated. Then, meaningful and meaningless edge information at each motor position is found with a median-based approach. Finally, the sharpness of the images are arranged by statistical analysis and the optimum focal point for the camera is found. Sobel, Robert and Prewitt Filters were applied to determine the best method for extracting edge information. Measurements were performed on the workpieces using camera positions where the focus was optimum for each method. According to the results, it has been observed that the camera position, which produces the closest result to the real values, is obtained from the focus determined by the Sobel Filter. The proposed method is leaded to researchers to provide automatic focus for camera control systems.

Memristive Fast-Canny Operation for Edge Detection

Memristor-based in- memory computing paradigm is a promising path for edge detection in image preprocessing on end devices that reduces the computational pressure on data centers. However, the implementation of the well-performing Canny operator for edge detection faces challenges in terms of computational time and area overhead when mapped to memristor arrays. In this work, we proposed an efficient memristive one-step implementation of a fast-Canny operator. Exploiting the associative property of multiplication, the conventional Canny operator consisting of Gaussian and Sobel operators is converted into a fast-Canny operator and mapped to an array of nine parallel memristors. Then, the output currents are the final pixels of the edge image. To verify the feasibility of the method, successful edge detection with high accuracy (OIS = 0.73) is achieved in device-aware simulation under device variation (<50%) and image noise ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> = 6%). Additionally, the implementation of the fast-Canny operator on memristor arrays can reduce the processing time by half and save the area of buffer compared to the prior two-convolution Canny operation. Our work suggests that the memristive fast-Canny operator could be a promising and efficient hardware solution for edge detection at the network edge.

Improved Edge Detection using Variable Thresholding Technique and Convolution of Gabor with Gaussian Filters

Medical Field, Robotic vision, Pattern recognition, Hurdle detection, and smart city are examples of areas that require image processing to achieve automation. Detecting an edge is an important stage in any computer vision application. The performance of the edge detecting algorithm is largely affected by the noise present in an image. An Image with a low signal-to-noise ratio (SNR), imposes a challenge to locate its edges. To improve the observable image boundaries, an adaptive filtering technique is proposed in this article. The proposed algorithm uses convolution of Gabor filter with Gaussian (GoG) operator to clean the noise before non-Maxima suppression. Furthermore, using variable hysteresis thresholding can further improve edge locating. The implementation of the algorithm was done by Python and Matlab. The obtained results were compared to a number of reviewed algorithms such as the Canny method, Laplacian of Gaussian, The Marr-Hildreth method, Sobel operator, and the Haar wavelet-based method. Three performance factors were used; PNSR, MSE, and processing time. The simulation result shows that the proposed method has higher PNSR, lower MSE, and shorter processing time when compared to the Canny detector, the Marr-Hildreth, Haar wavelet-based, Laplacian of Gaussian, and the Sobel operator methods. The higher PNSR, lower MSE, and shorter processing time mean improved edge details of the processed image.

Особливості комп’ютерного аналізу біомедичних зображень мікроциркуляції кон’юнктиви ока

An analysis was carried out, which showed that the task of creating a system for evaluating biomedical images of pathologies of the vessels of the human eye is relevant and needs to be solved, since the existing methods and systems of evaluation, as well as the existing methods of processing biomedical images do not meet the modern requirements for such systems in terms of accuracy , the reliability of processing biomedical images, which leads to the occurrence of errors in the diagnosis in ophthalmological studies. The results of the comparison of the effectiveness of the Kirsch, Sobel, Roberts, Wallace, SUSAN algorithms for processing biomedical images based on the set of information diagnostic features formed are the most informative for image segmentation, the algorithms based on Kirsch filtering and the nonlinear Sobel filter. A system has been developed for evaluating dynamic changes in biomedical images, which allows to evaluate the state of blood vessels and determine the conjunctival index based on the following indicators: the ratio of the diameters of arterioles and corresponding venules; caliber irregularities; meander windings; microaneurysms; glomeruli; reticular structures of vessels; changes in the number of functioning capillaries; arteriolo-venular anastomoses; hemorrhages; perivascular edema; sludge phenomena; microthrombi

UAV Target Detection under Complex Sky Background

&lt;p class="15"&gt;At present, unmanned aerial vehicles (UAVs) are widely used in various fields, and the management of UAVs is very important to solve the problems in the field of low-altitude safety. Due to the low flying height, small radar cross section, and inconspicuous characteristic signals of UAVs, the detection of UAVs based on video frames taken by fixed cameras cannot meet the existing requirements in terms of tracking speed and recognition accuracy. This paper proposes a multi-sensor fusion model. Firstly, the UAV target signal is improved by spatial filtering and improved Sobel operator edge detection algorithm, and then Gaussian filter is used to denoise, and finally the UAV small target is extracted based on the maximum inter-class variance method threshold segmentation algorithm. Experimental results show that this method can effectively enhance the UAV target signal in a complex environment, and the threshold segmentation method also has good adaptability, and can effectively screen UAVs under a complex sky background.&lt;/p&gt;

A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency.

Design of Power and Area Efficient Approximate Multipliers for Edge Detection Algorithm

Abstract: In this paper, we present a novel approximate computing scheme suitable for realizing the energy-efficient multiplyaccumulate (MAC) processing. First we design the approximate 4-2 compressors generating errors in the opposite direction while minimizing the computational costs. Based on the probabilistic analysis, positive and negative multipliers are then carefully developed to provide a similar error distance. Simulation results on various practical applications reveal that the proposed MAC processing offers the energy-efficient computing scenario by extending the range of approximate parts. This Design is implemented by Verilog HDL and simulated by Modelsim 6.4 c. The Performance is measured by Xilinx tool Synthesis Process. The proposed Sobel edge detection algorithm uses approximation methods to replace the complex operations; This design is done by Matlab and Modelsim using hdldameon, This proposed multipliers arerpelaced in the sobel operator based image Edge detection.