Sobel Edge Detection Algorithm Research Articles

Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation

Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning local vessel features, making it challenging to capture the graph structural information and fail to perceive the global context of vessels. Therefore, we propose a novel graph neural network-guided vision transformer enhanced network (G2ViT) for vessel segmentation. G2ViT skillfully orchestrates the Convolutional Neural Network, Graph Neural Network, and Vision Transformer to enhance comprehension of the entire graphical structure of blood vessels. To achieve deeper insights into the global graph structure and higher-level global context cognizance, we investigate a graph neural network-guided vision transformer module. This module constructs graph-structured representation in an unprecedented manner using the high-level features extracted by CNNs for graph reasoning. To increase the receptive field while ensuring minimal loss of edge information, G2ViT introduces a multi-scale edge feature attention module (MEFA), leveraging dilated convolutions with different dilation rates and the Sobel edge detection algorithm to obtain multi-scale edge information of vessels. To avoid critical information loss during upsampling and downsampling, we design a multi-level feature fusion module (MLF2) to fuse complementary information between coarse and fine features. Experiments on retinal vessel datasets (DRIVE, STARE, CHASE_DB1, and HRF) and coronary angiography datasets (DCA1 and CHUAC) indicate that the G2ViT excels in robustness, generality, and applicability. Furthermore, it has acceptable inference time and computational complexity and presents a new solution for blood vessel segmentation.

Edge Detection on Field Programmable Gate Array (FPGA)

Edges are significant local changes in the image and are very important features for analysing images. Edges typically occur on the boundary between two different regions in an image. An edge in an image is a significant local change in the image intensity, usually associated with a discontinuity in either the image intensity or the first derivative of the image intensity. Edge detection is one of the tools that can be used in many image processing applications for withdrawing information from image. Sobel edge detection is one of the operators that operate on gradient based edge selection methods to find edge pixels in the image. This report proposed an implementation of Sobel edge Detection algorithm to find the pixels in grayscale images. We present a video streaming architecture and IP implementation using high level synthesis. A video with 720p resolution streamed from the HDMI source and real time edge detected video captured on another monitor. For implementation, ZYBO board or kit is used with Vivado software with 2018.3 V, which has provided adequate peripherals for implementation.

Implementation of Image Enhancement and Edge Detection Algorithm on Diabetic Retinopathy (DR) Image Using FPGA

Diabetic retinopathy (DR) is an ocular ailment that may lead to loss of vision and eventual blindness among individuals diagnosed with diabetes. The blood vessels of the retina, a layer of light-sensitive tissue located at the posterior aspect of the ocular globe, are adversely impacted. The identification of DR entails the utilization of retinal fundus images. The detection of any form of abnormality in the eye through raw fundus images poses a significant challenge for medical practitioners. Hence, it is imperative to engage in the processing of fundus images. This paper delineates several image processing techniques for DR images, including but not limited to, manipulation of brightness levels, application of negative transformation, and utilization of threshold operations. It focuses on elucidating the enhancement techniques that pertain to DR images, which aim to optimize the visual quality of said images in order to facilitate more facile disease detection. The process of detecting edges within DR images is also executed by Sobel edge detection algorithm. In order to successfully execute the aforementioned algorithms, expedient and contemporaneous systems are favored to account for the intricacies of the image processing calculations. The exclusive utilization of software techniques in order to fulfill the prerequisites of advanced algorithms presents a significant challenge, owing to the multifarious processes that are involved in their computation, coupled with an exigent requirement for high processing speeds. The proposed model is utilized to articulate a proficient model for the design and execution of field programable gate array (FPGA)-based image enhancement processes along with the Sobel edge detection algorithm upon DR images. Finally, a Internet Protocol chip is developed that can combine multiple image enhancement operations into a single framework with less complexity.

Smart Edge Detection Technique in X-ray Images for Improving PSNR using Sobel Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: The aim of this study is to propose smart edge detection techniques in x-ray images for improving PSNR using the sobel edge detection algorithm and comparing it with the laplacian algorithm. Materials and methods: For the design of edge detection technique to improve PSNR Sobel edge detection algorithm is used along with the gaussian filter and it is compared with the laplacian algorithm. Sobel edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group, the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95%, and the threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the Sobel edge detection algorithm has a higher mean PSNR value of 39.15db than the laplacian algorithm 36.79db. It is observed that the Sobel edge detection algorithm performed better than the laplacian algorithm by performing an independent sample t-test. The p value is 0.09 which is greater than the normal value(p>0.05). Conclusion: Sobel edge detection has insignificantly greater PSNR when compared to the Laplacian algorithm.

Sobel Edge Detection Algorithm with Adaptive Threshold based on Improved Genetic Algorithm for Image Processing

In this paper, a novel adaptive threshold Sobel edge detection algorithm based on the improved genetic algorithm is proposed to detect edges. Because of the influence of external factors in actual detection process, the result of detection is often not accurate enough when the configured threshold of the target image is far away from the real threshold. Different thresholds of images are calculated by improved genetic algorithm for different images. The calculated threshold is used in edge detection. The experimental results show that the image processed by the improved algorithm has stronger edge continuity. It is shown that proposed algorithm has a better detection effect and applicability than the traditional Sobel algorithm.

OBJECT DETECTION BASED ON SPECTRAL ANALYSIS USING SOBEL AND ROBERTS EDGE DETECTION ALGORITHM

Aim: This paper proposes novel object detection (OD) approach based on a thorough examination of the image's details and its approximate density chart. Results: Our proposed OD approach is divided into two phases. Knowledge about Spatial Distribution of Objects obtained from a density map that is used to compute initial object positions. With the aid of the original object positions estimated, a saliency map that provides entity boundaries is then used to calculate the bounding boxes with precision, which is inspired by human attention to detail. The scale variance of objects induced by uncertain perspective is a common problem in object density map estimation. A new method for estimating the prior focus for map for any image is proposed. Sobel and Roberts Edge Detection Algorithm are used in this study. The proposed approach is based on sparse defocus dictionary learning on a newly constructed dataset. The focus power is determined by the number of non-zero coefficients of the dictionary atoms. Conclusion: The algorithm's output can capture spatial features and pick the threshold type in a variety of ways. HIGHLIGHTS: Object detection based on spectral analysis using Sobel and Roberts edge detection algorithm proved to be effective when compared with existing methodologies.

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.

A Novel Sobel Edge Detection Accelerator Based on Reconfigurable Architecture

A novel Sobel edge detection accelerator based on reconfigurable architecture is proposed to solve the problem of low power-to-performance ratio of traditional Sobel edge detection algorithm in CPU processing. The accelerator adopts pixel level fine grain image data parallel processing and row buffer storage architecture to improve the processing efficiency of edge detection. At the same time, a reconfigurable architecture based on FPGA is built. Through experiments, it can be found that the acceleration effect of the edge detection accelerator on video data is superior to that of the CPU software. Compared with similar accelerators, the acceleration performance of the novel accelerators improves by 10%. The results show that the proposed edge detection accelerator can be used in embedded systems to provide edge detection processing capability with high performance power consumption ratio.

Characterization of column flow regimes on horizontal tubes for falling‐film systems: An experimental approach

AbstractThe intertube flow regimes and hydrodynamic behavior of the liquid profile have a significant impact on the functioning of falling‐film evaporators. Column flow is one of the fundamental flow modes, which is actively observed in many industrial evaporators. A high‐speed digital photographic device is utilized to visualize the complete progression of column flow phases for intertube distances of 10/20/30/40 mm and Reynolds number (Re) values ranging from 41 to 583. The Sobel edge detection algorithm is implemented to quantify the flow parameters for each frame of a sequence. The research results indicated that the column flow regime between the inline horizontal tubes can be broadly classified into the following phases: columnar droplet flow, periodic column flow, satellite drops column flow, drift column flow, and mixing liquid film column flow. The findings show that the liquid jet diameters between the tubes range from 1.6 to 8.46 mm, an increase in the liquid jet diameter with Re and a decrease with tube‐to‐tube distance. The axial coverage of the liquid film profile beneath the tube differed from 2.07 to 3.99 mm and over the tube surface ranged from 1.14 to 2.68 mm. The formation of dry spots and flooding is a major problem in the operation of falling‐film heat exchangers. The shape and size of the liquid jet have a direct impact on the interfacial area; understanding the entire progression of column flow regimes, as well as their characteristics, can help designers avoid the aforementioned problem.

Correction Method of Visual Errors in Pole Vault Take-off Action Images

Pole Vault is one of the most difficult and complicated sport in athletic and tracks events; it is high jumping action with vaulting pole stick. In the study of Pole Vault technology, the rapid advancement of Augmented Reality (AR) technology has considerable significance for the visual error correction of the pole vault take-off action images in the simulated world. The key objective is to evaluate the correction algorithm of the visual errors of take-off action images of pole vault sport. This study analyzes the AR framework, features, and pole vault, as well as province methods for correcting visual defects. Pole vault take-off action images have been simulated with AR computation technique, and the AR images of continues pole vault made. This paper primarily provides an overview of augmented reality (AR) technology, including its properties and benefits, as well as a few visual error correcting methods. AR discusses how to choose an aerobics instructor who can help you maintain an aerobics regimen on a regular basis. Augmented reality has been used to model these pictures, and AR images of aerobics have been created. The visual error correction of the longitudinal direction, transverse direction, and height direction of AR simulated continue pole vault images with Scale Invariant Feature Transform (SIFT) algorithm, Mean Absolute Deviation (MAD) algorithm, Normalize Cross-Correlation (NCC) algorithm, and Sobel Edge Detection (SED) algorithm is proposed in this study. This revealed that poor performance in the pole vault sport is due in large part to a lack of a well-planned structure for implementing a corrective mechanism for visual mistake correction in take-off action images. The statistical result of perfect pole vault technique dissected and described, including the best manage, takeoff, drive and swing, and bar separation. Along with technical work, a pole vaulters training regimen should involve sprinting, weight lifting, and gymnastics as well.

Cancer Detection with an Image Processing Application

In this study, image processing algorithms and their performances are examined. In the first part of the study, image processing, color space, functions and frameworks that can be used in C# are studied. As a result of obtaining the necessary information, an algorithm has been written that turns existing images into gray. Then, Median filtering was applied to the image in order to increase the quality of the image. Threshold algorithm was used to clarify the region after filtering. Finally, Sobel edge detection algorithm was used for image processing. Machine learning was used to decide whether the tumor region detected after image processing is cancerous or not. A dataset and NaiveBayes Class are used for machine learning. In summary, the project determines the tumor area with image processing and tells whether the tumor is cancerous with machine learning.

A Comparative Study of Handwritten Character Recognition by using Image Processing and Neural Network Techniques

This study aims to analyze the effects of noise, image filtering, and edge detection techniques in the preprocessing phase of character recognition by using a large set of character images exported from MNIST database trained with various sizes of neural networks. Canny edge detection algorithm was deployed to smooth the edges of the images while the Sobel edge detection algorithm was used to detect the edges of the images. Skeletonization algorithm was applied to re-shape the structural shapes. In the context of the image filtering, the Laplacian filter was utilized to enhance the images and High pass filtering was used to highlight the fine details in blurred images. Gaussian noise, image noise with Gaussian intensity, function in Matlab with the probability density function P was deployed on character images of MINST. Pattern recognition neural networks are widely used in optical character recognition. Feedforward neural networks are deployed in this study. A comprehensive analysis of the above-mentioned image processing techniques is included during character recognition. Improved accuracy is observed with character recognition during the prediction phase of the neural networks. A sample of unknown characters is tested with the application of High pass filtering + feedforward neural network and 89%, the highest, average output prediction accuracy was obtained. Other prediction accuracies were also tabulated for the reader’s attention.

A Study of anImproved Edge Detection Algorithm for MRI Brain Tumor Images Based on Image Quality Parameters

Biomedical imaging with emerging technology plays a vital role in medical diagnosis and treatment. MRI is one such biomedical imaging technology. The incidence of brain tumor in increasing all over the world. Accurate detection of size and location of brain tumor helps in right diagnosis and treatment. In any kind of image processing, image segmentation is a fundamental step. Edge detection is an important technique in the analysis of images, which plays a crucial role in detecting the contour of brain tumor. In this paper, an improved algorithm called the Luminance edge detection algorithm is proposed and compared with the existing edge detection algorithms like Prewitt, Sobel, and Canny for the MRI images of the brain. PSNR and SSIM were calculated to compare the image quality of different edge detection techniques. Results revealed that the PSNR and SSIM values were slightly higher for the images obtained by the Luminance edge detection algorithm when compared to the images obtained by Prewitt, Sobel, and Canny edge detection algorithm. It proves that the proposed algorithm is better than the other edge detection algorithms in producing a quality image of the MRI brain. Hence it can be concluded that the proposed algorithm was better in detecting the contour of brain and brain tumor than the other edge detection techniques.

Detection algorithm of defects on polyethylene gas pipe using image recognition

Aiming at the defects in the polyethylene (PE) gas pipeline, a defect detection algorithm based on image recognition is proposed. Firstly, the collected image is preliminarily screened to obtain the image with defects. Gamma correction algorithm is used to enhance the image, and then dual filtering is used to eliminate the interference of noise on image recognition. Then the defect edge is extracted by the improved traditional Sobel edge detection algorithm. Image defects are segmented by adaptive threshold method, and defect contours are effectively extracted through an open operation. Finally, the defect feature parameters are extracted and inputted into an optimized support vector machine for defect recognition. The experimental results show that the detection algorithm in this paper can effectively improve the observability of the image. Through the trained support vector machine, it can effectively identify the open joint, crack, and deformation defects. The recognition rate of pipeline image defects reached 96.3%.

Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising

As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.

A Survey of Sobel Edge Detection VLSI Architectures

Edge detection is an essential processused to determine the object margins in most of the computer vision applications. Sobel edge detection algorithm, which is a simple method of edge detection, detects edges of various objects in an image. Real-time image applications need to be processed with large pixel data for a given time interval. So, Most of the VLSI architectures proposed for implementing sobeledge detection systems use FPGA, due to the parallel computing and reconfigurable feature. So, this paper introduces various VLSI architectures of sobel edge detection and comparesthe parameters like execution time, power dissipation with respect to similar input image size, different clock frequencies.

Segmentation Technique for Nucleus Detection in Blood Images for Chronic Leukaemia

leukaemia is a disease which develops in the bone marrow, causing a large formation of abnormal cells and usually affected adults. The process of inspecting visually on the microscopic images is time consuming and a tiring process. The developed technique is aimed at assisting the haematologists upon identifying the presence of nucleus in blood cell images. Therefore, this technique is hoped to aid the haematologists in early and fast identification of leukaemia. This paper will be focusing on Acute Myeloid Leukaemia (AML). This research work proposed a combination of methods for the detection of Leukaemia using image processing techniques such as L*A*B colour-based thresholding algorithm, Sobel edge detection algorithm, and watershed distance transform to identify the nucleus blasts of leukaemia cells from blood cells image. The developed technique shows that it is able to produce the image of segmented nucleus blasts.

Streak Detection in Color Printed Customer Content

Streaks are one of the most common print defects in electrophotographic printers that influence print quality. Streak defects are dark or light lines with the major axis along the printing process direction and are usually caused by a defective Intermediate Transfer Belt (ITB) or Organic Photo Conductor (OPC) component in the printer. Previously, we designed an algorithm to detect the streak defects only on standard printed test pages, which have uniform color [1]. In this paper, we design an algorithm to detect the streak defects on the customer content area, which we call a raster ROI. It is more complicated than the uniform color printed page because the customer content influences our streak detection result. Sometimes, the customer content has some dark or light straight lines along the printing process direction, and they are similar to streak defects. To detect the streak defects on the customer pages, we must separate the straight lines of customer content and the streak defects. To detect the streak in a raster ROI, we apply the Sobel edge detection algorithm and morphological operations to the master image, which includes the customer content without defects, to remove the straight customer content lines on the scanned test pages. The remaining dark or light straight line along the printing process direction may be streak defects. For the detected streak defects, we use the DAG-SVM multi-classification method to classify the rank of streak defects in the raster ROI.

Quantum image edge detection using improved Sobel mask based on NEQR

Recently, image edge detection using quantum image processing has focused due to having less circuit complexity and storage capacity. Edge extraction using the Sobel operator has the restriction in vertical and horizontal directions, which provides less edge information. In this paper, we introduce a quantum improved Sobel edge detection algorithm with non-maximum suppression and double threshold techniques for novel enhanced quantum representation method. We have analyzed the quantum circuit of realizing the edge detection algorithm, the number of edge pixels, simulation results, and circuit complexity. Thereafter, we have compared with the classical and some existing quantum edge detection algorithms. Our proposed algorithm can achieve a significant improvement in the case of edge information and circuit complexity.