Sobel Algorithm Research Articles

Research on coal-rock boundary identification based on the morphological sobel algorithm

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

Visual augmentation of live-streaming images in virtual reality to enhance teleoperation of unmanned ground vehicles

First-person view (FPV) technology in virtual reality (VR) can offer in-situ environments in which teleoperators can manipulate unmanned ground vehicles (UGVs). However, non-experts and expert robot teleoperators still have trouble controlling robots remotely in various situations. For example, obstacles are not easy to avoid when teleoperating UGVs in dim, dangerous, and difficult-to-access areas with environmental obstacles, while unstable lighting can cause teleoperators to feel stressed. To support teleoperators’ ability to operate UGVs efficiently, we adopted construction yellow and black lines from our everyday life as a standard design space and customised the Sobel algorithm to develop VR-mediated teleoperations to enhance teleoperators’ performance. Our results show that our approach can improve user performance on avoidance tasks involving static and dynamic obstacles and reduce workload demands and simulator sickness. Our results also demonstrate that with other adjustment combinations (e.g., removing the original image from edge-enhanced images with a blue filter and yellow edges), we can reduce the effect of high-exposure performance in a dark environment on operation accuracy. Our present work can serve as a solid case for using VR to mediate and enhance teleoperation operations with a wider range of applications.

PE Gas Pipeline Defect Detection Algorithm based on Improved YOLO v5

In order to improve defects detection efficiency in polyethylene (PE) gas pipelines and decrease leakage or other pipelines abnormalities in operation, this research proposed an improved YOLO(You Only Look Once) v5 detection model. First, the collected pipeline defect images were processed in grey scale, which improved the computational efficiency of the computer; then, Gamma transform and double filtering algorithms were applied respectively for image enhancement and noise reduction filtering of defects, which enhanced image quality and reduced image noise. Finally, the improved Sobel algorithm was applied to detect defective image edges and the defects in the image were segmented by adaptive threshold segmentation method to obtain binary images. The obtained binary images were employed to train the improved YOLO v5 detection model. The obtained experimental results showed that, compared with the original algorithm, the improved detection algorithm had better detection efficiency and higher robustness as well as higher recognition for common defects,improved YOLOv5 mAP and recall were 97.18% and 98.03%, respectively, the mAP has increased by 1.33% and the recall has increased by 3.83%,which can achieve the detection and identification of defect types of effects in PE gas pipes.

ASIC Design of Low Power Sobel Edge Detection Filter: An Analog Approach

This paper proposes a novel analog front end to filter edges in captured images. The proposed architecture employs the Sobel algorithm and uses a [Formula: see text] kernel to process the input images. We used 180 nm Semi-Conductor Laboratory (SCL) CMOS fabrication technology for implementation with a power supply of 1.8 V. The proposed design employs six analog differential pairs in each horizontal and vertical gradient unit. The inputs to the differential pairs are the active signals fed from the [Formula: see text] kernel using the Sobel weight masks. We connected the differential datum pairs in cascade to estimate the gradient index in horizontal/vertical direction using the net difference current between the output terminals. The horizontal and vertical gradient units, viz., [Formula: see text] and [Formula: see text], respectively, are configured based on the polarity of weights in respective Sobel masks. The gradient [Formula: see text] of the processing pixel (PP) is estimated using the net difference current in the combined circuitry employing [Formula: see text] and [Formula: see text] cells. The key advantages of the proposed design are very low area realization, high accuracy, and low power dissipation. The efficacy of the proposed edge filter analog front end is estimated through simulations and analysis with two sets of test signals in the processing window.

MACHINE WHELL EDGE DETECTION MORPHOLOGICAL OPERATIONS

One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms.

A Review Based on the Comparison between Canny Edge Detection and Sobel Algorithm

A Review Based on the Comparison between Canny Edge Detection and Sobel Algorithm

Triboelectric potential tuned oxide artificial tactile sensory platform with ultra-low power consumption

Sophistiated cognitive platform calls for multifunctional intelligent sensory devices with ultra-low power consumption. Triboelectric nanogenerator (TENG) provides an energy-efficient strategy to gate neuromorphic transistor with multiple sensing applications. To this end, a self-powered artificial tactile sensory neuron is proposed. Fish gelatin (GEL) and carboxylated-chitosan (CCs) are cross-linked to gate indium tin oxide (ITO) neuromorphic transistor. Thanks to the extremely strong protonic/electronic interfacial coupling at GEL-CCs based hydrogel/channel interface, good electrical performances are obtained at low voltage of only ∼0.8 V. The device demonstrates fundamental synaptic behaviors. Moreover, it exhibits ultra-low energy consumption of only ∼31.1 aJ for a single synaptic response with a good sensitivity of ∼4.65 dB. TENG is fabricated using polydimethylsiloxane elastomer and polytetrafluoroethylene, demonstrating good sensitivity of ∼8.0 V/kPa and good stabilities under pressure of 400 Pa. Thus, a tactile sensory platform is built with co-substrate configuration, integrating TENG and ITO neuromorphic transistor. Image edge detection is conceptually demonstrated by adopting Sobel algorithm. Furthermore, a wearable arterial pulse monitor is proposed based on the haptic sensory platform. The present work provides a candidate solution for realizing environment-friendly, energy-saving neuromorphic system and tactile perception system, demonstrating potentials in portable intelligent platform, prosthetics and human-machine interface.

Design of English video course platform based on machine learning and Sobel algorithm

Design of English video course platform based on machine learning and Sobel 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.

Identification and Classification of Defects in PE Gas Pipelines Based on VGG16

For the problem of classification and identification of defects in polyethylene (PE) gas pipelines, this paper firstly performs preliminary screening of the acquired images and acquisition efficiency of defective image acquisition was improved. Images of defective PE gas pipelines were pre-processed. Then, edge detection of the defective images was performed using the improved Sobel algorithm and an adaptive threshold segmentation method was applied to segment the defects in the pipeline images. Finally, the defect images were morphologically processed to obtain binary images. The obtained binary images were applied with VGG16 to complete the training of the defect classifier. The experimental findings show that in the TensorFlow API environment, the test set’s highest accuracy reached 97%, which can achieve the identification of defect types of underground PE gas transmission pipelines.

Height control in GMA-AM using external wire as controlling variable

ABSTRACT Gas metal arc additive manufacturing (GMA-AM) is gaining great attention to produce large-scale parts owing to its merits of low device investments and high deposition rates. Despite this fact, a robust control system for deposition height in thin-walled parts is yet to be developed due to the lack of an appropriate controlling variable. This study attempts to propose an external wire-based GMA-AM technique to solve this issue. The influences of arc current, travel speed, and external wire feed speed (EWFS) on layer geometry were studied. It is indicated that the EWFS exhibits a greater influence on the layer height of thin walls than the arc current and the travel speed. The nozzle to the top surface distance (NTSD), monitored via a vision sensor, was extracted by Median filter, Sobel algorithm, Canny algorithm, and Hough transform. The dynamics between the NTSD and the EWFS were established via a nonlinear model. A parameter self-tuning fuzzy controller was designed to control the NTSD via changing the EWFS. System simulation tests and control tests were performed to validate the robustness of the parameter self-tuning fuzzy controller.

Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images

Brain Tumors are the leading cause of cancer death in children. They are caused by the abnormal and uncontrolled growth of cells inside the brain or spinal canal. Classification of brain tumors using machine learning technology is very relevant for radiologists to confirm their analysis more effectively and quickly. Segmentation algorithm identified for detecting the tumor from the MRI brain scans need to detect shapeless tumor growth perfectly. Sobel edge detection is one of the widely used edge detection techniques in which only information along horizontal and vertical directions are considered. In this research, Sobel algorithm with 8-directional template is implemented for improving the detection of edges in brain tumor MRI images. The proposed algorithm is compared with other traditional edge detection algorithms. The performance of the proposed algorithm is analyzed in terms of MSE, RMSE, Entropy, SNR and PSNR. Analysis shows that 8-Sobel is comparatively the most suitable technique for analyzing brain tumor MRI images. Active contouring segmentation algorithm is applied on the edge detected images to verify the classification accuracy of segmented tumor.

Модификация метода выделения контуров объекта в интеллектуальных системах

This research paper focuses on the use of computer vision in intelligent systems to analyze human contours. With the growth of technology in various industries, there is a need to improve the efficiency of human-computer systems. The proposed method uses a video camera and computer software to detect a person in the image and process it using the OpenCV library and C++ programming language. The paper reviews existing human detection methods, analyzes an alternative method that uses computer vision, and develops a new method for human detection. Modifications include the use of the Kuwahar filter for image blurring and the Sobel algorithm for outline extraction. Applications for this technology include security at transportation hubs and crowded areas, remote health monitoring, enhanced control at borders and secure facilities, and interactive advertising and entertainment.

High performance and energy efficient sobel edge detection

Sobel edge detection is widely used in computer vision and image processing but its processing time becomes a serious problem in real-time environments, especially when an image is very large. Instead of utilizing a hardware-accelerated approach, we propose a purely software-based method which is simpler and cheaper. Our algorithm reduces the number of arithmetic operations and data loads, so that processing speed is increased and energy consumption reduced. The processing time is further reduced by the use of AVX intrinsics and OpenMP directives which distribute the workload among the AVX engines in a multi-core architecture. Our algorithm reduces the number of arithmetic operations by 22.73% compared to that of the state-of-the-art Sobel (SOAS) algorithm, while the number of data loads are reduced by 43.75% compared to SOAS. Performance and energy consumption comparisons between our algorithm and SOAS, as well as with the Sobel functions offered by the OpenCV and IPP libraries are investigated, and the results demonstrate that a multi-core version of our algorithm, implemented by AVX intrinsics, is on average 3.20, 9.34, and 13.99 times faster than IPP, SOAS, and OpenCV respectively. Also, it consumes an average of 2.91, 8.43, and 11.21 times less energy than IPP, SOAS, and OpenCV. Our algorithm, utilizing software modifications alone, benefits from both shorter development time and reduced cost compared to hardware approaches relying on an FPGA, ASIC, or GPU, making it more suitable for resource-constrained environments.

Research on Edge Detection of Agricultural Pest and Disease Leaf Image Based on LVQ Neural Network

Background: Roberts, Sobel, Prewitt and other operators are commonly used in image edge detection, but because of the complex background of agricultural pests and diseases, the efficiency of using these operators to detect is not ideal. Objective: To improve the accuracy of crop disease image edge detection, the method of using LVQ neural network to detect crop disease image edge was studied. Methods: It is proposed to use LVQ1 neural network to detect the edge of the image. The commonly used median feature quantity, directional information feature quantity and Krisch operator direction feature quantity are used as the input signal of LVQ1 neural network for network training. On the basis of simulation, an image feature vector that solves the image pixel neighborhood consistency is added, and an algorithm for edge detection using LVQ2 neural network is proposed. Computer simulations show that the improved algorithm significantly improved the edge image continuity of the output. Results: LVQ2 neural network can complete the edge detection of gray-scale image better, the output edge image has good continuity, clear contour and keeps most of the original image information. Compared with the LVQ1 neural network detection results, the edge image detected by LVQ2 neural network has obvious improvement in the processing of small edge, and the contour is clearer. It shows that the training method can converge the network better and obtain more ideal output results. Conclusion: The simulation comparison is carried out under the Matlab platform. The results show that based on the LVQ2 neural network, the four image feature quantities are used as the input signal detection algorithm, which significantly improved the output edge image continuity, compared with the traditional Sobel algorithm and LVQ1 nerve. The network is more superior, robust and generalized.

Research on image segmentation and edge detection technology based on computer vision

With the continuous development of computer vision technology, image segmentation technology and edge detection technology are becoming more and more mature. Firstly, this paper simulates the fixed threshold method and adaptive threshold method in image segmentation, and concludes that the fixed threshold method is applicable to images with obvious targets and backgrounds, and the adaptive threshold method is applicable to images with uneven distribution of light and dark, and secondly, the comparative experiment and result evaluation of edge detection of images by Sobel operator and Canny operator are used to conclude that the Sobel algorithm is more accurate in positioning edges, while Canny algorithm image edge information is more continuous and single edge response is better. Its edge detection is also better.

Research and Implementation of Digital Microscope Image Acquisition System Based on Embedded Linux

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the rapid development of multimedia and Internet in today's society, embedded Linux system also involves many places, and the application of digital microscope is also reused in many industrial manufacturing areas. The main purpose of this paper is to complete the research and implementation of digital microscope image acquisition system based on Embedded Linux, including image acquisition card, logic function, hardware circuit design and image acquisition system. In this paper, the CMOS digital camera is used to connect the CMOS digital camera with the computer through the transmission network, so as to collect the data. In the image preprocessing, the adaptive method based on mean and variance is implemented to correct the gray level of the collected image to avoid high error. In the image registration, the registration algorithm is used to smooth the mosaic place. Then, Sobel algorithm is used to carry out the color transition step by step to eliminate the gap in the image and realize the microscopic image mosaic. The results are as follows: the overall design scheme of the system is designed; the circuit board of digital microscope image acquisition and processing unit based on Embedded Linux is realized; image mosaic is realized by using image preprocessing, registration algorithm and fusion technology, In order to improve the quality of digital microscope image acquisition; through the feasibility test of the system, it is concluded that the performance of the embedded Linux digital microscope image acquisition system proposed in this paper is good, which is worthy of wide application.</span>

Implementation of Real-Time Edge Detection Using Canny and Sobel Algorithms

The process of performing tasks related to image processing requires the use of potential techniques such as edge detection. The technique can be used to accomplish tasks related to video and image manipulation. The process of edge detection is very virtual as it acts as the first phase of image analysis and understanding. In this paper, the edges of real-time images were detected for easy extraction of meaningful information from images. A prototype was implemented on OpenCV which is an in-built function cv2. () using python. Canny and Sobel edge detection algorithms are chosen to be used in this paper for comparison purposes to find out which method is better at edge detection. After using both algorithms to detect edges of real-time images, the result showed that the Canny algorithm produced thick edges compared to the Sobel algorithm. The canny algorithm follows the criteria of; Good detection, Good localization, and Minimal response, to output good edge detection results More so, canny uses a double threshold for edge revelation and applies the Gaussian filter which removes of any noise from an image, unlike Sobel which is not resistant to noise. The developed prototype for this study can detect edges of real-time objects without the intervention of any sensor input.

A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network

Osteoporosis, which is a common disorder associated with low bone mineral density (BMD), is one of the primary reasons for hip fracture. It not only limits mobility, but also makes the patient suffer from pain. Unlike traditional methods, which require both expensive equipment and long scanning times, this study aims to develop a novel technique employing a convolutional neural network (CNN) directly on radiographs of the hips to evaluate BMD. To construct the dataset, X-ray photographs of lower limbs and dual-energy X-ray absorptiometry (DXA) results of the hips of patients were collected. The core of this research is a deep learning-based model that was trained using the pre-processed X-rays images of 510 hips as the input data and the BMD values obtained from DXA as the standard reference. To improve performance quality, the radiographs of the hips were processed with a Sobel algorithm to extract the gradient magnitude maps, and an ensemble artificial neural network which analyses the outputs of CNN models corresponding to three Singh sites and biological parameters was utilized. The superior performance of the proposed method was confirmed by the high correlation coefficient of 0.8075 (p<0.0001) of the BMD measured by DXA in a total of 150 testing cases, with only 0.12 s required for applying the computing configuration to a single X-ray image.

RETRACTED: Design of english video learning platform based on FPGA system and sobel algorithm

In order to improve the system performance of image processing and improve the efficiency of image processing, we can use some hardware running image processing algorithms. in practice, we introduce some reconfigurable devices and more advanced programming languages to use FPGA for image processing. following will briefly introduce the design of FPGA in the edge detection system.The design has integrated a 32-bit soft RISC processor as dedicated hardware peripheral micro development and EDK embedded system developed by the system generator. Input is detected from a real-time image obtained from the CMOS camera displayed on the DVI display on the screen.