Sobel Edge Detection Operator Research Articles

FPGA-Based Improved Sobel Operator Edge Detection

Image edge detection is an important field in image processing, with its primary goal being to identify edge information of various objects in images. Traditional edge detection programs are mostly executed serially, resulting in low detection efficiency. This paper proposes an FPGA-based improved Sobel operator edge detection method, harnessing the parallel processing capability of FPGA to enhance detection efficiency. Building upon the Sobel operator in edge detection algorithms, this method increases the convolution calculation dimensions of the Sobel operator, expanding it from 2 directions to 8 directions for more precise edge information. Images captured by OV5640 are processed within the FPGA and displayed via VGA. Compared to other edge detection algorithms, the FPGA-based improved Sobel operator edge detection method efficiently and rapidly detects image edges, ensuring stable image display and accurate edge information.

Detection of crack defects in carbon fiber composites using passive infrared thermography

Aiming at the problem of low detection accuracy in detection of damage location, This article proposes a method to reduce the local jitter in passive infrared thermal imaging by using wavelet hierarchic hard threshold denoising based on template convolution, and thresholds are defined using an unsupervised Kmeans classification algorithm, the traditional SAD template convolution matching algorithm and the Sobel operator edge detection are improved. The denoizing algorithm achieves improved the denoising strength, the smoothness, and effectively extracts the crack defect features. To increase the effectiveness of nondestructive examination, the rapid detection method of temperature data matching is utilized before the infrared detection experiment, and the damage position was marked on the normal sample to allow for intuitive assessment of the damage location. The results show that by matching temperature data, the damage degree of the specimen can be quickly determined, and that the damage features can be accurately extracted using improved image processing algorithm.

A Novel Defect Segmentation for Pulse Infrared Images Based on Improved Fuzzy C-Means Algorithm With Weighted Distance

Pulsed infrared nondestructive testing (NDT) technology can effectively identify defects in engineering materials or structures. However, the original infrared images have the problems of low signal-to-noise ratio (SNR) and blurred edges. A novel defect segmentation method for pulsed infrared images based on improved fuzzy C-means algorithm with weighted distance is proposed. First, the original infrared images are denoised by wavelet transform. Second, an improved fuzzy C-means algorithm based on weighted distance (WDFCM) is proposed to cluster the infrared images. Afterward, the maximum between-cluster variance method (OTSU) is used to automatically select the thresholds and the binarization is performed to extract defect features. The Sobel edge detection operator is used to completely extract the structural properties of the defect regions. Finally, an experimental platform for pulsed infrared thermal imaging was built with two kinds of materials: flexible pipes and steel structures. The method proposed in this article was used for image segmentation subsequently. The results verify that the performance of the proposed method is optimal. The accuracy of defect detection can reach 98.95%, which is 16.82% higher than the original method.

Image Processing and Feature Extraction for Hull Structure GMAW Based on Weld Pool Visual Sensing

Image processing and feature information extraction based on the visual perception of the weld pool are considered essential components of intelligent welding quality monitoring of hull structure gas metal arc welding (GMAW). The unstable characteristics, such as large spatter, much smoke, and strong arc light during hull structure GMAW, lead to the blurring of image acquisition and the difficulty of contour extraction of the weld pool. The present study is aimed at addressing the practical issues from two perspectives, i.e., a spectrum‐visual‐sensing acquisition system and an image‐processing and feature extraction algorithm. First of all, by analyzing the light energy distribution law and acquiring the optical parameters relevant to the cut‐off composite dimming and near‐infrared narrowband filtering, spectral sensing is employed in establishing models of arc light radiation to detect the strength of continuously distinctive spectral lines. Besides, an appropriate high‐speed charge‐coupled device (CCD) camera is selected to build a visual acquisition system, which can reduce the external interference of the arc light on the image acquisition of the weld pool. Afterwards, the implementation of an image‐processing fusion model based on the spatial information fuzzy C‐means (FCM) clustering analysis and Sobel edge detection operator accompanies the investigation of the geometric aspects of the weld pool image. In terms of clear segmentation of the interest region, the edge detection and accurate extraction of the target contour are successfully obtained. In the subsequent section, the Hough transform analysis is adopted to establish the geometric feature extraction model of the weld pool, with corner detection, conversion, and camera calibration as the core technology. Additionally, the left and right views of the image contour are calibrated to achieve the lossless conversion of corner pixels and physical coordinates. Finally, three other distinct image‐processing methods are designed to compare the segmentation effect of the edge contour with the fusion model, and then, the extraction accuracy of the geometric features of the weld pool is verified. The interference of the arc light and smoke has been demonstrated to be substantially diminished, which is attributable to the visual‐sensing system during image acquisition of the weld pool. The results of edge fusion of the weld pool image show that based on the GMAW using the FCM‐Sobel fusion method, a superior extraction accuracy of geometric features characterized by smoothness, continuousness, no breakpoints, and less noise has fulfilled the engineering requirements.

UAV Target Detection under Complex Sky Background

<p class="15">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.</p>

Non-stationary random noise removal in ground-penetrating radar images by using self-guided filtering

Numerous high-computational-complexity algorithms have been proposed for random noise removal in ground-penetrating radar (GPR) images, which are not suitable for real-time applications. Further, most of them ignore the non-stationarity of random noise in GPR images, resulting in a limited scope for their applications. To address this issue, a self-guided filter is proposed in this study for removing non-stationary random noise in GPR images. The proposed method consists of two steps: prefiltering and final filtering. In prefiltering, the original noisy GPR image is used as the guidance and input image for guided filtering, which employs the proposed time-varying regularization parameter for obtaining the prefiltering result. In final filtering, the prefiltering result and the original noisy GPR image are used as the guidance and input image, respectively, for the proposed guided filtering combined with edge information. The edge information is obtained by using the Sobel edge detection operator to extract the prefiltering result. The experimental results demonstrate that the proposed method effectively removes the non-stationary random noise in GPR images, and it is suitable for real-time applications.

Extract nanoporous gold ligaments from SEM images by combining fully convolutional network and Sobel operator edge detection algorithm

In order to quantitatively analyze the nanoporous gold (NPG) sponge-like structure, we combined Fully Convolutional Network (FCN) and Sobel operator edge detection algorithm to extract NPG ligament accurately. The image datasets of NPG morphology were acquired by scanning electron microscope (SEM). FCN was used to identify the ligaments and pores in SEM images preliminary. In addition, we further used Sobel operator edge detection algorithm to determine the ligament boundary precisely. This proposed method provides a foundation for analyzing the structural geometry and microstructure characteristics of NPG. Furthermore, the obtained ligament width distributions were used to characterize the NPG structure.

Tunnel Lining Crack Recognition Based on Improved Multiscale Retinex and Sobel Edge Detection

China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.

Development of GUI for Malaysian herbs plant image identification

In the previous era, sickness and diseases were treated using traditional herbs since medical technology had yet to be developed. Due to massive development in medicine, more herbs species are facing the risk of extinction due to not being well conserved. This paper describes the development of a graphical user interface (GUI) for identification of Malaysian Herbs Plant from image data using MATLAB. Besides, the GUI development will contribute to the database development of herbs species that are much needed to systematically stores relevant information. It can provide valuable knowledge for society. It is quite challenging for people including the herbs’ expert, to differentiate herbs’ plants since there are too many of them and limited knowledge and experiences. Fortunately, this can be overcome by using image processing approaches specifically image segmentation and classification. A set of image data consists of five types of Malaysian herbs plants is used in this study. The types are Rerama, Sirih, Mexican Mint, Belalai Gajah dan Senduduk. Sobel edge detection operator is a segmentation approach that works by computing the gradient of image intensity at each point and measuring the pixel value changes in horizontal and vertical directions by using two convolution masks. The shape and texture features are used in multiclass Support Vector Machine (SVM) model to classify the species of the herbs. Results show that performance of model are 95.56% for accuracy and sensitivity, 98.89% for specificity, 95.78% for precision, and 94.53% for Matthews correlation. The demonstration of image data shows the effectiveness of the developed GUI in classifying the five types of herbs.

Extract Nanoporous Gold Ligaments from SEM Images by Combining Fully Convolutional Network and Sobel Operator Edge Detection Algorithm

Extract Nanoporous Gold Ligaments from SEM Images by Combining Fully Convolutional Network and Sobel Operator Edge Detection Algorithm

Comparative Analysis of Extraction Methods of Coniferous Sheet Cell Contour

Zhu, M.; Zhao, L., and Ren, H.E., 2020. Comparative analysis of extraction methods of coniferous sheet cell contour. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 492–496. Coconut Creek (Florida), ISSN 0749-0208.Plank contour extraction is the precondition of plank material identification work, accurate extraction of contour can improve the precision of the plank material identification, the experiment selected pinus massoniana plate cell image, using chain code method, Roberts, Sobel edge detection operator edge detection operator, Prewitt edge detection operator, Krisch edge detection operator, gauss Laplace operator control experiment analysis, experiment results show that the contour extraction method of Krisch edge detection operator on soft wood cell contour extraction effect is ideal, enhance the credibility of the subsequent experiment plank cell recognition.

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

There are numerous studies on brain imaging applications. The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor. Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues. Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor. However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance. They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients. Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations. The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS). The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.

Real Time Sobel Edge Detection Based on Reconfigurable Computing

Edge detection is a fundamental operation in many image and video processing applications. It is used in various fields like industries, aerospace, surveillance, medical fields, traffic monitoring system, lane detection, driverless vehicles, crack detection in roads and several other applications. Most of the edge detection algorithms are software based but in real time applications these are not efficient hence in this paper we have explored about Hardware platform. The reason for selecting Sobel edge detection operator is it incorporates both the edge detection and a smoothing operator to provide good edge detection capability in noisy environment. This design uses Verilog HDL language for design and Vivado is used for simulation.

Research on edge detection algorithm based on improved sobel operator

In order to solve the shortcomings of traditional Sobel edge detection operator, such as low accuracy of image edge location and rough edge extracted, an improved edge detection algorithm based on Sobel operator is proposed. Firstly, in the aspect of the accuracy of edge detection, the direction template is increased from two directions of horizontal and vertical to eight directions; secondly, in terms of the rough edge extracted and the noise sensitivity, combined with the operation of mathematical morphology, the edge can be refined, and at the same time, some part of the noise can be suppressed. The experiment results show that the improved edge detection algorithm has a better accuracy in image positioning, and a certain ability to suppress noise. Finally, the detected image edge of the image is more delicate and clear than the traditional Sobel operator.

Radar remote sensing image retrieval algorithm based on improved Sobel operator

Aiming at the time-consuming problem caused by large computational load of radar image retrieval, based on blocking histogram, Sobel edge detection operator and gray level co-occurrence matrix (GLCCM), new radar remote sensing image retrieval algorithm based on improved Sobel operator is proposed. Firstly, the Sobel edge detection algorithm is used to process the image, the edge image is acquired, the radar remote sensing image is analyzed from different angles, and then the different radar remote sensing images are transformed. Then, based on the above processing, Radar Remote Sensing Image Retrieval Algorithm is acquired; finally, the plurality of statistic of the matrix is recorded as a feature vector describing the radar image, and the image is retrieved according to the feature vector of the radar image. Through a large number of experiments, Radar Remote Sensing Image Retrieval algorithm can greatly reduce the retrieval time, and it also has a good retrieval effect for images with rich texture.

A design of license plate recognition system using convolutional neural network

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

Multi-stream CNN for facial expression recognition in limited training data

Limited data is a challenging problem to train Convolutional Neural Networks. On the other hand, acquiring a database in a demanded scale is not a straightforward task. In this paper, handcrafted features along with a multi-stream structure are proposed as a solution to improve performance of limited data via CNN. Three handcrafted features using local binary pattern code extractor and Sobel edge detection operator in horizontal and vertical directions of images have been extracted to apply to the multi-stream CNN model. Our model is based on two distinct structures including three-stream and single-stream structures. The three-stream structure can be employed to improve the recognition rate in facial expression classifiers when the training data is limited. In three-stream structure, each of information channels will be added to distinct streams separately. Furthermore, the transfer learning technique employed and behaviour of VGG16 architecture trained with limited data have been studied to be compared with the proposed method. In addition, input data is expanded by means of rotation, cropping, and flipping. Next, three-stream and single-stream structures are examined while using limited and also expanded training data. We have evaluated the mentioned system in order to compare it with state of the arts for CK+ and MUG databases in both limited-data and expanded-data. The results indicate that by using limited-data, recognition accuracy will be improved through the mentioned strategy. (92.19 to 88.95 in CK+ database and 85.4 to 82.5 in MUG database). Additionally, the performance was improved in comparison with benchmark methods.

Remote Sensing Image Enhancement Via Edge-Preserving Multiscale Retinex

Recently, an image enhancement receives a considerable research attention due to its wide application. In this paper, we propose a novel algorithm to enhance the contrast of remote sensing image while avoiding color distortion and detail loss. First, based on the Sobel operator strength, the local adaptive weighting parameter is obtained to penalize the fixed regularization parameter of original guided filtering. Then, the grayscale difference between flat patch and edge patch, which is caused by the fixed regularization parameter is amplified meanwhile without sacrificing the denoising performance. Finally, the Gaussian filtering of original multiscale retinex with color restoration is replaced with the guided filtering based on the Sobel edge detection operator to estimate the incident component of an image. Comparing with the previous algorithms, the proposed one can obtain more edge details and better color fidelity but with lower computational complexity and higher efficiency.

Retraction: Xiaohong Zhu, Lihe Zhou, Zili Yang and Joyati Debnath, A new text information extraction algorithm of video image under multimedia environment

At present, under the multimedia environment, the accuracy of the text information extraction of video images is poor. Moreover, the extraction process is complicated. In this paper, a text information extraction algorithm for video image based on time adaptive model is proposed. Using Sobel edge detection operator, text edge of the image is extracted. Using OSTU algorithm. the adaptive threshold is obtained, to make global binary processing smooth. Through selective masking smoothing, some irrelevant background is then filtered out. By morphological method, the text area is then merged. Finally, using the connected domain, the text area is extracted, to get the text block, and obtain the text information of the video image. On this basis, according to the color feature of text in video images, through user interaction, the learning process of color online machine based on the generated time and with adaptive model can be obtained to detect the same caption in the video sequence, so as to realize the text information extraction algorithm for video image in multimedia environment. The experimental results show that the proposed algorithm can accurately extract the text information, and the speed of extraction is faster.

Hybrid learning vector quantization (LVQ) algorithm on face recognition using webcam

In this research, hybridization of Learning Vector Quantization (LVQ) algorithm with Self Organizing Kohonen for face recognition using webcam. Hybrid technique performed is Kohonen algorithm used for initial weighting and the result of weighting inserted into LVQ algorithm to get result of training in the form of final weight used for face recognition. The data used in this study is the face of a digital image of the acquisition with a digital tool make use of camera that will be used for learning (learning data set) and a set of images for testing (testing data set).The first step of the process the face image (preprocessing) used for prepare the input face image to be input into network. The preprocessing step in this research is divided into four step like Image Readings, Grayscaling, Sobel operator edge detection and binaryization. The result of this test is percentage of face recognition success with LVQ algorithm is 57.03%, Kohonen is 52.59% and Hybrid is 68.88%.While the average time of the introduction process is for LVQ of 2.64 seconds, Kohonen of 2.61 seconds and Hybrid 2.59 seconds. From the above results can be concluded that in terms of accuracy Hybrid algorithm slightly superior to the Kohonen is 16.29% and in terms of time Hybrid algorithm faster than LVQ algorithm of 0.05 seconds and Kohonen an average of 0.02 seconds.