Traditional Edge Detection Methods Research Articles

Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED

Automatic target recognition technology is an important research direction in the field of machine vision. Artificial ground targets, such as bridges, airports and houses, are mostly composed of straight lines. The ratio of geometric primitive lines to the triangle area formed by their combination is used as the feature quantity to describe the group of lines, so as to characterize the artificial ground target. In view of the shortcomings of traditional edge detection methods, such as background suppression, non-prominent targets, missing positions, etc., this paper proposed an image edge detection method based on depth learning. By combining the traditional edge detection algorithm with the edge detection algorithm based on an improved HED network, the real-time target image edge detection was completed. An automatic target recognition method based on template matching was proposed. This method solved the problem of both homologous template matching and heterogeneous template matching, which has important theoretical value. First, the lines were combined to form the geometric primitives of the line group, and then the relationship of the lines in the group was determined by using the characteristic quantity of the line group. The best line group matching the target template was found in the image edge, and the homonymous points in the real-time image and the target template were calculated. The affine transformation matrix between the two images was obtained according to the homonymous points, and then the accurate position of the target in the real-time image was found.

Single-pixel edge imaging with gradient Radon spectrum

Edge detection has been a fundamental component in many image segmentation and object detection systems. Recently, many researchers have implemented direct edge detection with the single-pixel imaging system. Compared with traditional edge detection methods, it does not need the procedures of acquiring the object image and then performing edge detection on the image. However, many previous works need to shift the primary illumination patterns at least twice to get the gradient information in multiple directions, which leads to the surge of the measurements. In this paper, we propose a novel single-pixel edge imaging scheme by obtaining the gradient Radon spectrum. Although the proposed scheme also needs to shift the primary illumination patterns once to obtain the gradient information of the object, compared with the Radon single-pixel imaging, no additional measurements are required. In addition, with the gradient Radon spectrum, this scheme can also directly obtain the positions of the straight edges which can be used for image correction, target positioning, and so on. The experimental and numerical results indicate that a high-quality edge image of the object can be restored by the proposed scheme, and the positions of the straight edges can be effectively located with the gradient Radon spectrum.

A point selection strategy with edge and line detection for Direct Sparse Visual Odometry

In most feature-based Visual Simultaneous Localization and Mapping systems, the pixels in a current image are compared with the correlative pixels in previous images, and the difference in the coordinates of pixels shows the movement of the camera. Different from the feature-based systems, direct methods operate on image intensity directly. Every pixel on the image or selected pixels with sufficient intensity gradient can be utilized. However, the noises in the images may affect the performance of those algorithms as the pixels are not adequately selected. In this work, we propose a new pixel selection method for a direct visual odometry system that focuses on the edge pixels. The edge pixels are usually more stable and repeatable than normal pixels. We apply the traditional edge detection method with adaptive parameters to get rough edge results. Then the edges are separated by gradient and shape. We use straightness, smoothness, length, and gradient magnitude to select the meaningful edges. We replace the pixel selection step of Direct Sparse Odometry and Direct Sparse Odometry with Loop Closure to present the evaluation on open datasets. The experimental results indicate that our method improves the performance of existing direct visual odometry systems in man-made scenes but is not suitable for pure natural scenes.

Survey of Image Edge Detection

Edge detection technology aims to identify and extract the boundary information of image pixel mutation, which is a research hotspot in the field of computer vision. This technology has been widely used in image segmentation, target detection, and other high-level image processing technologies. In recent years, considering the problems of thick image edge contour, inaccurate positioning, and poor detection accuracy, researchers have proposed a variety of edge detection algorithms based on deep learning, such as multi-scale feature fusion, codec, network reconstruction, and so on. This paper dedicates to making a comprehensive analysis and special research on the edge detection algorithms. Firstly, by classifying the multi-level structure of traditional edge detection algorithms, the theory and method of each algorithm are introduced. Secondly, through focusing on the edge detection algorithm based on deep learning, the technical difficulties, advantages of methods, and backbone network selection of each algorithm are analysed. Then, through the experiments on the BSDS500 and NYUD dataset, the performance of each algorithm is further evaluated. It can be seen that the performance of the current edge detection algorithms is close to or even beyond the human visual level. At present, there are a few comprehensive review articles on image edge detection. This paper dedicates to making a comprehensive analysis of edge detection technology and aims to offer reference and guidance for the relevant personnel to follow up easily the current developments of edge detection and to make further improvements and innovations.

Enhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation

In image processing, edge detection is a critical issue. Edge detection is a key approach for evaluating the edge of various objects in a digital image. These edges are found using the gradients, which are present in the image. The intensity and value of pixels determine the gradients. In digital images, edge detection lowers the quantity of data and filters out irrelevant data while maintaining the image's key structural features. In this paper, a new edge detection approach based on a fuzzy rule-based system is proposed. In digital image processing, the proposed method typically depends on fuzzy logic systems. The main goal of this system is to show how fuzzy logic may be used in image processing. This paper provides a fuzzy logic-based edge detection technique that uses a sharpening Gabor filter to regulate edge quality and a Gaussian filter to reduce noise caused by sharpening. This is determined by utilizing applications such as “Peak Signal to Noise Ratio (PSNR) F-Measure, and Hausdorff distance (HoD) to prove that fuzzy logic outperforms the proposed system. The findings for edge detection approaches are included in high quality. The proposed approach outperforms most commonly used traditional edge detection methods. The proposed method also reduces the number of noisy features and may be used for a wide range of images.

Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

Edge detection consists of a set of mathematical methods which identifies the points in a digital image where image brightness changes sharply. In the traditional edge detection methods such as the first-order derivative filters, it is easy to lose image information details and the second-order derivative filters are more sensitive to noise. To overcome these problems, the methods based on the fractional differential-order filters have been proposed in the literature. This paper presents the construction and implementation of the Prewitt fractional differential filter in the Asumu definition sense for SARS-COV2 image edge detection. The experiments show that these filters can avoid noise and detect rich edge details. The experimental comparison show that the proposed method outperforms some edge detection methods. In the next paper, we are planning to improve and combine the proposed filters with artificial intelligence algorithm in order to program a training system for SARS-COV2 image classification with the aim of having a supplemental medical diagnostic.

Edge Detection Algorithm of MRI Medical Image Based on Artificial Neural Network

In order to be able to extract useful information from MRI medical images, we need to carry out effective image segmentation of MRI medical image and use appropriate operators to detect the edge of the image. Firstly, this paper adopts a research method based on the characteristics of horizontal, vertical and diagonal differences, and then carries out comparative analysis on different operators from different perspectives such as frequency domain and velocity domain. After analyzing the results, it is concluded that: Canny operator can detect complete, continuous and detailed edges at a faster speed. This paper uses edge detector based on Canny operator as the training output of artificial neural network. By training artificial neural network. The optimized parameters were obtained, and subjective analysis and computational analysis were performed on the quality of edge detection results of MRI medical images based on different methods. The results show that compared with the single Sobel, Canny and other traditional operators, the edge information of MRI medical image obtained by the neural network-based edge detection method is more complete and its processing time is nearly 3 times faster than the traditional edge detection method.

An improved edge detection technique

Traditional edge detection methods tend to apply a single threshold over the entire image. However, natural images rarely have uniform illumination throughout, thus just a single threshold across the image is insufficient. This paper explores a method to recursively divide an image into regions and provide each region with an optimal threshold. For each region, we have calculated the threshold automatically using Otsu's binarisation method. The method's key goal is to reduce the effect of noise present in images, which leads to the elimination of false edges. It does this while also ensuring that true edges present within the image are not lost. We have proved that asymptotic time complexity of the proposed method is O(MNlogℓ) (where ℓ = min{M,N}). We have compared the performance of our method with the Canny edge detection technique. The Canny edge detector is a well known and widely used edge detection technique which outperforms all the classical edge detection techniques. The results show that our method outperforms the Canny edge detection technique. PSNR values for our method are much higher than that of the Canny edge detection algorithm for almost all the images considered from BSD500 benchmark dataset.

An improved edge detection technique

Traditional edge detection methods tend to apply a single threshold over the entire image. However, natural images rarely have uniform illumination throughout, thus just a single threshold across t...

Edge Detection Enhancement Based on Filtering and Threshold Estimation

An edge detection is a critical tool under image processing and computer vision. It is used for security and reliability purposes to provide enhanced information about an object and recognize the contents of the image for the applications of object recognition in computer vision. The most prominent application may be pedestrian detection, face detection, and video surveillance. Traditional edge detection method has many issues that are discussed in this paper. In this study, we enhanced the edge detection technique by applying filtering and detecting the threshold values to differentiate between different contrasts in the image. Differential operations are used to detect two adaptive thresholds on the histograms of the images. We have examined this technique on three databases Pascal, Corel, and Berkeley. The results obtained were then examined with qualitative and quantitative assessments test. Entropy, Mean Squares Error, and Peak Signal to Noise Ratio values were examined and it gave better results.

Contour detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved objects to accurately locate their center. Thus, contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In this paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithm’s performance is better than both traditional machine learning methods, such as Support Vector Machine, Extreme Learning Machine and even deep Convolutional Neural Network. The extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, and thus can be applied to contour detection for Cassini ISS images.

Image Edge Recognition of Virtual Reality Scene Based on Multi-Operator Dynamic Weight Detection

Image edge detection in virtual reality scenes is one of the most important technologies in the field of image processing and computer vision. It occupies an important position in the image processing system and is a key factor that affects the performance of the entire system. The quality of the algorithm directly affects the performance of the computer's visual system. This paper briefly describes the basic theoretical methods of image edge detection. The basic theory and processing flow of image edge detection are analyzed, and several commonly used edge detection preprocessing methods and their basic principles are elaborated in detail. The existing traditional edge detection methods are briefly introduced, and the traditional edge detection methods are compared. This paper studies the adaptive multi-scale edge detection method based on Canny algorithm, and through theoretical analysis, compares the advantages and disadvantages of various algorithms in image edge detection. In the process of acquiring image edges, the local maximum value of the modulus is calculated along the gradient direction and the threshold is adaptively selected based on the image blocking principle. Comparing with the traditional modulus maximum edge detection method, this algorithm overcomes the contradiction that can suppress the interference of noise and obtain better edge detection effect to a certain extent. Experiments show that the method in this paper not only suppresses the interference of impulsive noise to the image edge detection, but also greatly reduces the possibility of false edges, and obtains a satisfactory accurate binary edge image.

Multi-Scale Edge Detection Method for Potential Field Data Based on Two-Dimensional Variation Mode Decomposition and Mathematical Morphology

Using observational data to determine the edges of the sources is an important task in the interpretation of potential field data. Extracting the edges of deep and shallow bodies effectively is the key to correctly understanding the underground structure. Based on the good multi-scale decomposition ability of two-dimensional variational mode decomposition (2D-VMD) and the outstanding shape analysis capability of mathematical morphology (MM), a new multi-scale edge detection method for potential field data is proposed. We propose using the variance of this morphological filter as a basis for selecting the optimal structural element (SE) scale. By establishing theoretical models and comparing the results of our method with those of traditional edge detection methods, the proposed method is shown to be effective at detecting edges within potential field data. Taking the Hanmiao area of Chifeng city, Inner Mongolia, China, as an example, 1:50000 aeromagnetic data are processed and analysed by this method. The physical properties of the rocks in the study area are also discussed. The results of theoretical calculations and real data processing show that this method can accurately extract the edges of the sources at different scales. And the real data processing results show that this method is suitable for the identification of structural faults.

Fuzzy Logic Image Processing

With facial recognition software becoming more widely used, especially in mobile apps such as Snapchat, boundary detection will continue to be one of the primary areas of interest in enhancing software performance. Edge detection is paramount in discriminating objects so they can be used and processed. This paper will present a fuzzy system for boundary detection. The proposed system will then compare it to traditional methods of edge detection using MATLAB's image processing toolbox. The proposed fuzzy system allows for more effective tool since the membership functions can be more defined and robust to accommodate different images, as well as tailored to result in a more effective processed image due to ambiguity. The results of the simulation of the fuzzy system shows that it was more capable of dealing with ambiguities in the input images, so in pictures where the edges were not so clear or required more detail, the fuzzy system was more capable since it can be better tailored for use.

Fuzzy logic image processing

With facial recognition software becoming more widely used, especially in mobile apps such as Snapchat, boundary detection will continue to be one of the primary areas of interest in enhancing software performance. Edge detection is paramount in discriminating objects so they can be used and processed. This paper will present a fuzzy system for boundary detection. The proposed system will then compare it to traditional methods of edge detection using MATLAB's image processing toolbox. The proposed fuzzy system allows for more effective tool since the membership functions can be more defined and robust to accommodate different images, as well as tailored to result in a more effective processed image due to ambiguity. The results of the simulation of the fuzzy system shows that it was more capable of dealing with ambiguities in the input images, so in pictures where the edges were not so clear or required more detail, the fuzzy system was more capable since it can be better tailored for use.

Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

The traditional edge detection method is altogether inaccurate, nonadaptive, and particularly ineffective on noisy images. This paper proposes a novel edge detection algorithm based on gray entropy theory and local texture features. In the 3×3 neighborhood window, 28 comparison sequences are constructed according to local texture features. The reference sequence is composed of the median of all elements in the 3×3 neighborhood window. A total of 28 gray relation degrees as obtained by gray relation analysis between the 28 comparison sequences and reference sequences, as well as 28 gray relation degrees, are analyzed by gray entropy theory to initially filter the image. Gray entropy analysis is then performed on the comparison sequences composed of 28 texture features and reference sequences composed of the central pixel points of the filtered image to determine the maximum gray entropy difference. A comparative threshold adaptive acquisition method is designed to separate gray entropy difference sequence elements and identify all edge points accordingly. The simulation results show that the proposed algorithm effectively achieves adaptive edge detection and has strong anti-noise capability. The results of this study may provide a workable reference for edge information detection in the field of artificial intelligence (e.g., image recognition, pattern recognition applications).

Effect of morphological algorithms on medical imaging

Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment. In this article, we focus on describing the new morphological algorithms by ITK (Insight Segmentation and Registration Toolkit). These morphological operators eliminate noise, detect good edges, and overcome the drawback of traditional edge detection methods.

Optical plasma boundary reconstruction based on least squares for EAST Tokamak

Reconstructing the shape and position of plasma is an important issue in Tokamaks. Equilibrium and fitting (EFIT) code is generally used for plasma boundary reconstruction in some Tokamaks. However, this magnetic method still has some inevitable disadvantages. In this paper, we present an optical plasma boundary reconstruction algorithm. This method uses EFIT reconstruction results as the standard to create the optimally optical reconstruction. Traditional edge detection methods cannot extract a clear plasma boundary for reconstruction. Based on global contrast, we propose an edge detection algorithm to extract the plasma boundary in the image plane. Illumination in this method is robust. The extracted boundary and the boundary reconstructed by EFIT are fitted by same-order polynomials and the transformation matrix exists. To acquire this matrix without camera calibration, the extracted plasma boundary is transformed from the image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using least squares to minimize the error between the optically reconstructed result and the EFIT result. Once the transform matrix is acquired, we can optically reconstruct the plasma boundary with only an arbitrary image captured. The error between the method and EFIT is presented and the experimental results of different polynomial orders are discussed.

MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact

Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.

A Novel Method for Edge Detection in Images Based on Particle Swarm Optimization

Edges give important structural information about the images. Edge detection is a process of identifying and locating the edges in an image. Edges are the points where discontinuity of intensity occurs. It also represents the boundaries of objects in images. In this paper a new edge detection method based on Particle Swarm Optimization is discussed. The proposed method uses morphological operations and a thresholding technique to improve the result of edge detector. This algorithm performs better in images comparing to other traditional methods of edge detection. The performance of proposed method is compared with traditional edge detection methods such as Sobel, Prewitt, Laplacian of Gaussian and Canny with parameters Baddeley's Delta Metric. Statistical analysis is performed to evaluate accuracy of edge detection techniques.