Image Edge Detection Method Research Articles

Retracted: Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

Retracted: Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

Research on Image Processing and Edge Detection Methods for Ice-covered Cables and Ropes

Research on Image Processing and Edge Detection Methods for Ice-covered Cables and Ropes

Seasonal potential fishing zone model in the regional fisheries management of Indonesia (WPP-RI) 716 based on remote sensing satellite data

The variability of atmosphere-ocean interactions highly influences the distribution of fishing zones in the surrounding Indonesia Through Flow (ITF) channel, such as the WPP-716 region. Therefore, the seasonal determination of the fishing zone is crucial in this zone as one of the regional fisheries managements in Indonesia. This study aims to create a potential fishing zone (PFZ) model in the Regional Fisheries Management of Indonesia (WPP-RI) 716 based on the remote sensing satellite data. This study used the daily SST data extracted from the Suomi-National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (SNPP-VIIRS) image Level 2 from 2018 to 2019. A Single Image Edge Detection (SIED) method is applied to determine the PFZ points. Supporting data, such as sea current patterns, chlorophyll-a, fish-catching zones and Catch per Unit of Effort (CpUE), are also considered to bolster this study's findings. The models were validated and compared with the results established by central authority fish catch data. Of particular concern, the highest detection of PFZ is observed during the second transitional season, reaching 1835 points scattered all over the study area. The frequently detected PFZ is identified in northern Sulawesi and Sangihe Islands, where the circular eddy current observed during the second transitional season, ranging from 0.6 to 0.8 m/s, indicating upwelling (high productivity) events proven by the variability of surface temperature and chlorophyll-a and aligned with the high value of CpUE (ranging from 20 to 28 ​kg/GT/day). The developed model is expected to be a basis for further decision-making regarding fishery resources management in the study area.

Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

Image edge detection plays a crucial role in image analysis and recognition. However, when dealing with color images captured by unmanned aerial vehicles (UAVs), there are certain limitations, such as large operations, multiple noise sources, easy distortion, and missing information in edge detection. To address these shortcomings, this study proposes a UAV color image edge detection method based on an enhanced fireworks algorithm. In this method, the color image pixels of the UAV are represented using quaternions. The explosion amplitude formula of the fireworks is divided into two categories based on the mean value of the number of fireworks explosions. For each category, an explosion formula is proposed, and the explosion mutation operator of the fireworks algorithm is improved accordingly. By applying the proposed algorithm, the preliminary edges of a UAV color image are obtained. Additionally, a novel approach for color image edge refinement is introduced. This approach involves classifying the edge points based on their degree of attachment, which leads to the formation of the edges in a UAV color image. Experimental results demonstrate that the algorithm proposed in this study offers several advantages, including fast calculation, strong denoising capability, and high-quality edge detection.

A Novel Edge Detection Method for Multi-Temporal PolSAR Images Based on the SIRV Model and a SDAN-Based 3D Gaussian-like Kernel

Edge detection for PolSAR images has demonstrated its importance in various applications such as segmentation and classification. Although there are many edge detectors which have demonstrated an impressive ability to achieve accurate edge detection results, these methods only focus on edge detection in a single-date PolSAR image. However, a single-date PolSAR image cannot fully characterize the changes in scattering mechanisms of land cover in different growth cycles, resulting in some omissions of the true edges. In this paper, we propose a novel edge detection method for multi-temporal PolSAR images based on the SIRV model and an SDAN-based 3D Gaussian-like kernel. The spherically invariant random vector (SIRV) and span-driven adaptive neighborhood (SDAN) improve the estimation accuracy of the average covariance matrix (ACM) in terms of data representation and spatial support, respectively. We propose an SDAN-based 2D Gaussian kernel to accurately extract the edge strength of single-date PolSAR images. Then, we design a 1D convolution kernel in the temporal dimension to smooth fluctuations in the edge strength of multi-temporal PolSAR images. The SDAN-based 2D Gaussian kernels in the X- and Y-directions are combined with the 1D convolution kernel in the Z-direction to form an SDAN-based 3D Gaussian-like kernel. In addition, we design an adaptive hysteresis threshold method to optimize the edge map. The performance of our proposed method is presented and analyzed on two real multi-temporal PolSAR datasets, and the results demonstrate that the proposed edge detector achieves a better performance than other edge detectors, particularly for crop regions with time-varying scattering mechanisms.

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.

Geo‐information mapping improves Canny edge detection method

AbstractAiming at the shortcomings of the current Canny edge detection method in terms of noise removal, threshold setting, and edge recognition, this paper proposes a method for improving Canny edge detection by geo‐information mapping. The shortcomings of the traditional Canny edge detection method are analyzed by using the Canny optimal criterion and Tobler's First Law, which points out the direction of edge detection optimization by using the difference between edge properties and noise properties. The property characteristics and spatial distribution rules of edge points and edge lines are inspected using the geographic information mapping theory and technical methods, and edge identification criteria are defined at two levels of edge points and edge lines. Finally, the method model of improved Canny edge detection is constructed by combining guided filtering. The experimental results show that the improved edge detection method has the advantages of enriched edge details, accurate edge recognition, and strong self‐adaptive capability. This is a new attempt of geo‐information mapping theory and technical method in image edge detection, which has certain theoretical significance and strong practical guidance.

All-optical image edge detection based on the two-dimensional photonic spin Hall effect in anisotropic metamaterial.

Optical image processing based on the photonic spin Hall effect (SHE) has been gaining significant attention as a convenient and an accurate way for image edge detection. However, the recent edge imaging techniques depending on optical differentiation is mainly achieved by modulation of one-dimensional photonic SHE. Here, we theoretically predict the two-dimensional photonic SHE in the anisotropic metamaterial, and find that its longitudinal and transverse displacements exhibit spin-dependent property at filling factors with increasing incidence angle. As the transverse and in-plane displacements induced by the photonic SHE can be controlled by the filling factor of the crystal structure, the optical axis angle, and the incident angle, this intrinsical effect can be used to realize a tunable edge imaging. Interestingly, by changing the optical axis of the anisotropic metamaterial, the in-plane displacements are equal to the transverse displacements for a certain filling factor and the corresponding incident angle. Therefore, we propose a two-dimensional image edge detection method based on the photonic SHE in anisotropic metamaterial. Further numerical results validate the theoretical proposal.

Color Image Edge Detection Method Based on the Improved Whale Optimization Algorithm

A high level of computation is required for edge detection in color images captured by unmanned aerial vehicles (UAVs) to address issues, such as noise, distortion, and information loss. Thus, an edge detection method for UAV-captured color images based on the improved whale optimization algorithm (WOA) is proposed in this study. In this method, the color image pixels are represented by quaternions, and the global random position variables and information exchange mechanism are introduced into the random walk foraging formula of the WOA. Further, a random disturbance factor is also introduced into the predator-prey formula of the spiral bubble net. The proposed improved WOA is then used to obtain the preliminary edge of the UAV-captured color image. An edge-point classification method using the radius of the shortest distance between the whale and the current global optimum in each iteration is presented to enhance a preliminary edge. The experimental results show that the proposed edge detection method has the advantages of strong denoising, fast speed, and good quality.

A Novel Image Edge Detection Method Based on the Asymmetric STDP Mechanism of the Visual Path

The detection of image edges plays an important role for image processing. In view of the fact that these existing methods cannot effectively detect the edge of the image when facing the image with rich details. This paper proposes a novel method of asymmetric spike-timing-dependent plasticity (STDP) image edge detection based on the visual physiological mechanism. In the proposed method, the original image is preprocessed by the Gabor filter to simulate the visual physiological orientation characteristics to obtain the image information in different directions, and the orientation feature fusion is used to reconstruct the primary edge feature information of the image. Then, based on the mechanism of the visual nervous system, a neuron network composed of dynamic synapses based on the asymmetric STDP mechanism is constructed to further process it to obtain impulse response images. In order to eliminate disturbance of the neuron’s system noise on the impulse response image, the impulse response image is filtered by a Gaussian filter. Then, the lateral inhibition between neurons is applied to refine the filtered image edges. Finally, the result is normalized, and the final edge of the experimental image is obtained. Experimental results based on the colony image data set collected in the laboratory indicate that the proposed method achieved better performance than these state-of-the-art methods; meanwhile, the AUC value remains above 0.6.

Sensitive Ant Algorithm for Edge Detection in Medical Images

Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimization (ACO). In order to increase both the quality and the stability of image edge detection, a vector called pheromone sensitivity level, PSL, was used within ACO. Each ant in the algorithm has one assigned element from PSL, representing the ant’s sensibility to the artificial pheromone. A matrix of artificial pheromone with the edge information of the image is built during the process. Demi-contractions in terms of the mathematical admissible perturbation are also used in order to obtain feasible results. In order to enhance the edge results, post-processing with the DeNoise convolutional neural network (DnCNN) was performed. When compared with Canny edge detection and similar techniques, the sensitive ACO model was found to obtain overall better results for the tested medical images; it outperformed the Canny edge detector by 37.76%.

Edge detection of foreign matter suspension image of high voltage transmission line based on improved Canny operator

In the edge detection of foreign object hanging image of high voltage transmission line, it is easy to appear that multiple responses will appear at one image edge point, which affects the detection effect. Based on the improved Canny operator, an edge detection method for foreign matter suspension image of high voltage transmission line is designed. The collected image is preprocessed in three steps: gray processing, optical correction and noise reduction, so as to better reflect the characteristics of the original image and improve the image quality. The non-uniform distribution of potential energy of foreign body hanging image data field is used to locate the image area of foreign body hanging. The morphological filter can extract the local noise and make the image clearer. The Canny operator is improved to obtain the partial derivative of the distance measurement function and automatically update the threshold to eliminate the multi-level response. The test results show that the method in this paper is better than the image edge detection method based on Canny operator and Sobel operator in three indexes: positive detection rate, false detection rate and missed detection rate.

Distribution and transport characteristics of fine particulate matter in beijing with mobile lidar measurements from 2015 to 2018

Accurately quantifying the concentration and transport flux of atmospheric fine particulate matter (PM2.5) is vital when attempting to thoroughly identify the pollution formation mechanism. In this study, the mobile lidar measurements in Beijing on heavily polluted days in December from 2015 to 2018 are presented. The lidar was mounted on a vehicle, which could perform measurements along designated routes. On the basis of mobile lidar measurements along closed circuits of the 6th Ring Road around Beijing, the spatial distribution and transport flux of PM2.5 in Beijing were determined with information of wind field. In the spatial distribution, both the concentration and transport of PM2.5 were revealed to be more significant in the southern section of Beijing. The regional transport layer at heights < 1.3 km plays an important role in pollution formation. The maximum transport flux reached 1600 μg/(m2*sec) on 11 December 2016. With the aerosol boundary layer height determined from the image edge detection (IED) method, the inter-annual variations of the aerosol boundary layer height (ABLH) were also analysed. The ABLH decreased from 0.73 to 0.46 km during the same heavy pollution period from 2015 to 2018. Increasingly adverse aerosol boundary layer (ABL) meteorological factors, including lower ABLH, light winds, temperature inversions, and accumulated moisture, have become necessary for pollution formation in Beijing.

A novel edge detection method based on efficient gaussian binomial filter

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

Detection of Chlorophyll-a Front and Its Relation to Skipjack Tuna Catches in Makassar Strait

The purpose of this study is to detect chlorophyll-a front by using the Single Image Edge Detection (SIED) method and analyze the influence of the closest distance of chlorophyll-a front to the skipjack tuna catches.

Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model

Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

Realization of clothing image contour extraction and collar segmentation

With the change of personalized ideas, the clothing and apparel industry is developing towards small batches and multiple varieties. Looking for fast and efficient apparel image processing and recognition is the most urgent problem to be solved by apparel companies. The establishment of a collar style sample library is the prerequisite for apparel image processing and recognition research. Apparel image preprocessing is the basis of research. This article takes the clothing style map as the research object, establishes a collar style sample library, and takes a round neck T-shirt image as an example, which contains 8 types of collars commonly used in clothing, and each collar type corresponds to 60 images. The advantages and disadvantages of commonly used image graying, sharpening, edge detection, morphological processing, and image segmentation processing methods are compared and analyzed. Corresponding image preprocessing schemes have been formulated to provide new ideas for automatic pattern recognition, software development and application.

Image-based method for the angular vibration measurement of a linear array camera.

Measurements of angular vibration of aerial cameras in a variety of operating conditions are critical to analyze the performance of the vibration isolation system. Instead of using an additional optical system to measure the angular motion of the camera, an image-based and easy-to-implement method is proposed for the linear array camera to measure the image motions captured by the camera directly. The natural frequencies of the vibration isolation were also measured by laboratory vibration test. For image vibration measuring, the angular vibration is represented in image motion by the relationship between the image motion and angular motion of the camera. Based on the pushbroom imaging principle, the image motion at the edge of the foreground image of the linear object is extracted using image processing technology including image segment and edge detection methods. Then the image motion is analyzed in the time and frequency domains. The proposed method has been successfully demonstrated for the angular vibration measurement by a flight test. The results of the vibration sensors and the position and orientation system of the flight tests are also given to validate the effectiveness and accuracy of the proposed approach.

Retracted] Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

With the rapid development of the national economy, the film industry has risen rapidly under these conditions, and the development of film occupies a more important position in this process, which has led to the development of investment in the film field, an internationally recognized cultural space. This paper studies the commercial investment in the film field based on the edge detection of the digital image of the visual sensor. The purpose of the research is to point out the direction of commercial investment in the film industry by studying the effect of the edge detection of the digital image of the visual sensor on the application of the film object. The image edge detection algorithm based on wavelet transform and morphology and the application of these image edge detection algorithms such as wavelet theory analyze the shortcomings of movie, sound effects, and pictures and make a comparison before and after the improvement. Audiences, as the direct enjoyers of the cultural product of the film, their opinions, and evaluations of the film, largely determine the quality of the work. On the contrary, a good work can attract the public’s attention and bring a large impact. The data acquisition in this process is mainly based on the questionnaire survey of the audience. The experimental results show that before applying digital image edge detection to transform the clip, 7 people thought that the clip needed to be further improved to improve the quality of the film itself, and only 1 person affirmed the film; after the modification, 9 people proposed the film praise; in addition, 15 people audience watched the film processed by the edge detection method and gave high evaluations to the three aspects of sound effects, special effects, and editing, especially the editing part, with a score of 8.9.

Image Edge Detection with Fuzzy Ant Colony Optimization Algorithm

Searching and optimizing by using collective intelligence are known as highly efficient methods that can be used to solve complex engineering problems. Ant colony optimization algorithm (ACO) is based on collective intelligence inspired by ants' behavior in finding the best path in search of food. In this paper, the ACO algorithm is used for image edge detection. A fuzzy-based system is proposed to increase the dynamics and speed of the proposed method. This system controls the amount of pheromone and distance. Thus, instead of considering constant values for the parameters of the algorithm, variable values are used to make the search space more accurate and reasonable. The fuzzy ant colony optimization algorithm is applied on several images to illustrate the performance of the proposed algorithm. The obtained results show better quality in extracting edge pixels by the proposed method compared to several image edge detection methods. The improvement of the proposed method is shown quantitatively by the investigation of the time and entropy of conventional methods and previous works. Also, the robustness of the proposed method is demonstrated against additive noise.