Image Edge Detection Method Research Articles

PEMETAAN POTENSI PENANGKAPAN IKAN MENGGUNAKAN METODE SINGLE IMAGE (STUDI KASUS: PERAIRAN SELATAN JAWA)

Fishing Potential (PPI) is needed to increase fisheries productivity. Traditionally, fishermen use their senses and hereditary habits to determine PPI. Traditionally, fishermen use the five senses and hereditary habits to determine PPI. This method, in addition to being less efficient, can also lead to over-exploitation if fishing is carried out in the same place continuously. The existence of remote sensing technology can be used to determine PPI faster while considering aspects of approval and spatial distribution. One method of determining PPI is based on remote sensing data, mapping the thermal front area first before determining the PPI through manual interpretation based on pixel located between warm and cold water. Manual interpretation has limitations, it depends on humans and takes a long time. To overcome these limitations, in this study, the Single Image Edge Detection (SIED) method developed by Cayulla and Cornillon was applied in the form of a toolbox thus it could be used automatically. Sea Surface Temperature (SPL) data obtained from MODIS Level 2 (6 April 2018) was subsequently processed using this toolbox and successfully developed 22 PPI points in the southern part of Java Island. Meanwhile, PPI detection using the Temperature Gradient Analysis (TGA) method which is done manually produces 28 points. There are 20 locations of the same point using these two methods. Using the SIED method based on the toolbox can improve data, reduce human error with accuracy 64%.

Optimal imaging of multi-channel EEG features based on a novel clustering technique for driver fatigue detection

Fatigue may cause a decrease in mental and physical performance capacity, which is a serious safety risk for the drivers in the transportation system. Recently, various studies have demonstrated the deviations of electroencephalogram (EEG) indicators from normal vigilant state during fatigue in time and frequency domains. However, when considering spatial information, these feature descriptors are not satisfying the demand for reliable detection due to the well-known challenge of signal mixing. In this paper, we propose a novel approach based on clustering on brain networks (CBNs) to alleviate the problem to improve the performance of driver fatigue detection. The clustering algorithm was employed to extract the spatial nodes with distinct connectivity attributes throughout the EEG-based brain networks. Then, the temporal features of wavelet entropy from the extracted nodes were transformed to spatio-temporal images so that the image edge detection method (pulse-coupled neural networks) to distinguish different stages of fatigue can be used. The experimental results demonstrated the temporal features from the extracted nodes reduced signal mixing and showed clearer deviations. The detected fatigue based on the imaging method was to an extent consistent with self-reported subjective feelings and most of the critical fatigue was detected before the subjective feelings of fatigue. For all the subjects, 21 of 29 accidents happened after detected fatigue in the simulated driving task. Therefore, the proposed method owns potential value for early warning and avoidance of traffic accidents caused by driver fatigue.

Comparison of image edge detection methods on potholes road images

Dinas pekerjaan Umum (DPU) in Surabaya in carrying out road repairs, especially in potholes, must know the position, area and depth. So it is important to do research to find out the surface area of potholes. To find out the surface area of the hole, the edge detection process is done first. Edge detection functions to get the edge of an object. Edge detection is obtained by utilizing a drastic change in the intensity value at the boundary of two areas. In this study, to determine the edge of an object (potholes road) by comparing three edge detection methods consisting of the Frei-Chen, Laplacian and Laplacian of Gaussian (LoG) methods. From the results of testing the Laplacian of Gaussian method has an average value of accuracy of 67%, sensitivity of 81.97% and specificity of 65.16%.The measurement results obtained by the Lapalacian of Gaussian method which is the best method for edge detection in potholes road images, because it produces the highest accuracy, sensitivity and specificity.

Edge Detection Using the Bhattacharyya Distance with Adjustable Block Space

In this paper, we propose a new edge detection method for color images, based on the Bhattacharyya distance with adjustable block space. First, the Wiener filter was used to remove the noise as pre-processing. To calculate the Bhattacharyya distance, a pair of blocks were extracted for each pixel. To detect subtle edges, we adjusted the block space. The mean vector and covariance matrix were computed from each block. Using the mean vectors and covariance matrices, we computed the Bhattacharyya distance, which was used to detect edges. By adjusting the block space, we were able to detect weak edges, which other edge detections failed to detect. Experimental results show promising results compared to some existing edge detection methods.

Research on Target Deviation Measurement of Projectile Based on Shadow Imaging Method in Laser Screen Velocity Measuring System.

In the laser screen velocity measuring (LSVM) system, there is a deviation in the consistency of the optoelectronic response between the start light screen and the stop light screen. When the projectile passes through the light screen, the projectile’s over-target position, at which the timing pulse of the LSVM system is triggered, deviates from the actual position of the light screen (i.e., the target deviation). Therefore, it brings errors to the measurement of the projectile’s velocity, which has become a bottleneck, affecting the construction of a higher precision optoelectronic velocity measuring system. To solve this problem, this paper proposes a method based on high-speed shadow imaging to measure the projectile’s target deviation, ΔS, when the LSVM system triggers the timing pulse. The infrared pulse laser is collimated by the combination of the aspherical lens to form a parallel laser source that is used as the light source of the system. When the projectile passes through the light screen, the projectile’s over-target signal is processed by the specially designed trigger circuit. It uses the rising and falling edges of this signal to trigger the camera and pulsed laser source, respectively, to ensure that the projectile’s over-target image is adequately exposed. By capturing the images of the light screen of the LSVM system and the over-target projectile separately, this method of image edge detection was used to calculate the target deviation, and this value was used to correct the target distance of the LSVM to improve the accuracy of the measurement of the projectile’s velocity.

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.

Automatic wavefront reconstruction on single interferogram with spatial carrier frequency using Fourier transform

Optical interferometry can be applied to obtain the wavefront phase and reconstruct the three-dimensional wavefront by analyzing interference fringes. Single interferogram analysis based on two-dimensional Fourier transform is proposed. This method just needs to acquire and analyze single interferogram to solve the wavefront phase. Compared with phase-shift method, Single interferogram analysis can reduce experiment cost and the limitation of measuring environment like vibration, airflow and noise, and meet the requirement of real-time and dynamic interferometry. Because the wavefront phase information is included in the carrier frequency components of single interferogram, an image edge detection method is used to automatically extract the center and boundary of the first-order carrier component of the spatial spectrum of the interferogram in the frequency domain.

Demonstration of Quantum Image Edge Extration Enhancement Through Improved Sobel Operator

Quantum image processing is a system where both quantum computing and image processings are key. Now it is required to investigate how to apply image processing concepts such as image edge detection and improved Sobel operator, to quantum image processing. Just as traditional image processing helps to image manipulation, our efforts in quantum image processing contribute to the further development of quantum algorithm and quantum information theory. Edge detection is an important problem in traditional image processing, and image edge detection algorithm can filter image edge features and retain important attributes. We proposed a quantum edge detection algorithm, which employs improved Sobel operator to improve the performance, so as to solve the problem of unsatisfactory traditional image edge detection methods. In practical application, the amount of image data to be processed increases sharply, and the computing power of classical computer becomes a limitation. Quantum information processing can effectively accelerate many classical problems by virtue of quantum mechanical characteristics, such as quantum superposition, entanglement, parallelism. Our proposed method uses the novel enhanced quantum representation to store quantum images, which stores all the pixels in the image in a superimposed state, realizing parallel computation. The improved eight-direction Sobel operator is used to calculate the gray gradient, and the quantum circuit is designed to realize quantum edge detection. Simulation results have shown that our algorithm can extract edge with dimension $2^{n}\times 2^{n}$ when the computational complexity is $O(n^{2} + 2^{q+3})$ . The improved algorithm can detect more edge details and has strong adaptability.

A method for improving the crack resistance of aluminum alloy aircraft skin inspired by plant leaf

Aircraft skins are likely to experience cracks and fracture failure due to the combined action of shear, bending, and torsional load. Inspired by the crack resistance exhibited by plant leaf, a method is proposed to improve the crack resistance of aluminum alloy aircraft skin. The characteristic parameters of main and secondary leaf veins are extracted by image edge detection and analysis methods. According to a constructed collection of self-similar fractal sets, a bio-inspired residual stress field with fractal characteristics extracted from leaf veins is applied to specimens ahead of crack tip by using laser peening. The effects of fractal parameters on crack retardation are analyzed using interaction integral. The results show that the stress intensity factor ahead of crack tip is reduced by applying a bio-inspired residual stress field, whereas the plastic zone area ahead of crack tip is enlarged. The correlation between these two trends reveals the mechanism of stress intensity decrease after the introduction of bio-inspired residual stress field. The optimal crack retardation effect is achieved at a fractal angle of 55°, at which the residual fatigue life is increased by up to 203.0%. Compared with square-shaped laser peening, full-coverage laser peening, square criss-cross pattern method, and single-edge notched tensile (SENT) specimen repair method, the proposed method achieves the longest residual fatigue life, which is almost three times that of the square-shaped laser peening method. Therefore, this theoretical study presents a potential method for improving the crack resistance of aluminum alloy aircraft skin.

A method for image edge detection based on interval-valued fuzzy sets

A method for image edge detection based on interval-valued fuzzy sets

Application of image edge detection methods for precise estimation of the standard surface roughness parameters: Polypropylene/ethylene-propylene-diene-monomer blend as a case study

Material surface roughness is an important property in different fields of science and its precise measurement is still a serious concern. Roughness measurement is commonly implemented using stylus profilometer. Although it suffers from some drawbacks such as low speed and destructive nature. Optical methods, such as machine vision coupled with image texture analysis have shown promising capability for noncontact/nondestructive roughness measurement. In the present study, a roughness evaluation method is proposed based on image edge detection algorithms. The method was applied to investigate the surface roughness of polypropylene/ethylene-propylene-diene-monomer (PP/EPDM) blend as an important engineering plastic. Different roughness patterns were created on PP/EPDM sheets employing hot press processing. Images of the roughened samples were captured with 10 different resolutions. In the proposed method, the performance of five different edge detectors including Roberts, Prewitt, Sobel, Laplacian of Gaussian (LoG), and Canny were examined. The results showed that LoG method in the images with 200 dpi resolution effectively evaluates PP/EPDM surface roughness. Linear correlation coefficients (R2) between LoG results and Stylus profilometry results was greater than 0.98. Moreover, some mathematical models were developed for evaluation of the roughness parameters based on LoG edge frequency. The models’ results showed 6.7% deviation from stylus profilometry results in the worst case. The proposed method can be used as a feasible solution for roughness evaluation of polymeric materials in online inspections.

Image edge detection method based on anisotropic diffusion and total variation models

In this study, a novel image edge detection technique based on the combination of total variation (TV) and anisotropic diffusion (PM) models is presented. In the proposed technique, the authors first use the gradient magnitude to eliminate the noise, then utilise the adaptive weight function to detect the edges of the image. The adaptive weight function has a high ability to adapt and change according to the areas information (edges or flats areas). More specifically, TV filter is applied on the areas which suffer from double and false edges, whereas, anisotropic diffusion filter is applied on the areas which suffer from weak and discontinuous edges. Applying TV filter on the double edges areas will allow one to remove most of the false edges, and thus to obtain much sharper edges. While, applying anisotropic diffusion filter on the discontinuous edges areas will lead to obtaining robust and continuous edges. Consequently, less false edges besides high localisation accuracy were obtained. Experimental results demonstrate the superiority of the new approach in terms of removing the false edges and improving the localisation accuracy of the edges. As objective quantitative performance measures, the peak signal-to-noise ratio (PSNR) and Pratt's figure of merit (FOM) were used.

Establishment of cellular automata image model and its application in image dimension measurement

Aiming at how to improve the efficiency of image edge detection, an image edge detection method based on least squares support vector machine (LSSVM) and cellular automata is proposed. Firstly, a new kernel function is constructed based on the Gauss radial basis kernel and polynomial kernel, which enables the LSSVM to fit the gray values of the image pixels accurately. Then, the gradient operator of the image is deduced, and the gradient value of the image is obtained by convolution with the gray value of the image. Then, the cellular automata evolves the gradient value according to the designed local rules to locate and detect the image edge. Simulation results show that the proposed edge detection algorithm is effective, and the new algorithm has higher detection performance than Sobel and Canny algorithms.

A Novel Medical Image Edge Detection Method Based on Reinforcement Learning and Ant Colony Optimization

A Novel Medical Image Edge Detection Method Based on Reinforcement Learning and Ant Colony Optimization

Real-time Human Corneal Images Processing Analysis of Placido Disk Corneal Topography Using Extenics

A sub-pixel image edge detection method based on polar coordinates is proposed in order to accurately detect images obtained from corneal topographic instrument based on Placido disk. The corneal image obtained by the corneal topographic instrument has the following characteristics: 1. Real time. The image acquisition video is set to 30 frames/sec, so the image processing algorithm should have the millisecond level of computing performance; 2. Quality is not stable. Because of the influence of photographing environment, machine and human entry factors, the images of cornea will have various kinds of noises. So we should propose a stable and effective preprocessing algorithm. 3. Image offset. Due to the operation characteristics of such corneal topographic system, the consecutive images’ centers are unavoidable drifted during the process of image acquisition. So it is necessary to match the same serial target pictures. According to the above characteristics, based on the theory of extension and matter-element data structure, this paper efficiently structured captured images and uses two-dimensional Gaussian smoothing function to preprocess the data of all kinds of noises, including real human eyelashes removing and uses mixed Canbel (combining the canny and Sobel algorithms) and regional three-polynomial fitting method to effectively detect the sub-pixel edges of corneal images which we derive. Based on the experiments of standard simulation eye and real human eye, it is proved that the algorithm presented in this paper has good robustness and detection accuracy (the refractive error distributions of standard simulation eyes with different curvatures are less than 0.25D), which meets the operation requirements of actual systems used in industry. So our algorithm can be used in such corneal topographer in order to help detection of various eye diseases.

Stability Evaluation of Fault Diagnosis Model Based on Elliptic Fourier Descriptor

The performance evaluation of fault diagnosis algorithm is an indispensable link in the development and acceptance of the fault diagnosis system. Aiming at the stability evaluation of the fault diagnosis model based on the characteristic clustering, an image edge detection method based on the Elliptic Fourier Descriptor (EFDSE) is proposed to evaluate the stability of the fault diagnosis model, which applies similarity measurement of image to effective evaluation of faulty diagnosis algorithm. The quantitative evaluation index of the diagnostic capability of characterization based cluster fault diagnosis model is used to provide reference for the acceptance and reliability of the diagnosis results. Finally, the effectiveness of the stability evaluation is verified by the fault data of the motor bearings.

Implementation of sobel method to detect the seed rubber plant leaves

This research was conducted to develop a system that can identify and recognize the type of rubber tree based on the pattern of leaves of the plant. The steps research are started with the identification of the image data acquisition, image processing, image edge detection and identification method template matching. Edge detection is using Sobel edge detection. Pattern recognition would detect image as input and compared with other images in a database called templates. Experiments carried out in one phase, identification of the leaf edge, using a rubber plant leaf image 14 are superior and 5 for each type of test images (clones) of the plant. From the experimental results obtained by the recognition rate of 91.79%.

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.

Optimizing Robinson Operator with Ant Colony Optimization As a Digital Image Edge Detection Method

Edge detection serves to identify the boundaries of an object against a background of mutual overlap. One of the classic method for edge detection is operator Robinson. Operator Robinson produces a thin, not assertive and grey line edge. To overcome these deficiencies, the proposed improvements to edge detection method with the approach graph with Ant Colony Optimization algorithm. The repairs may be performed are thicken the edge and connect the edges cut off. Edge detection research aims to do optimization of operator Robinson with Ant Colony Optimization then compare the output and generated the inferred extent of Ant Colony Optimization can improve result of edge detection that has not been optimized and improve the accuracy of the results of Robinson edge detection. The parameters used in performance measurement of edge detection are morphology of the resulting edge line, MSE and PSNR. The result showed that Robinson and Ant Colony Optimization method produces images with a more assertive and thick edge. Ant Colony Optimization method is able to be used as a method for optimizing operator Robinson by improving the image result of Robinson detection average 16.77 % than classic Robinson result.

Adaptive Ant Colony Optimization on Mango Classification Using K-Nearest Neighbor and Support Vector Machine

Abstract— Leaves recognition can use an image edge detection method. In this research, the classification of mango gadung and manalagi will be performed. In the preprocess stage edge detection method using adaptive ant colony optimization method. The use of adaptive ant colony optimization method aims to optimize the process of edge detection of a mango leaves the bone image. The application of ant colony optimization method on mango leaves classification has successfully optimized the result of edge detection of a mango leaves the bone structure. Results showed edge detection using adaptive ant colony optimization method better than Roberts and Sobel method. The result an experiment of mango leaves classification with k-nearest neighbor method get accuracy value equal to 66,25%, whereas with the method of support vector machine obtained accuracy value equal to 68,75%.Keywords— Edge Detection, Ant Colony Optimization, Classification, K-Nearest Neighbor, Support Vector Machine