Edge Detection Effect Research Articles

An edge detection method of colony image based on mediocrity ant colony algorithm

The colony is one of the important research objects in microbial technology, which can realize the evaluation of food safety level, environmental pollution degree, therapeutic effect of medical drugs, and characteristics of agricultural fungicides. Traditional colony image research requires human visual observation and statistics, which will result in low work efficiency and high work intensity. Colony image edge detection is an important basis for colony image research. Traditional edge detection operators cannot meet the accuracy requirements of the detection results. This paper proposes a Mediocrity Ant Colony Algorithm (MACA) to achieve edge detection of colony images. MACA combines the mediocrity rule, uses empirical functions to establish a pheromone database that can be used as a pheromone update reference table, adopts the Chebyshev distance as a weight that affects pheromone update, and combines heuristic information acquisition with maximum variance classification method and local path weights. The method that jointly affects the ant transition probability incorporates feedback rules for obtaining path weights to improve the edge detection effect. By performing edge detection simulation experiments on six colonies of three types of bacteria, and comparing with the classic edge detection operators and two classic ant colony edge detection algorithms, the detection performance, detection results and running time are proposed. The stability and accuracy of MACA algorithm is better than other methods, and the ideal results of the colony image edge detection by the ant colony algorithm are obtained.

Noncontact cable tension estimation using edge recognition technology based on convolutional network

The cable is the main load-bearing component of cable-stayed, suspension, and tied-arch bridges. The tension of the cable is crucial to the bridge's overall safety. Therefore, achieving an accurate and rapidcable tension estimation is of great practical importance. Currently, the method of estimating cable tension based on image processing technology avoids the disadvantages of conventional estimation methods, such as high cost, difficult sensor installation, and potential structural damage. However, for estimating cable tension based on image processing technology, a complex optical background may reduce the accuracy and stability of cable tension estimation. A method for estimating cable tension based on a deep convolutional network is proposed to solve the problem. The convolutional network is initially used to identify cable edges. Second, analyzing the edge pixels' motion identifies the cable's fundamental frequency. Finally, the cable tension is estimated using the correlation between fundamental frequency and cable tension. A field experiment was conducted on cable-stayed and tied-arch bridges in Chengdu to validate the proposed method's applicability and accuracy. The results of the experiments indicate that the edge detection effect of the proposed method is demonstrably superior to that of the Canny algorithm under various optical backgrounds and that the cable tension relative difference between the proposed method and the conventional method based on accelerometer is within 5%.

Amp-vortex edge-camera: a lensless multi-modality imaging system with edge enhancement.

We demonstrate a lensless imaging system with edge-enhanced imaging constructed with a Fresnel zone aperture (FZA) mask placed 3 mm away from a CMOS sensor. We propose vortex back-propagation (vortex-BP) and amplitude vortex-BP algorithms for the FZA-based lensless imaging system to remove the noise and achieve the fast reconstruction of high contrast edge enhancement. Directionally controlled anisotropic edge enhancement can be achieved with our proposed superimposed vortex-BP algorithm. With different reconstruction algorithms, the proposed amp-vortex edge-camera in this paper can achieve 2D bright filed imaging, isotropic, and directional controllable anisotropic edge-enhanced imaging with incoherent light illumination, by a single-shot captured hologram. The effect of edge detection is the same as optical edge detection, which is the re-distribution of light energy. Noise-free in-focus edge detection can be achieved by using back-propagation, without a de-noise algorithm, which is an advantage over other lensless imaging technologies. This is expected to be widely used in autonomous driving, artificial intelligence recognition in consumer electronics, etc.

Evaluation of edge detection algorithm of frontal image of facial contour in plastic surgery

With the improvement of medical levels and the continuous improvement of people’s living standards, the demand for beauty by the general public is increasing. The plastic surgery industry has also developed by leaps and bounds. People’s dissatisfaction with their own facial appearance, facial injuries and some other reasons have prompted people to carry out facial reconstruction, and facial plastic surgery has developed rapidly. However, in the current facial plastic surgery, the edge detection effect on the contour image is general. In order to improve the edge detection effect of facial contour lines in medical images, this paper proposed a facial contour line generation algorithm. First, the detection effects of four operators were compared. After comparing the effects, the Sobel operator was used as the input data to generate an edge detection algorithm. Then, the grayscale features of the tissue in the image and the symmetry of the image were used to perform bidirectional contour tracking on the detected image to extract facial contour lines. In addition, for facial contour features, the midpoint method can be used to generate auxiliary contours. The algorithm was verified by a set of facial CT (Computed Tomography) images in the experiment. The results showed that the new generation algorithm accelerated the edge detection speed, had good denoising performance, and enhanced the edge detection effect by about 12.05% compared with the traditional edge detection algorithm. The validity and practicability of facial edge detection were verified, and it provided a theoretical basis for further realizing the design of a facial contour digital image processing system.

Modified canny edge detection technique for identifying endpoints

Edge detection is an image processing technique that retains the edges of an object in an image while discarding other features. The Canny edge detection technique is regarded as one of the most successful edge detection algorithms because of the good edge detection effect. However, one of its problems is the discontinued edges. In this paper, we present an endpoint identification algorithm that can pinpoint the position of the discontinued edges. After the endpoints are identified, they are paired together based on distance, and the broken gaps are filled by connecting the endpoints. Results have shown that, visually, our method has fewer discontinued edges when compared to Canny. Also, the mean square error of our method is lower than traditional Canny, indicating that our technique produces edge images that are more accurate than the traditional Canny.

Fractal Visual Art Graphic Design Based on Computer-Aided Algorithms

Embedded machine vision algorithm development platform is of great significance. Based on the elaboration of visual communication design, this paper further details the design of fractal visual art graphics based on computer-aided algorithms to design edge detection schemes, where edges represent sudden changes in the signal and are able to characterize the rich information of an image. In response to the problems of poor robustness and difficult parameter selection of the currently widely used edge detection algorithms, the platform encapsulates joint edge detection graphic components in a higher-order processing library to achieve the requirements of high-precision detection and to realise enhanced edge detection effects. Finally, the experimental analysis shows that the proposed algorithm has a more accurate detection effect and can meet the user's high-precision detection requirements.

The Pointer-type Instrument Recognition Method based on Yolov3

<p>In order to solve the problems of fuzzy image, different measuring range and recognition accuracy of pointer instrument in the process of power inspection, propose a pointer-type instrument recognition method based on Yolov3. Firstly, super-resolution technology is introduced to reconstruct the instrument image. Then, a combined Yolov3 and OCR- based dial feature extraction method is used to detect the dial in the instrument image, effectively extract the digital features in the region, and recognize the instrument by combining the digital features pointer-type angle. Next, to solve the instrument image’s fuzzy problem, a super-resolution image reconstruction technology is designed to reconstruct the fuzzy instrument image and get a clear instrument image. Finally, a method based on a Yolov3 and OCR combination is proposed to detect the instrument image and recognize the digital features of the dial to target the insufficient use of dial features. Comparison of the effect of traditional machine learning algorithm and Canny edge detection in the pointer-type instrument image recognition shows that the accuracy and practicability of this method have been significantly improved in the recognition of pointer-type instruments in complex environment images.</p> <p> </p>

Edge Detection Algorithm-Based Lung Ultrasound in Evaluation of Efficacy of High-Flow Oxygen Therapy on Critical Lung Injury.

The study focused on the therapeutic effects of high-flow oxygen therapy on patients with critical lung injury using edge detection-based ultrasound images. Firstly, the traditional Canny edge detection algorithm was improved, and the optimal threshold was obtained by optimizing the median filter and combining Otsu algorithm and threshold iteration method. Then, the optimized algorithm was compared with the traditional Canny edge detection algorithm and applied to process the lung ultrasound images of 120 cases of critical lung injury, to compare the efficacy of high-flow oxygen therapy and the traditional oxygen therapy. It was found that the peak signal-to-noise ratio (PSNR) (20.34~31.3), edge intensity value (17.89~27.34), and edge detection effect of the improved Canny algorithm were better than the traditional Canny algorithm (15.2~28.61, 9.44~18.56). The failure rate of extubation (4.1%), reintubation rate (0.8%), comfort (2.38 ± 0.15 points), dry humidity score (1.07 ± 0.21 points), antibiotic use (7.41 ± 0.74 days), and hospital stay (8.66 ± 1.02 days) in the experimental group were significantly lower than the corresponding indexes in the control group (11.7%, 5%, 4.25 ± 0.26 minutes, 4.94 ± 0.78 minutes, 19.29 ± 1.7 days, and 27.49 ± 2.22 days), and the difference was statistically significant (P < 0.05). In the experimental group, within 48 hours after extubation, the respiratory rate (RR), heart rate (HR), arterial partial pressure of carbon dioxide (PaCO2), and HCO3− were significantly lower than those of the control group; and the values of transcutaneous oxygen saturation (SpO2), mean arterial pressure (MAP), arterial partial pressure of oxygen (PaO2), and pH were significantly higher than the control group, and the difference was statistically significant (P < 0.05). In conclusion, the algorithm in this study is superior to the traditional Canny algorithm, and the high-flow oxygen therapy can reduce the failure rate of extubation, strengthen patient comfort, improve the degree of gas humidification, stabilize the respiratory function and circulatory system, and shorten the time of antibiotic use and hospital stay.

Medical Image Enhancement Based on Laplace Transform, Sobel Operator and Histogram Equalization

Medical image enhancement is one of the most widely used medical image processing techniques in medical domain. Its purpose is to improve the visual effect of the image and facilitate the analysis and understanding of the image by human or machine. The Laplace transform and the Sobel gradient operator are two common ways of performing edge detection, image sharpening and enabling the image to be enhanced. However, each has limitations when used in isolation. The Laplace operator has a good edge detection effect, but it will make the image noise expand; the Sobel operator has a certain ability to smooth the noise, but the edges of the image obtained after processing are rougher. This paper therefore proposes a method based on both Laplace transform and Sobel operator, and histogram equalization of the transformed image is processed to enhance the image. Using a combination of both filtering methods avoids the disadvantage. This method was found to be effective in improving the quality of lung images and skeletal images through several experiments.

Edge Detection Network with Multi-Depth Feature Enhancement and Top-Level Information Guidance

<p indent=0mm>The existing edge detection networks still have problems such as missing edges and excessive noise in complex natural scenes. Therefore, an edge detection network with multi-depth feature enhancement and top-level information guidance is proposed. First, UNet++ is used as the backbone network to extract features of different depths, and the edges of different scales are made more complete by feature superposition. Then, a feature enhancement module is added after the side output of each convolution layer to increase the receptive field and enhance the multi-scale information by introducing the dilated convolution. Finally, a top-level information guidance module is designed to enhance the edge detection effect by introducing top-level semantic features into jump connection. The experimental results show that training on the three datasets of BSDS500, NYUDv2 and Multicue has achieved good results. On the BSDS500 dataset, the ODS, OIS and AP of this network reach 0.821, 0.839 and 0.869 respectively, which is generally higher than the existing edge detection networks. Moreover, the result has less noise and the subjective effect is closer to the ground truth.

Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection

In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive P -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.

Parameter detection for surface acoustic wave filter based on image processing

As core devices for the smartphone RF front end, surface acoustic wave (SAW) filters are in short supply. The detection of SAW filter parameters refers to the connection between layout design and process production, which is an important part of capacity improvement. This paper devises a method based on image processing to detect SAW filter parameters. We improve the morphological operator to recover an image and identify the number of interdigital transducer (IDT) finger bars under noise conditions. The feature points in the color image are selected to extract the scale. The Canny edge detection effect is enhanced through using an adaptive filter, increasing the gradient operator, bilinear interpolation, and other algorithms. We use a minimum shape to enclose the edges to determine the IDT width and period and apply Fourier transform to verify the detection results. The experimental results show that the number of IDT finger bars is accurately identified. The width and period are exactly the same as the real values at the 10th percentile, and the detection requirements are met.

Edge detection algorithm of medical image based on Canny operator

Edge detection is an important part of image segmentation, in this paper, the edge detection algorithm based on traditional Canny operator for medical images is studied. The Canny operator is improved using Otsu algorithm and double-gate limit detection method, and the ability of Canny operator edge detection is strengthened. The simulation of the algorithm is realized on the computer platform by MATLAB, and the experimental results are analyzed from two image objective evaluation indexes of information entropy and mean square error. The experimental results show that compared with the traditional Canny algorithm, the improved adaptive double threshold Canny algorithm has better edge detection effect, richer image details, better noise suppression and less false edges.

Research on 3D Modeling Software Maya Digital Media Animation Assisted Brain Surgery Technology

Abstract This article uses the three-dimensional (3D) modeling software Maya and the 3D interactive software Unity3D to develop an interactive teaching system that integrates the display of the human brain, the display of the principle pathology of cerebral thrombosis, and the simulation of stent surgery in the brain. The use of this system can result in unlimited learning by multiple people, avoiding shortcomings such as insufficient resources, and strengthening the characteristics of interaction. For human brain models, functions such as zoom-in, rotation, and observation of a single part can be implemented, and text descriptions are provided. For the demonstration of cerebral thrombosis, animation is provided for simulation. For intra-brain stent surgery, the system can automatically demonstrate the surgical process, and it can be actually operated by the user, thereby enhancing the convenience and fun of student learning. This system uses Unity3D software as the main development tool. It uses the software’s advanced lighting system, particle system, and algorithms such as edge detection and screen post effects to establish the main frame of the system. It utilizes C # language for programming, thus enriching the functions of the system. On testing, the virtual interactive teaching system realized by the above method exhibits high interactivity, fun, and practicability.

Edge Detection Algorithm Optimization and Simulation Based on Machine Learning Method and Image Depth Information

Machine learning algorithms have become a hot topic in current research due to their unique learning performance, and have achieved fruitful research and application results in various fields. In this paper, the idea of machine learning classification algorithm is applied to depth image edge detection, AdaBoost algorithm and decision tree are used for image edge detection. The algorithm is created from training set creation, depth image feature extraction and combination of AdaBoost and image depth information, creating image training sample sets, selecting image features, training algorithm classifiers, and simulating medical ultrasound image classifiers. Finally, the machine learning algorithm was simulated and tested. The experimental results show that the edge detection effect is good, the algorithm adaptability is strong, and no adjustment parameters are needed.

Improved skin lesions detection using color space and artificial intelligence techniques

Background: Automatic skin lesion image identification is of utmost importance to develop a fully automatized computer-aided skin analysis system. This will be helping the medical practitioners to provide skin lesions disease treatment more efficiently and effectively.Material and method: In this article, two image processing techniques for accurate detection of skin lesions have been proposed. In first technique, the optimization of edge detection has been carried out by using a branch of artificial intelligence called nature inspired algorithm. Ant colony optimization (ACO) is used to increase effectiveness of edge detection in skin lesion. The second technique deals with the color space-based split-and-merge process in combination with global thresholding segmentation and edge smoothing operations.Result: The performance of both techniques has been measured by entropy performance evaluation parameter. The results show remarkable improvement in output images obtained by Canny edge detection technique optimized by ACO in comparison with ACO-Sobel, ACO-Prewitt and Edge Smoothing-Color Space techniques.Conclusion: ACO-Canny Edge detection technique shows far better effieciency for skin lesion detection as compared to ACO-Sobel, ACO-Prewitt and Edge Smoothing Color Space technique.

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.

Multimedia animation filtering simulation based on image extraction

With the rapid development of computer technology and network technology, a large number of audio, video, text and other multimedia information are pouring in, but only part of the information is of practical value to Internet users. Therefore, in the information explosion society, timely screening and reasonable pushing of a large number of information has become an urgent technical problem. This paper will study and analyse the research point of multimedia animation filtering in Internet environment. Based on the basic principles of image sequential edge detection algorithm and watershed image detection algorithm, and combined with mathematical morphology theory, this paper creatively proposes an improved character class edge detection and filtering algorithm. Based on the image filtering algorithm proposed in this paper, the coordination and optimization of edge detection accuracy and anti-noise ability can be achieved. Finally, simulation experiments are carried out for the above algorithms. The experimental results show that the proposed optimization algorithm has excellent edge detection effect.

A Novel Edge Detection Algorithm Based on Distance

Under the framework of Canny algorithm, a distance-based edge detection algorithm is proposed to improve the gradient magnitude of Canny. In our algorithm, the gradient magnitude can be acquired by taking the distance from the center of the mask as the weight factor. The operator not only can calculate the image gradient better, but also has good separability in the horizontal and vertical directions, and its gradient amplitude has a certain degree of rotation invariance. Finally, this operator is compared with various gradient operators for the Lena image simulation and the actual waterfront edge detection experiment. It shows that the operator has a better edge detection effect from the experimental results.

Automatic Learning Technology of Railway Based on Deep Learning for Railway Obstacle Avoidance

Railway identification is the key to dividing the dangerous area and safety area of the rail surface. It is also the key technology for the detection of rail surface obstacles. The problem is that the traditional rail detection edge detection effect is not good and the illumination environment is seriously affected. This paper proposes a track recognition detection system based on deep learning. The system can not only identify the rails, but also plan the dangerous areas, apply the convolutional neural network, extract the feature information of the rails in different environments, and use the Mask R-CNN instance segmentation algorithm. The identification of the rails and the division of the dangerous areas are carried out to achieve accurate and rapid detection. The test results show that the system has high anti-interference and accuracy.