Canny Edge Detection Method Research Articles

ANALYSIS OF CANNY EDGE DETECTION METHOD FOR FACIAL RECOGNITION IN DIGITAL IMAGE PROCESSING

The document explores the application of the Canny Edge Detection method in facial recognition systems, specifically for identifying edge patterns in digital images. In the context of technological advancements, the focus is on enhancing data processing through efficient image analysis techniques. The research addresses how different edge detection methods, including Roberts, Prewitt, Sobel, and Canny, function, with the latter being highlighted for its superior ability to minimize error and deliver accurate edge detection results. The study outlines the development of a system designed to identify optimal edge detection parameters using the Canny method, focusing on facial images captured from the front. The system is limited to edge identification in such images, and performance is measured using a correlation coefficient. The process involves several technical steps, such as pre-processing the image (grayscale conversion and noise reduction) and using Gaussian filters and hysteresis thresholding to refine the detection. The research's ultimate aim is to optimize Canny's performance for identifying edges, contributing to advancements in facial recognition technology.

SEGMENTATION OF DIGITAL IMAGES WITH WAVELET TRANSFORMATION USING MATLAB VERSION R2010B

The purpose of this research is to explore and apply the use of wavelet transformation for the segmentation of digital images, utilizing MATLAB version R2010B. The study aims to analyze how wavelet transform can be used to enhance the accuracy and effectiveness of image segmentation, which is a critical process in image processing and computer vision. The research contributes to the field of digital image processing by demonstrating the application of wavelets transformation for segmenting digital images. In this study, the study provides insights into how wavelets can be utilized to improve the detection of image features, especially in identifying image features more accurately. The experimental results show that only the Canny operator's edge detection method has the best edge detector in detecting the edges of objects in wavelet images. The technique of determining the threshold value (thresholding) can be carried out in two ways, namely, automatic method and technique carried out by trial and error. Finally, improving automatic thresholding techniques using AI-driven algorithms to reduce the reliance on trial-and-error methods could provide more consistent results in varied application areas.

AUTOMATIC PANEL QUALITY MONITORING SYSTEM USING VISION-BASED ABNORMAL OBJECT DETECTION

The rate of “Not Good” products due to the presence of abnormal objects during the quality control inspection of mobile phone’s panels is as high as 38 percent without supervision and 19 percent with naked eye’s inspection. To reduce the loss of small abnormal objects and improve the recognition rate of object detection of automatic panel quality monitoring, the paper proposes abnormal object recognition based on a template matching and subtract background technique. Firstly, the object recognition network based on convolutional neural networks (CNNs) is introduced initially. Then, the Canny Edge Detection method improves the image input quality. Next, template matching is utilized to discover things that depart from a standard pattern or a predetermined model. Models for background reduction will be implemented instantly into smartphone panel quality checking systems. Continually, a vision-based quality control (QC) strategy will be developed. Lastly, experimental results from a practical vision-based automatic anomalous object identification system demonstrate the viability and enhancement of the suggested strategy.

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle.

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang-Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

Enchancing Lung Disease Classification through K-Means Clustering, Chan-Vese Segmentation, and Canny Edge Detection on X-Ray Segmented Images

The lungs are one of the vital organs in the human body. Not only play a role in the respiratory system, the lungs are also responsible for the human circulatory system. Supporting examinations can also facilitate medical workers in determining the diagnosis. Usually a lung examination is complemented by a chest X-ray examination procedure. This examination aims to see directly and assess the severity of lung conditions. With current technological advances, image analysis can be done easily. Through digital image processing methods, information can be obtained from images that can be used for analysis as a support for diagnoses in the world of health. Image segmentation is a method in which digital images are divided into several segments or subgroups based on the characteristics of the pixels in the image. In this study, clustering with the K-Means method will be carried out on the results of segmentation of x-ray images of lung diseases, namely Covid-19, Tuberculosis, and Pneumonia. The segmentation method that will be implemented is the Chan-Vese Method and the Canny Edge Detection Method. This research shows that the results of the accuracy of applying the K-Means Clustering method to Chan-Vese and Canny Edge-Based Image Segmentation are 80%.

Improving 3D edge detection for visual inspection of MRI coregistration and alignment

BackgroundVisualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another. New methodWe suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative). ResultsWe demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity. Comparison with existing methodsCurrent methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image. ConclusionThe second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online.

Image Segmentation and Classification using Neural Network

Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable of learning complex features directly from images and achieving outstanding performance across several datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection methods for pre-processing, and K-means clustering have been applied to segment the images. Image augmentation improves the size and diversity of datasets for training the models for image classification. This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that provides insights into the selection of pre-trained models and hyper parameters for optimal performance. We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such as CNN and VGG 16 for classification.

An End-to-End Inclination State Monitoring Method for Collaborative Robotic Drilling Based on Resnet Neural Network.

The collaborative robot can complete various drilling tasks in complex processing environments thanks to the high flexibility, small size and high load ratio. However, the inherent weaknesses of low rigidity and variable rigidity in robots bring detrimental effects to surface quality and drilling efficiency. Effective online monitoring of the drilling quality is critical to achieve high performance robotic drilling. To this end, an end-to-end drilling-state monitoring framework is developed in this paper, where the drilling quality can be monitored through online-measured vibration signals. To evaluate the drilling effect, a Canny operator-based edge detection method is used to quantify the inclination state of robotic drilling, which provides the data labeling information. Then, a robotic drilling inclination state monitoring model is constructed based on the Resnet network to classify the drilling inclination states. With the aid of the training dataset labeled by different inclination states and the end-to-end training process, the relationship between the inclination states and vibration signals can be established. Finally, the proposed method is verified by collaborative robotic drilling experiments with different workpiece materials. The results show that the proposed method can effectively recognize the drilling inclination state with high accuracy for different workpiece materials, which demonstrates the effectiveness and applicability of this method.

A parallel Canny edge detection algorithm based on OpenCL acceleration.

In the process of Canny edge detection, a large number of high complexity calculations such as Gaussian filtering, gradient calculation, non-maximum suppression, and double threshold judgment need to be performed on the image, which takes up a lot of operation time, which is a great challenge to the real-time requirements of the algorithm. The traditional Canny edge detection technology mainly uses customized equipment such as DSP and FPGA, but it has some problems, such as long development cycle, difficult debugging, resource consumption, and so on. At the same time, the adopted CUDA platform has the problem of poor cross-platform. In order to solve this problem, a fine-grained parallel Canny edge detection method is proposed, which is optimized from three aspects: task partition, vector memory access, and NDRange optimization, and CPU-GPU collaborative parallelism is realized. At the same time, the parallel Canny edge detection methods based on multi-core CPU and CUDA architecture are designed. The experimental results show that OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieves 20.68 times acceleration ratio compared with CPU serial algorithm at 7452 × 8024 image resolution. At the image resolution of 3500 × 3500, the OCL_Canny algorithm achieves 3.96 times the acceleration ratio compared with the CPU multi-threaded Canny parallel algorithm. At 1024 × 1024 image resolution, the OCL_Canny algorithm achieves 1.21 times the acceleration ratio compared with the CUDA-based Canny parallel algorithm. The effectiveness and performance portability of the proposed Canny edge detection parallel algorithm are verified, and it provides a reference for the research of fast calculation of image big data.

Tubercolusis Segmentation Based on X-ray Images

Tuberculosis or TB is an infectious disease caused by the bacteria Mycobacterium tubercolusis. This disease usually attacks the lungs, but can also affect other organs such as the kidneys, bones and brain. TB is highly contagious, and can spread through the air when someone who is infected coughs or sneezes. Risk factors that can increase a person's chances of developing TB include a weak immune system, such as people with AIDS, diabetes, or people taking immunosuppressant drugs. And people who live or work in environments with high rates of TB transmission are also at risk of infection. Symptoms of TB are usually a cough that lasts more than three weeks, unexplained weight loss, fever, night sweats and persistent fatigue. In more severe cases, TB can cause coughing up blood, chest pain and difficulty breathing. One of the examination tools that can be used to detect TB disease is x-rays. Which produces X-Rays to help and confirm the diagnosis of TB disease, to see the chest part of the body which is used as medical record documentation. In X-ray photos, random dark and light spots of noise are often found which are caused by several factors. Based on the facts above, image segmentation is an important task for doctors in diagnosing disease. Automatic detection or segmentation of lung images from chest x-ray images is the initial stage of the diagnosis process. This research aims to implement a segmentation method to determine edge detection in clearer images using several segmentation methods, namely the Canny Edge Detection method, Sobel reading chest x-ray results for tuberculosis. And canny edge detection with segmented RGB image (otsu's thresholding) produces the highest value, namely 230,466.0 pixels and a lesion volume of 14,818.625 mm3.

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

IMPLEMENTASI METODE DETEKSI TEPI CANNY UNTUK MENGHITUNG JUMLAH UANG KOIN DALAM GAMBAR MENGGUNAKAN OPENCV

Edge detection is often used in image processing to find the edge boundaries of image objects and this is important for selecting the edges of image parts. The image edge detection used in this study counts coins in an image, so that it can easily pass through the human eye but not by a computer. In a computer system, it is necessary to provide programs and test data so that the computer can read and count coins like a human. The purpose of this study is to count coins in an image using Canny edge detection and Gaussian filtering methods. The images used in this study are in the form of *jpg and tested using the Visual Studio Code application using OpenCV and Python. The results of this study indicate that the results of the number of coins read from the images inputted to the system can be read properly, with a data accuracy of 90%. Canny's edge detection method can be used to count the number of coins in an image.Keywords: Pengolahan Citra; Deteksi tepi Canny; Gaussian filtering; OpenCV;

ASSESSMENT OF THE IRIS RECOGNITION USING EDGE DETECTION ALONG BENCHMARK DATASETS

An individual's identity has grown more essential for individuals to satisfy modern corporate the community's higher safety standards. Iris represents one of the finest and most precise biometric systems now in utilization, with multiple edge detection methods used. As a consequence, understanding the different kinds of edge detection algorithmic methods which are now under operation is crucial. Three edge detection methods are used to be assessed on iris data during the present research. These methodologies are carried out using the MATLAB environment, which provides the evaluation with verified results. CASIA and MMU are the datasets that are used for this purpose. In contrast, the results reveal that the canny edge detection method works quite efficiently. Visual quality is crucial in vision-based recognition of objects. The present research assesses the visual aesthetic using various quality indicators such as PSNR and MSE. When compared to PSNR and MSE, however, MSE is recognized as the best image quality statistic.

A low cost IoT-based Arabic license plate recognition model for smart parking systems

License Plate Recognition is one of the significant enablers that can be utilized in wide range of applications in ITS and smart cities. The proposed design relies on three image processing stages to achieve license plate identification with high accuracy which are pre-processing, segmentation, and character recognition. The canny edge detection method with various thresholds, contour detection, and masking techniques are used to locate the car edges and license plate. In the experiment presented in this paper, 200 images were used to identify Egyptian car plates. The model successfully identified Arabic license plates with 93% accuracy. A prototype is implemented using ESP32 Cameras and Raspberry-Pi to test the system's performance. Moreover, a database and a website are hosted on the RPi to allow users to search for their car location in the parking lot using the car's full or partial license plate which was saved in database upon detection.

Effect of travel speed and number of layers on surface waviness of ER70S6 deposits fabricated through non-transferred wire arc additive manufacturing

The present experimental study introduced NTA-WAAM system for fabricating thin-layered structures using ER70S-6 wire. The study is directed towards determining the Sw in the manufactured parts, considering varying level of TS (0.17, 0.19, and 0.24 m/min) and BL (2, 3, 7 layers). The Sw is computed through image processing using canny edge detection method. Increasing the BL from 2 to 7 and reducing TS from 0.24 to 0.17 m/min, the resulting Sw is increased from 1.05 to 2.69 mm. So with high BL, TS should be more to suitably control the Sw because of high heat dissipation and low fluidity of molten pool. In addition to the morphological attributes, microstructural evolution, XRD, Rietveld refinement, tensile, and wear testing are incorporated to present the feasibility and stability of part fabricated through NTA-WAAM. Crystallite size, dislocation density, and residual stress are computed through XRD. It is observed that when approaching the top section from bottom zone of deposition, crystallite size is reduced from 23.43 to 21.85 nm, whereas dislocation density (2.83 × 1012 to 6.52 × 1012m−2) and residual stress (−263.72 to −300.12 MPa) are increased. It indicates the adverse effect of crystallite size towards dislocation density and residual stress, which is primarily attributed to varying level of heat transfer during deposition. The deposit exhibits an ideally refined crystal structure than initial filler wire as χ2≈1 (from Rietveld analysis). Compressive residual stress development further improves the fabricated part’s tensile characteristics. Formation of equiaxed dimples, characterises the ductile failure phenomena, whereas the enhancement in their dimensions reflects the improvement in ductility.

ASPHALT ROAD DAMAGE DETECTION SYSTEM USING CANNY EDGE DETECTION

Road is a means of access for humans to move from one place to another, connecting one place to another, and serving as transportation infrastructure. At all times, asphalt roads are passed by road users, including small, medium, and large vehicles. However, road conditions are not always smooth and often there are damages in certain parts of the road. Factors such as water, weather, temperature changes, unstable soil conditions, air temperature, poor compaction process on the base layer, and the weight or overload of heavy vehicles that exceed capacity, as well as the increasing volume of vehicles, can cause road damage. Road damage can reduce economic revenue and increase accident rates. Some types of asphalt road damage include undulating, potholes, cracking, and asphalt puddles on the road surface. Potholes are the most common type of damage that cause accidents for road users. This study uses the Canny edge detection method to detect asphalt road damage. The minimum object size that can be detected as road damage is 15x15 pixels and the maximum is 290x540 pixels. Testing was conducted on 65 primary data and 35 secondary data, and the average accuracy obtained were 90.5% and 88%, respectively.

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.

Experimental investigation of crossflow air effect on spray characteristics of gelled Jet-A1 fuel using an internally impinging air-blast atomizer

The present study aims to experimentally analyse the influence of crossflow aerodynamic parameters on the spray characteristics of gelled Jet-A1 fuel. A Jet-A1 gel fuel is prepared by combining 85 percentage by weight (wt. %) of Jet-A1 liquid as base fuel with an organo-gel solution having Thixatrol ST (7.5 wt. %) as gelling agent and Rectified xylene (7.5 wt. %) as solvent. Shear flow test is performed on the gel fuel using a rotational rheometer, to understand its non-Newtonian rheological properties. An internally impinging air blast atomizer is used to study the gel spray characteristics by injecting the gel fuel transversely into the subsonic cross flowing airstream environment. Gel spray images are captured by generating a laser sheet along the midplane of the test section across the crossflow air stream direction using the Laser Sheet Photography (LSP) technique. The captured gel spray images are used to visualize the gel spray structure in both spray axis as well as the crossflow air stream direction. These images are then investigated by developing an algorithm in MATLAB using an image processing tool. By incorporating the canny edge detection method into the MATLAB code, gel spray characteristics such as droplet size distribution, Sauter-mean-diameter, Arithmetic-mean-diameter, windward trajectories, and maximum penetration height are determined. Further, the effect of aerodynamic parameters such as crossflow Reynolds’ number and aerodynamic Weber number on the gel spray characteristics are investigated. Droplet size distribution and maximum penetration height of gelled Jet-A1 spray revealed its dependence on crossflow Reynolds’ number. Sauter-Mean-Diameter of gel droplets and windward trajectories of the gel spray decreases significantly as the aerodynamic Weber number increases. Thus, it can be concluded that the fineness of the spray is also a function of the crossflow aerodynamic parameters other than the atomizer.

Spatial Wave Measurement Based on U-net Convolutional Neural Network in Large Wave Flume

This study proposed a spatial wave measurement method based on a U-net convolutional neural network. First, frame images are extracted from a video collected by a physical model experiment, and a dataset of spatial wave measurements is created and extended using a data enhancement method. A U-net convolutional neural network is built to extract the spatial wave information of the images; evidently, the segmented water level is close to that of the original image. Next, the U-net convolutional neural network is compared with the sensor, pixel recognition, and Canny edge detection methods. Pixel recognition results reveal that the maximum and minimum errors of the U-net convolutional neural network are 3.92% and 1.05%, those of the Canny edge detection are 5.97% and 1.33%, and those of the sensor are 11.8% and 1.6%, respectively. Finally, the nonlinear characteristic quantities of waves are measured using the proposed U-net convolutional neural network. The kurtosis and asymmetry calculated in the spatial domain are slightly larger than those calculated in the time domain, whereas the skewness calculated in the spatial domain is smaller than that calculated in the time domain. The asymmetry and kurtosis increase with an increase in wave height and period, whereas the skewness increases with an increase in wave height but decreases with an increase in period.

Image Watermarking Using Least Significant Bit and Canny Edge Detection.

With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today's world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB's high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks.