Edge Detection Method Research Articles

Flow control method in concrete particles pneumatic sweeping process based on image recognition and OPC protocol

Based on image recognition and OPC(Object linking and embedding for Process Control) protocol, an intelligent pneumatic sweeping scheme is proposed to solve the problem of pavement aggregates cleaning. Firstly, the effects of four different auxiliary light sources and six different image segmentation methods on the recognition of single particle images were analyzed, and the maximum entropy segmentation method was the best under white light. Secondly, the effect of four different filtering methods on discrete multi-particle image recognition was studied. Among them, the adaptive median filter can better solve the problem of interference points misidentified as particles. The recognition effect of four different edge detection methods on aggregate particle pile was analyzed. And Canny operator with better effect was finally used to recognize particle pile from the overhead and side view. Finally, the communication mode between the host computer and the controller established based on OPC protocol was verified by experiments.

Three-Stage Interpolation Method for Demosaicking Monochrome Polarization DoFP Images.

The emergence of polarization image sensors presents both opportunities and challenges for real-time full-polarization reconstruction in scene imaging. This paper presents an innovative three-stage interpolation method specifically tailored for monochrome polarization image demosaicking, emphasizing both precision and processing speed. The method introduces a novel linear interpolation model based on polarization channel difference priors in the initial two stages. To enhance results through bidirectional interpolation, a continuous adaptive edge detection method based on variance differences is employed for weighted averaging. In the third stage, a total intensity map, derived from the previous two stages, is integrated into a residual interpolation process, thereby further elevating estimation precision. The proposed method undergoes validation using publicly available advanced datasets, showcasing superior performance in both global parameter evaluations and local visual details when compared with existing state-of-the-art techniques.

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%.

LRFE: A NOVEL LOCAL RESPONSE FEATURE ELIMINATION PROCESS FOR IDENTIFICATION OF LUNG CANCER CELLS

One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

Automated medication verification system (AMVS): System based on edge detection and CNN classification drug on embedded systems

A novel automated medication verification system (AMVS) aims to address the limitation of manual medication verification among healthcare professionals with a high workload, thereby reducing medication errors in hospitals. Specifically, the manual medication verification process is time-consuming and prone to errors, especially in healthcare settings with high workloads. The proposed system strategy is to streamline and automate this process, enhancing efficiency and reducing medication errors. The system employs deep learning models to swiftly and accurately classify multiple medications within a single image without requiring manual labeling during model construction. It comprises edge detection and classification to verify medication types. Unlike previous studies conducted in open spaces, our study takes place in a closed space to minimize the impact of optical changes on image capture. During the experimental process, the system individually identifies each drug within the image by edge detection method and utilizes a classification model to determine each drug type. Our research has successfully developed a fully automated drug recognition system, achieving an accuracy of over 95 % in identifying drug types and conducting segmentation analyses. Specifically, the system demonstrates an accuracy rate of approximately 96 % for drug sets containing fewer than ten types and 93 % for those with ten types. This verification system builds an image classification model quickly. It holds promising potential in assisting nursing staff during AMVS, thereby reducing the likelihood of medication errors and alleviating the burden on nursing staff.

An illumination-robust edge detection method for nuclear fuel assembly deformation measurement

Fuel assemblies (FAs) are critical components of nuclear reactor cores. The excessive deformation of FAs can pose a serious risk of safety accidents in nuclear power plants. Hence, regular monitoring of the deformation of the FAs is necessary. To accomplish this task, the most critical challenge lies in accurately extracting the edges of FAs in the nuclear underwater environment with spatial variations in illumination. In this paper, a novel illumination-robust edge detection method is proposed. Firstly, tilt correction is applied to arrange the fuel rods vertically in the image. Then, the center of each fuel rod is located through grayscale projection and filtered by rod center spacing variance minimization. Subsequently, adaptive thresholding is employed to obtain a binary image. Finally, the edges are identified by searching in the vicinity of rod centers in the binary image. The effectiveness and robustness of the proposed method have been validated in experiments, and the approach has been successfully applied to the FA deformation measurement in nuclear power plants.

Image-Based Concrete Crack Detection Method Using the Median Absolute Deviation.

This paper proposes an innovative approach for detecting and quantifying concrete cracks using an adaptive threshold method based on Median Absolute Deviation (MAD) in images. The technique applies limited pre-processing steps and then dynamically determines a threshold adapted for each sub-image depending on the greyscale distribution of the pixels, resulting in tailored crack segmentation. The edges of the crack are obtained using the Laplace edge detection method, and the width of the crack is obtained for each centreline point. The method's performance is measured using the Probability of Detection (POD) curves as a function of the actual crack size, revealing remarkable capabilities. It was found that the proposed method could detect cracks as narrow as 0.1 mm, with a probability of 94% and 100% for cracks with larger widths. It was also found that the method has higher accuracy, precision, and F2 score values than the Otsu and Niblack methods.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.

ANALYZING THE COMPARATIVE METHODS OF PREWITT, ROBINSON, KRISCH AND ROBERTS IN DETECTING THE EDGES OF RICE LEAVES

This research explores the vital role of rice in Indonesia as a staple food and primary source of income for farmers. Efforts are being made to increase rice production to meet the growing demand. The study focuses on object edge detection in image analysis, evaluating methods like Prewitt, Robinson, Krisch, and Roberts. Digital imaging plays a crucial part in visually presenting information, and image processing improves image quality for human and machine recognition. Detecting object edges, particularly in rice leaf images, is essential for computer inspection. The experiment on fifteen rice leaf images shows that the Krisch method performs better in edge detection, with a 52% average accuracy and smoothness. Other methods, such as Prewitt (6%), Robinson (11%), and Roberts (14%), have lower accuracy rates. These findings provide a foundation for enhancing edge detection in rice leaf image analysis. The study also emphasizes the need for refining classification models. Overall, this research provides insights into the effectiveness of edge detection methods in rice leaf image analysis.

An improved method for edge detection based on neighbor distance for processing hemispheric photography

Abstract Hemisphere photos are now widely applied to provide information about solar radiation dynamics, canopy structure and their contribution to biophysical processes, plant productivity and ecosystem properties. The present study aims to improve the original ‘edge detection’ method for binary classification between sky and canopy, which works not well for closed canopies. We supposed such inaccuracy probably is due to the influence of sky pixels on their neighbor canopy pixels. Here, we introduced a new term ‘neighbor distance’, defined as the distance between pixels participated in the calculation of contrast at the edges between classified canopy and sky, into the ‘edge detection’ method. We showed that choosing a suitable neighbor distance for a photo with a specific gap fraction can significantly improve the accuracy of the original ‘edge detection’ method. We developed an ND-IS (Neighbor Distance-Iteration Selection) method that can automatically determine the threshold values of hemisphere photos with high accuracy and reproductivity. It combines the modified ‘edge detection’ method and an iterative selection method, with the aid of an empirical power function for the relationship between neighbor distance and manually verified gap fraction. This procedure worked well throughout a broad range of gap fractions (0.019–0.945) with different canopy compositions and structures, in five forest biomes along a broad gradient of latitude and longitude across Eastern China. Our results highlight the necessity of integrating neighbor distance into the original ‘edge detection’ algorithm. The advantages and limitations of the method, and the application of the method in the field were also discussed.

Predictive Identification of Malignant Melanoma from Fractional Differentiation-based Image Enhancement

Recent advancements in computer vision and artificial intelligence allow for the automatic detection of some abnormalities in medical photographs. One of these is a skin lesion, and prompt and accurate detection of these conditions substantially aids in treatment. When image processing is combined with the fundamental edge detection technique, there has been potential demonstrated in the automatic identification and delineation of boundaries inside skin lesions. In this work, we investigate how to improve edge-detection in photos using fractional differentiation. We present a fractional differential filter based method for edge detection in images of skin lesions. The original photos are then improved with the help of the derived images. During acquisition, acquired images are classified using deep learning models. In the experiments, a well-researched dataset of HAM10000 is employed. The outcomes demonstrate how well these filters work to cut through noise and pick up minute edge characteristics that can provide useful information while performing recognition tasks.

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.

An Asynchronous Spiking Neural Membrane System for Edge Detection.

Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In the designed system, each of the three modules implements one type of the three operations associated with the edge detection of digital images, and they collaborate each other through the resonant connections. An algorithm called EDSNP for edge detection is proposed to simulate the working of the designed asynchronous SN P system. A quantitative analysis of EDSNP and the related methods for edge detection had been conducted to evaluate the performance of EDSNP. The performance of the EDSNP in processing the testing images is superior to the compared methods, based on the quantitative metrics of accuracy, error rate, mean square error, peak signal-to-noise ratio and true positive rate. The results indicate the potential of the temporal firing and the proper neuronal connections in the SN P system to achieve good performance in edge detection.

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.

Method of comparing and transforming images obtained using UAV

The subject matter of this article involves reviewing and developing methods for the comparison and transformation of images obtained using UAV via Computer Vision tools. The goal is to improve methods for image comparison and transformation. Various image-processing methods were employed to achieve the goal of this study,thereby contributing to the development of practical algorithms and approaches for image analysis and comparison. The tasks can be described as follows: 1) development of image comparison methods: design tools for the comparison of images from UAV that efficiently detect differences using algorithms such as cv2.absdiff and the PIL module; 2) Image transformation: implement transformation methods for images from UAV, including perspective transformation and thresholding, to enhance the quality and accuracy of image analysis. The methods used were algorithm development, image transformation methods, statistical analysis, experimental testing, and performance evaluation. The metrics used in this article are response time and accuracy. Algorithms for image comparison have also been refined, particularly those transformed through Global Threshold Value, Adaptive Mean Thresholding, and Adaptive Gaussian Thresholding. A novel change filtering method was introduced to enhance the precision of image comparison by filtering out insignificant alterations following image transformation. Comprehensive investigation of image comparison involving edge detection methods has been systematically presented. The results contain the development of practical algorithms and approaches for image analysis and comparison applicable in diverse areas such as military, security, and agriculture. Possibilities of applying our methods and algorithms in the context of drones were also considered, which is particularly relevant in tasks related to computer vision in unmanned aerial vehicles, where limited resources and the need for real-time processing of a large volume of data create unique challenges. Conclusions. The results contain OpenCV and PIL image comparison methods. OpenCV pixel-by-pixel comparison algorithm showed a better response time with the same accuracy. OpenCV method has 92,46% response time improvement compared with PIL and is 276ms. As for image thresholding with comparison, a method based on Global Threshold Value showed the shortest response time (266ms) and the lowest accuracy. The highest accuracy and response time (366ms) were obtained using the Adaptive Gaussian Thresholding method.

Sample-Based Gradient Edge and Angular Prediction for VVC Lossless Intra-Coding

Lossless coding is a compression method in the Versatile Video Coding (VVC) standard, which can compress video without distortion. Lossless coding has great application prospects in fields with high requirements for video quality. Since the current VVC standard is mainly designed for lossy coding, the compression efficiency of VVC lossless coding makes it hard to meet people’s needs. In order to improve the performance of VVC lossless coding, this paper proposes a sample-based intra-gradient edge detection and angular prediction (SGAP) method. SGAP utilizes the characteristics of lossless intra-coding to employ samples adjacent to the current sample as reference samples and performs prediction through sample iteration. SGAP aims to improve the prediction accuracy for edge regions, smooth regions and directional texture regions in images. Experimental results on the VVC Test Model (VTM) 12.3 reveal that SGAP achieves 7.31% bit-rate savings on average in VVC lossless intra-coding, while the encoding time is only increased by 5.4%. Compared with existing advanced sample-based intra-prediction methods, SGAP can provide significantly higher coding performance gain.

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.

Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.

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

A Multi-Branch Residual Network Based on Depth Correlation Features for the Classification of Chinese Ink Paintings

Despite significant strides in digital classification of Chinese ink paintings, existing methods predominantly rely on low-level features, insufficient for capturing the nuanced artistic styles of such works. This study introduces a novel multi-branch residual network that leverages depth correlation features to enhance the classification of Chinese ink paintings. We innovatively combine global style features, extracted using the Gram matrix, with local brushstroke features obtained via the Holistically-Nested Edge Detection(HED) method. This dual-feature approach addresses the limitations of previous studies by incorporating high-level stylistic nuances alongside low-level details, resulting in a more robust classification system. Quantitative results demonstrate a marked improvement in classification accuracy, with our network outperforming existing state-of-the-art models by significant margins in both artist and genre classification tasks. This advancement not only underscores the efficacy of integrating diverse feature sets but also paves the way for more sensitive and accurate management of digital art repositories.