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1
Agrawal, Himangi, and Krish Desai. "CANNY EDGE DETECTION: A COMPREHENSIVE REVIEW." International Journal of Technical Research & Science 9, Spl (2024): 27–35. http://dx.doi.org/10.30780/specialissue-iset-2024/023.
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Canny edge detection is a widely employed technique in image processing known for its effectiveness in identifying and highlighting edges within digital images. Because of its excellent performance, the Canny Edge Detector is one of the most used edge detection algorithms. For several image processing techniques, including image enhancement, image segmentation, tracking, and image/video coding, edge detection serves as a preliminary step. Compared to the Sobel algorithm, Canny's edge detection approach yields much lower memory requirements, reduced latency, and enhanced throughput without sacrificing edge detection performance. This paper provides a comprehensive review of canny edge algorithm, elucidating each step. In this paper, the canny edge algorithm is implemented on an image as well as in real time using MATLAB and its Simulink model. We have also performed high level synthesis of the proposed algorithm using HDL coder.
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HUSSAIN, MUHAMMAD, TURGHUNJAN ABDUKIRIM, and YOSHIHIRO OKADA. "WAVELET-BASED EDGE DETECTION IN DIGITAL IMAGES." International Journal of Image and Graphics 08, no. 04 (2008): 513–33. http://dx.doi.org/10.1142/s0219467808003210.
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This paper proposes a wavelet based multilevel edge detection method that exploits spline dyadic wavelets and a frame work similar to that of Canny's edge detector.2 Using the recently proposed dyadic lifting schemes by Turghunjan et al.1 spline dyadic wavelet filters have been constructed, which are characterized by higher order of regularity and have the potential of better inherent noise filtering and detection results. Edges are determined as the local maxima in the subbands at different scales of the dyadic wavelet transform. Comparison reveals that our method performs better than Mallat's and Canny's edge detectors.
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Luh Putu Risma Noviana, I Putu Eka Indrawan, and Gde Iwan Setiawan. "ANALYSIS OF CANNY EDGE DETECTION METHOD FOR FACIAL RECOGNITION IN DIGITAL IMAGE PROCESSING." Jurnal Manajemen dan Teknologi Informasi 15, no. 2 (2024): 29–34. http://dx.doi.org/10.59819/jmti.v15i2.4107.
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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.
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Latifa Khoirani, Rino Ariansyah, and Supiyandi Supiyandi. "Aplikasi Pengolahan Citra Untuk Peningkatan Deteksi Tepi Melalui Segmentasi Citra." Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer 2, no. 3 (2024): 196–203. http://dx.doi.org/10.61132/mars.v2i3.191.
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An important digital image processing is image segmentation, which separates objects from the background for further analysis. One segmentation technique is edge detection, which looks for boundaries between areas of different brightness. This article compares four edge detection methods: Roberts, Prewitt, Sobel, and Canny. The results show that, despite requiring more complex computations, Canny's method produces the sharpest and best connected edges; Sobel and Prewitt's method, on the other hand, is faster and simpler than Roberts' method, but is less effective in dealing with noise and often produces edges that are not connected to the plane. The choice of edge detection method depends on the application. Sobel and Prewitt are good for speed and stability, and Roberts is suitable for fast processing of images with minimal noise.
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Rachitha, M. Raikar, Vasanth Kavitha, and NR Deepak. "Road Detection Using Lane Detection Algorithms with Noise and Edge Detection Techniques." Recent Trends in Data Knowledge Discovery and Data Mining, no. 1 (February 14, 2025): 1–9. https://doi.org/10.5281/zenodo.14869492.
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<em>Road fault detection is an essential part of modern vehicle systems. Particularly in real-time vehicular ad hoc networks (VANETs), this study addresses the limitations of existing fault detection algorithms. This often presents lower performance in noisy and adverse environments such as fog, powder, shadows, potholes, oil slicks and tire skid marks. To overcome these challenges, We have implemented and evaluated advanced edge detection techniques. These techniques, which include Laplacian, Sobel, and Canny edge detection, are used on previously processed road photos. It focuses on minimizing the effect of noise on the detection process and isolating regions of interest (ROI). Comparative analysis draws attention to each technique's advantages and disadvantages. In noisy environments, Canny's edge detection performs better in terms of accuracy and resilience. The foundation for enhancing error detection systems is provided by these results. As a result, autonomous driving technology is more dependable and safer.</em>
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Yu, Zhongdang, and Hamid Reza Karimi. "Edge Detector Design Based on LS-SVR." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/419792.
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For locating inaccurate problem of the discrete localization criterion proposed by Demigny, a new criterion expression of “good localization” is proposed. Firstly, a discrete expression of good detection and good localization criterion of two dimension edge detection operator is employed, and then an experiment to measure optimal parameters of two dimension Canny's edge detection operator is introduced after. Moreover, a detailed performance comparison and analysis of two dimension optimal filter obtained via utilizing tensor product for one dimension optimal filter are provided which can prove that least square support vector regression (LS-SVR) is a smoothness filter and give the construct method of the derivate operator. This paper uses LS-SVR as the object function constructor and then realizes the approximation of two dimension optimal edge detection operator. This paper proposes the utility method of using singleness operator to realize multiscale edge detection by referencing the multiscale analysis technology of the wavelets theory. Experiment shows that the method has utility and efficiency.
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MYAKININ, OLEG O., DMITRY V. KORNILIN, IVAN A. BRATCHENKO, VALERIY P. ZAKHAROV, and ALEXANDER G. KHRAMOV. "NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION." Journal of Innovative Optical Health Sciences 06, no. 02 (2013): 1350009. http://dx.doi.org/10.1142/s1793545813500090.
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In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modifications. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre- and post-processing operations is demonstrated. Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.
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S, Britto Ramesh Kumar, and Bhuvaneshwari A. "AN EFFICIENT BRITWARI TECHNIQUE TO ENHANCE CANNY EDGE DETECTION ALGORITHM USING DEEP LEARNING." ICTACT Journal on Soft Computing 12, no. 3 (2022): 2634–39. http://dx.doi.org/10.21917/ijsc.2022.0377.
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Artificial Intelligence edge detection refers to a set of mathematical techniques used to recognize digital image locations. The picture brightness plays a vital role in detecting dissimilarities and making decisions. Edges are the sharp changes in pictures with respect to the brightness and are commonly categorized into a collection of curved line segments. The main focus of this paper is to find sharp corner edges and the false edges present in the MRI images. The canny edge algorithm is a popular method for detecting these types of edges. The traditional canny edge detection technique has various issues that are discussed in this paper. This study analyses the canny edge algorithm and enhances the smoothing filter, pixel identifier, and feature selection. The proposed Britwari technique, Tabu Search Heuristic Pattern Identifier (TSHPI) enhances the edge detection using SUSAN Filter. Feature Selection is performed to improvise the canny edge method. Deep Learning algorithm is used for classification of pre-trained neural networks to find a greater number of edge pixels. The implementation results show that the Britwari proposed technique (SUSAN Filter Tabu Search Heuristic Pattern Identifier Hill Climbing) reached better accuracy than the traditional Canny Edge Detection algorithms. The results produced better feature set selection using edge detection in MRI images.
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Sitanggang, Sarinah, and Paska Marto Hasugian. "Image Edge Detection for Batak Ulos Motif Recognition using Canny Operators." Login : Jurnal Teknologi Komputer 18, no. 01 (2024): 137–54. https://doi.org/10.58471/login.v18i01.42.
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Batak ulos is one of the handicrafts originating from North Sumatra. In Batak Ulos, there are various kinds of motifs that are characteristic of these ulos. One way to find out the type of ulos is by knowing the motives found on the ulos. For that we need a system that can detect ulos and then can recognize these ulos. The system was built using the canny edge detection method proposed by Jhon Canny in 1986, and is known as the optimal edge detection operator. Canny operator is one of edge detection which is very good in detecting image edges. Canny operators have met the criteria in detecting, namely detecting very well, responding well and localizing well and clearly. The system built also uses the C # programming language in Microsoft Visual Studio 2010.
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Jayasree, M., K. Narayanan N, V. Kabeer, and C. R. Arun. "An Enhanced Block Based Edge Detection Technique Using Hysteresis Thresholding." Signal & Image Processing : An International Journal (SIPIJ) 9, no. 2 (2019): 15–26. https://doi.org/10.5281/zenodo.3248683.
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Edge detection is a crucial step in various image processing systems like computer vision , pattern recognition and feature extraction. The Canny edge detection algorithm even though exhibits high accuracy, is computationally more complex compared to other edge detection techniques. A block based distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for edge detection depending on block type and the distribution of gradients in each block. A novel method of computation of high threshold has been proposed in this paper. Block-based hysteresis thresholds are computed using a non uniform gradient magnitude histogram. The algorithm exhibits remarkably high edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit quantifies the accuracy of the edge detector, which showed better values than that of original Canny and distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges for diverse block size.
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Deepak, Mathur *. Dr. Prabhat Mathur. "EDGE DETECTION TECHNIQUES IN IMAGE PROCESSING WITH ELABORATIVE APPROACH TOWARDS CANNY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 638–43. https://doi.org/10.5281/zenodo.55797.
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Edge is defined as the boundary pixels that connect two separate regions. Edges are local changes in the image intensity. Edges characterize boundaries and are therefore a problem of fundamental importance in image processing.. Edge detection plays a very important role in image processing The edges detected by algorithms are used by advanced computer vision,medical field,Brain Tumor detection,geologic formation extraction,biometrix and many more fields.In this article we are going to survey various edge detection techniques such as sobel, Prewitt, Robert,, Marr Hildrith and Canny operators.Althought each edge detection technique has its own merits and limitations .In this paper we would like to present various edge detection techniques with emphasis on Canny because the performance of canny edge detection technique is judged as the best in the field of image processing.
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Isar, Alexandru, Corina Nafornita, and Georgiana Magu. "Hyperanalytic Wavelet-Based Robust Edge Detection." Remote Sensing 13, no. 15 (2021): 2888. http://dx.doi.org/10.3390/rs13152888.
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The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.
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Wicaksono, Damar, Diva Putra Almeyda, Irfan Mikola Muldiyanto Putra, and Letty Malihatuningrum. "Analisis Perbandingan Metode Pra Pemrosesan Citra untuk Deteksi Tepi Canny pada Citra Berbagai Kondisi Jalan menggunakan Bahasa Pemrograman Python." JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) 7, no. 1 (2024): 17–31. http://dx.doi.org/10.34012/jutikomp.v7i1.3872.
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Edge detection is one of the essential techniques in digital image processing used to identify sudden changes in pixel intensity in an object. In the context of road marking detection on highways, accurate edge detection plays a crucial role in improving motorist safety and navigation. The Canny Edge Detection method has been proven effective in detecting edges with high accuracy in digital image processing. However, applying Canny Edge Detection on road images in various conditions still requires further research. This research aims to implement the Canny Edge Detection method in road marking detection on highway images. The main stages of this research include image pre-processing, where noise is removed, and the image is converted into a grayscale image to prepare the image before edge detection using the Canny method. In addition, a comparison will be made with several other image pre-processing methods, such as median and bilateral blur, to determine the most effective method for edge detection on road markings. Based on the research results, applying the Canny Edge Detection method with pre-processing using median blur is a practical approach to road marking detection on highway images. This method can produce accurate and optimized edge detection, which can be the basis for developing automatic road marking detection systems on highways. The findings can contribute to the improvement of motorist safety and navigation as well as the development of more accurate and effective edge detection technology on road markings.
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Hamdani, Ibnu Mansyur, Ismi Rizqa Lina, and Muhammad Takdir Muslihi. "Deteksi Tepi Optimal dengan Integrasi Canny, CLAHE, dan Perona-Malik Diffusion Filter." Jurnal Mosfet 5, no. 1 (2025): 127–36. https://doi.org/10.31850/jmosfet.v5i1.3638.
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Edge detection is a fundamental technique in digital image processing, crucial for identifying object boundaries. However, detecting edges in low-intensity and noisy images remains a significant challenge. This study proposes an optimal edge detection method by integrating the Canny algorithm, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Perona-Malik Diffusion Filter, with automatic kappa (k) value determination using the Fractional Order Sobel Mask. The process begins with noise reduction through the Perona-Malik Diffusion Filter, followed by local contrast enhancement using CLAHE, and concludes with edge detection via the Canny algorithm. Experimental results demonstrate that the proposed method significantly enhances edge clarity and robustness against noise compared to the conventional Canny algorithm, particularly for low-intensity images and images with noise. Tests on leaf and medical images confirm the effectiveness of this method in improving edge detection quality in digital images.
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Mole S S, Sreeja. "RAPID BLEEDING REGION DETECTION IN WIRELESS CAPSULE ENDOSCOPY VIDEOS." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 8 (2017): 6389–92. http://dx.doi.org/10.24297/jac.v13i8.5757.
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Wireless Capsule Endoscopy (WCC) is a medical imaging technique used to examine parts of the gastrointestinal tract. Computer aided detection is used to increase the speed of detection, better performance and reduce the time. Before finding the bleeding regions the edge regions are first detected and removed. Both the edge and the bleeding regions will share the same Hue value and the luminance should be same for the bleeding and the non -bleeding regions .We use a canny edge detector operator for detecting the edge regions in L channel. Canny edge detector is used to detect more edge pixels and preserve more bleeding pixels based up on canny edge algorithm. This method in edge removal algorithm includes edge detection, edge dilation and edge masking. After the removal of edges, those regions are made in to segment through super-pixel segmentation and regions are classified using Artificial Neural Network by Radial Bias Function (RBF).Â
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Ramgundewar, Pallavi, S. P. Hingway, and K. Mankar. "Design of Modified Canny Edge Detector Based on FPGA for Portable Device." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 7 (2015): 06–11. http://dx.doi.org/10.53555/nneee.v2i7.184.
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Edge detection is one of the key stages in image processing and object reorganization. The Canny Edge Detector is one of the most widely used edge detection algorithm due to its superior performance. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-based Canny algorithm. Directly applying the original Canny Edge detection algorithm at the blocklevel leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a modified Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. Here we propose the design of modified Canny Edge detection algorithm that results in significantly reduced memory requirement, decrease in latency, increase throughput, with no loss in edgedetection performance as compare to original Canny Detector Algorithm. Here we are using matlab to convert image into text/pixel value.
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Kieu, S. T. H., A. Bade, and M. H. A. Hijazi. "Modified canny edge detection technique for identifying endpoints." Journal of Physics: Conference Series 2314, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2314/1/012023.
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Abstract 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.
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Tengku Alang, Tengku Ahmad Iskandar, Tan Tian Swee, Tan Jia Hou, et al. "Global Canny algorithm based on Canny edge detector framework in magnetic resonance imaging." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4-2 (2017): 445–51. http://dx.doi.org/10.11113/mjfas.v13n4-2.761.
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Magnetic resonance imaging is an important modality in the diagnosis and pathology detection. Edge detection is used for image segmentation and feature extraction as part of the medical image analysis. There is no ideal and universal algorithm which performs perfectly under all conditions. Conventional Canny edge detector is not suitable to be used in Magnetic resonance images that contaminated by Rician noise. In this paper, we propose the use of customized non-local means into the Canny edge detector instead of Gaussian smoothing in the conventional Canny edge detector to effectively remove Rician noise while preserving edges in Magnetic resonance image of an internal organ. The result shows that our method can yield better edge detection than conventional method, with minimal false edge detection. The proposed method undergoes several attempts of parameter adjustment to detect true edges successfully using optimal parameter setting.
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Zhang, Liu, Liu, Li, and Ye. "Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model." Symmetry 11, no. 4 (2019): 557. http://dx.doi.org/10.3390/sym11040557.
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The symmetrical difference kernel SAR image edge detection algorithm based on the Canny operator can usually achieve effective edge detection of a single view image. When detecting a multi-view SAR image edge, it has the disadvantage of a low detection accuracy. An edge detection algorithm for a symmetric difference nuclear SAR image based on the GAN network model is proposed. Multi-view data of a symmetric difference nuclear SAR image are generated by the GAN network model. According to the results of multi-view data generation, an edge detection model for an arbitrary direction symmetric difference nuclear SAR image is constructed. A non-edge is eliminated by edge post-processing. The Hough transform is used to calculate the edge direction to realize the accurate detection of the edge of the SAR image. The experimental results show that the average classification accuracy of the proposed algorithm is 93.8%, 96.85% of the detection edges coincide with the correct edges, and 97.08% of the detection edges fall into the buffer of three pixel widths, whichshows that the proposed algorithm has a high accuracy of edge detection for kernel SAR images.
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Diman, Suhardiman, Zahir Zainuddin, and Salama Manjang. "Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method." EPI International Journal of Engineering 2, no. 2 (2019): 139–44. http://dx.doi.org/10.25042/epi-ije.082019.08.
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Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV. The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.
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Wang, Zhicheng, Zhe Wang, Nanhai Huang, and Jie Zhao. "An Improved Canny-Zernike Subpixel Detection Algorithm." Wireless Communications and Mobile Computing 2022 (June 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/1488406.
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This paper proposed a new subpixel detection method that detects subpixel edges directly, as opposed to the previous method, which requires crossing the entire image. It shows superior subpixel detection on the edge directly, which enhances subpixel edge detection speed significantly. In order to overcome the problem of noise reduction, this paper employs bilateral filtering. The method first performed coarse localization with the improved operator to determine the coordinates and gradient direction of the edge points. Then, the Zernike moment algorithm was used for subpixel repositioning of edge points. Finally, subpixel level edge positioning of the image is obtained. The detection algorithm is used to identify the edges of large-size workpieces, and the results reveal that the approach has superior positioning accuracy, noise immunity, and fast detection speed.
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Onyedinma Ebele G., Asogwa Doris C., and Onwumbiko Joy N. "Exploring the Effectiveness of Sobel, Canny, and Prewitt Edge Detection Algorithms on Digital Images." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1722–30. https://doi.org/10.30574/wjaets.2025.15.1.0346.
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Edge detection is a fundamental process in image processing, crucial for identifying object boundaries and structural features within images. This study explores three classical edge detection techniques - Canny, Sobel, and Prewitt. Six test images were used to ascertain their performance based on five metrics: Recall, Precision, F1-Score, Structural Similarity Index (SSIM), and Figure of Merit (FoM) implemented using python. The experimental results indicate that the Canny operator consistently outperforms the others in terms of Recall, F1-Score, and FoM, demonstrating superior capability in detecting true edges with high sensitivity and robustness against noise. The Sobel operator achieves the highest Precision and SSIM scores, reflecting strong edge localization and structural preservation, although with lower overall edge detection effectiveness. The Prewitt operator offers balanced performance across all metrics, providing a compromise between detection quality and computational simplicity. These findings are consistent with general observations from the literature, where Sobel is recognized for its noise resistance and simplicity, making it suitable for fast, real-time applications, while Prewitt, offering a similarly straightforward implementation, exhibits slightly greater sensitivity to noise. The Canny operator, widely regarded as the optimal edge detector, remains the preferred method for applications requiring high precision, low error rates, and strong edge continuity. Consequently, Canny is best suited for high-accuracy edge detection tasks, Sobel excels in structure-preserving applications, and Prewitt is recommended for general-purpose, resource-constrained scenarios.
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Chhetri, Chhetra Bahadur, and Manish Pandey. "Performance Evaluation of Different Images Using Edge Detection Algorithms." National College of Computer Studies Research Journal 2, no. 1 (2023): 34–48. http://dx.doi.org/10.3126/nccsrj.v2i1.60053.
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To determine which edge detection method performs best and worst on different image types, numerous edge detection algorithms are examined. For the performance analysis, some sample photos from the web and some from Java are used as sources. The entropy and signal noise ratio are used to gauge how well the edged image performs. In image processing, conducting a thorough investigation of various edge detection techniques is highly worthwhile two widely used edge detection algorithms Log, and Canny—are taken into consideration in this analysis. Here in this paper, the analysis is focused on the performance of different edge detector algorithms. All candidate algorithms of edge detection are implemented in JAVA. The result of empirical performance shows that two variants namely canny perform better results for the edge detection algorithm. The result shows that when considering only the performance aspect. Cycle/byte is calculated for comparing different variants. Cycle/byte is decreased when the canny edge detector is examined. The canny edge detection algorithm shows a better performance than LoG. LoG has more than 3 times higher cycle/byte than Canny Edge detection.
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KAUR, AMANDEEP, and CHANDAN SINGH. "A HYBRID EDGE DETECTOR USING FUZZY LOGIC AND MATHEMATICAL MORPHOLOGY." International Journal of Image and Graphics 10, no. 02 (2010): 251–72. http://dx.doi.org/10.1142/s0219467810003767.
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Edge detection concerns the localization of significant variations of the grey level image. Detection of edges in an image is very important step for a complete image understanding system. This paper proposes a new approach to edge detection which adopts fuzzy reasoning to detect edges and mathematical morphology for edge thinning. The results achieved by this algorithm are comparable to the Canny approach. In Canny edge detector we may require many runs using different combinations of the three parameters (two threshold and one sigma values) whereas in the proposed technique only one parameter needs to be set by the user coarsely to get the same results, also the computation load of Canny is higher than the proposed approach.
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M. Almufti, Saman. "Hybridizing Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques." Academic Journal of Nawroz University 11, no. 2 (2022): 135–45. http://dx.doi.org/10.25007/ajnu.v11n2a1320.
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Ant colony optimization is a swarm intelligent algorithm that mimics the ant behaviors to optimize solutions for hard optimization problems. Over years Ant-based algorithms have been used in solving different problems including: Traveling Salesman Problem (TSP), Wireless Sensors Network (WSN), Benchmark Problem, and it has been used in various image processing applications. In the image processing fields various techniques have been used to detect edges in a digital image such as Canny and Sobel edge detectors. This Study, proposed a hybridized Ant Colony Optimization algorithm for optimizing the edge detector quality. The proposed method initializes its attribute matrix and the information at each pixel routed by ants on the input image. Experimental results show the results of the proposed algorithm and compare the results with the original built-in MATLAB edge detection method called Canny and the results of basic Aco edge detector. All three algorithms tested in different images and the MSE and PNSR are calculated before and after applying Gaussian noise. Based on the Experimental results obtained by the three used methods (Canny Edge Detector, Ant Colony Optimization, and Hybrid Aco-Canny), the proposed Hybrid ACO-CANNY methods was the best method for detecting edges.
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Supriyatin, Wahyu. "Perbandingan Metode Sobel, Prewitt, Robert dan Canny pada Deteksi Tepi Objek Bergerak." ILKOM Jurnal Ilmiah 12, no. 2 (2020): 112–20. http://dx.doi.org/10.33096/ilkom.v12i2.541.112-120.
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Computer vision is one of field of image processing. To be able to recognize a shape, it requires the initial stages in image processing, namely as edge detection. The object used in tracking in computer vision is a moving object (video). Edge detection is used to recognize edges of objects and reduce existing noise. Edge detection algorithms used for this research are using Sobel, Prewitt, Robert and Canny. Tests were carried out on three videos taken from the Matlab library. Testing is done using Simulik Matlab tools. The edge and overlay test results show that the Prewitt algorithm has better edge detection results compared to other algorithms. The Prewitt algorithm produces edges whose level of accuracy is smoother and clearer like the original object. The Canny algorithm failed to produce an edge on the video object. The Sobel and Robert algorithm can detect edges, but it is not clear as Prewitt does, because there are some missing edges.
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Sneha, Kumari *. "PERFORMANCE EVALUATION AND EFFECTIVE ANALYSIS OF EDGE DETECTION ALGORITHMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 282–86. https://doi.org/10.5281/zenodo.809170.
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Edge detection in a digital image is one of the important jobs in digital image processing. Edges in the image are the significance of discontinuity present in the image. Detecting the accurate edges or boundaries ease the location of objects in the image and parameters like shape, area can be measured easily. This paper presents a brief study on different edge detection techniques like Canny Operator, Sobel Operator, Prewitt Operator and Roberts Operator. Quality Assessment research is to measure the image quality. Unclear boundaries are produced due to low quality and other possible factors present in the image. Brief analysis of different edge detection algorithms are discussed here. The experimental results are produced and validated with the help of MATLAB Software
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Cao, Jianfang, Lichao Chen, Min Wang, and Yun Tian. "Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform." Computational Intelligence and Neuroscience 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/3598284.
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The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator’s dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.
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Wang, Zhenyu, Mingshun Yang, Leijie Ren, et al. "An Improved BM3D-Canny-Zernike Algorithm for Micro-Size Detection of Electronic Connectors." Traitement du Signal 39, no. 3 (2022): 899–906. http://dx.doi.org/10.18280/ts.390315.
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To detect the micro-size injection molded parts of electronic connectors, this paper establishes a complete size detection system based on machine vision, and measures the size through image acquisition and processing, according to the features of the injection molded parts. The proposed system is called the improved BM3D-Canny-Zernike algorithm. Specifically, the traditional block matching and three-dimensional filtering (BM3D) image denoising algorithm was improved to optimize the peak signal-to-noise ratio (PSNR) and reduce the mean squared error (MSE). Then, the Canny algorithm was improved for pixel-level edge detection, and the Zernike moment is improved for detecting edges on the subpixel-level more effectively and reducing the calculation amount. Finally, the least squares method was employed to fit the edge to be measured. The exact pixel length was obtained by solving the function of different edges, thereby realizing size measurement. Experimental results show that the mean error percentage of our algorithm was 8.73%, which meets the needs of industrial detection.
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Pinastawa, I. Wayan Rangga, Musthofa Galih Pradana, and Khoironi Khoironi. "Edge Detection Model Performance Using Canny, Prewitt and Sobel in Face Detection." Sinkron 8, no. 2 (2024): 623–31. http://dx.doi.org/10.33395/sinkron.v8i2.13497.
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Detection of objects in the form of objects, humans and other objects at this time has been widely applied in many aspects of life. The help of this technology can facilitate human work, one of which is facial detection to get information about a person's identity. Face identification and detection is closely related to Data Mining science with Image Processing sub-science. This facial detection and recognition can use several technical approaches, one of which is to use edge detection. Edge detection is one of the basic operations of image processing. In the image classification process, edge detection is required before image segmentation processing. There are several methods that can be used to perform edge detection such as Canny, Prewitt and Sobel. These three methods are methods that have accurate and good detection results, with the advantages of each method having its own added value. From the results of previous studies that stated these three methods have good results, it became interesting to conduct a comparative study of these three methods in detecting edges in facial images. Edge detection applied to this study identifies facial images, and will get similarities with the original image from the result analysis process, and is reinforced by measurement results using the Mean Square Error error degree. The final result of this study states that this study the most optimal Mean Square Error measurement results obtained the final results in the Canny method of 10, the Prewitt method of 41 and Sobel of 29. These results show that the value of the Canny method has the smallest Mean Square Error value, which indicates that the Canny method on facial image edge detection has the most optimal results.
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Sundari M, Vana. "VLSI Architecture Design and Implementation of CANNY Edge Detection." Journal of University of Shanghai for Science and Technology 24, no. 02 (2022): 200–208. http://dx.doi.org/10.51201/jusst/22/0167.
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In One of the most essential methods in digital image processing is edge detection. Because of its capacity to detect edges even in images heavily contaminated by noise, the Canny edge detector is the most widely used edge detection algorithm. A modified canny edge detector is created in MATLAB and implemented in FPGA in this project. The mask is used for gradient calculation, and bilinear interpolation of four pixels is used in non-maximal suppression. In the iris detection subsystem, this edge detector is used as a pre-processing stage. The goal of creating the hardware modules for the canny edge detector was to minimise its complexity, improve its performance, and make it appropriate for VLSI implementation on a reconfigurable FPGA-based platform.
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Samuel, Moveh, Maziah Mohamad, Shaharil Mad Saad, and Mohamed Hussein. "Development of Edge-Based Lane Detection Algorithm using Image Processing." JOIV : International Journal on Informatics Visualization 2, no. 1 (2018): 19. http://dx.doi.org/10.30630/joiv.2.1.101.
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Image processing is known as the process of converting an image into a digital form so as to obtain an enhanced image and to extract useful information from it. This paper presents a simple step by step analysis of edges-based lane detection. Some of the known and common edge detection techniques such as Sobel, Canny, Prewitt and Roberts were studied and evaluated using image segmentation, morphology, image statistic and Hough Transform. The result indicated some similarities in the process as well as major differences. These differences were observed to be as a result of the high sensitivity of the edge detector in detecting noise such as cast shadows and unmarked lanes. This could be noticed in the case of canny edge detector. Also these data could be considered in the development of a multi-system edge detector, which could be used to analyze various road scenes and runs the appropriate edge detector best suited for the current situation.
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Qian, Yue Jing, and Yuan Fan. "Rice Edges Detection Based on Canny Operator." Advanced Materials Research 542-543 (June 2012): 1302–5. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1302.
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Rice edge detection is the first step on obtaining rice image feature. In this paper, an improved Canny edge detection algorithm was represented to obtain thin and robust rice edges. Firstly, nonlinear diffusion filter was used to wipe of noise efficiently and kept the edge information of the image. Secondly, gradient calculation of pixel diagonal direction was considered in the calculation of neighborhood gradient amplitude which further repressed the impact of noise. Thirdly, using average interclass variance could self-adaptively calculate the double thresholds for different images. The results of the experiment indicate that the improved algorithm has a better accuracy and precision in the edge detection.
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Yu Xinshan, 于新善, 孟祥印 Meng Xiangyin, 金腾飞 Jin Tengfei та 罗锦泽 Luo Jinze. "基于改进Canny算法的物体边缘检测算法". Laser & Optoelectronics Progress 60, № 22 (2023): 2212002. http://dx.doi.org/10.3788/lop223400.
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Saluky and Yoni Marine. "PENERAPAN ALGORITMA DETEKSI TEPI CANNY MENGGUNAKAN PYTHON DAN OPENCV." Smart Techno (Smart Technology, Informatics and Technopreneurship) 5, no. 1 (2023): 1–7. http://dx.doi.org/10.59356/smart-techno.v5i1.73.
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Abstract: Object detection is one step in object recognition in the field of computer vision. The edges of the image characterize the boundaries that distinguish it from other objects and are therefore a very important problem in image processing. Accurate Image Edge Detection can significantly reduce the amount of data and filter out useless information while retaining important structural properties in the image. Since edge detection is at the forefront of image processing for object detection, it is very important to have a good understanding of edge detection algorithms. In this study, applying canny edge detection using python and OpenCV and also compared with other image processing methods. The result is that Canny edge detection has a better performance compared to other algorithms such as LoG (Laplacian of Gaussian), Robert, Prewitt and Sobel.
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Liang, Ying Bo, and Li Hong Zhang. "Multi-Dimension and Structure Element Edge-Detection Based on Mathematical Morphology." Advanced Materials Research 490-495 (March 2012): 919–21. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.919.
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A novel multi-dimension and structure element edge-detection based on mathematical morphology is presented to resolve blur problem of classical morphology when detecting an edge to reduce the noise but hard to preserve the details and edge information of the original image effectively. First,pretreatment of the image are completed by close-open operation to eliminate noise; second,do close operation to smooth image,in the end,using the operation of morphological gradient for smooting image,the ideal image edge under the environment of existing noise is obtained,and it is applied to detect the edges of welding pore images. The experimental results show that it is compared with classical Sobel operators,Canny operators and traditional edge detection algorithm, the proposed algorithm has the following distinguished advantages:accuracy of edges detected, a clear outline of the image, and can preserve more image details as well, and insensitive to noise.
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Tuyet, Vo Thi Hong. "Edge detection based on augmented lagrangian method for lowquality medical images." ENGINEERING AND TECHNOLOGY 8, no. 1 (2020): 106–15. http://dx.doi.org/10.46223/hcmcoujs.tech.en.8.1.910.2018.
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Medical images are useful for the treatment process. They contain a lot of information on displaying abnormalities in your body. The contour of medical images is a matter of interest. In there, edge detection is a process prepared for boundaries. Therefore, the edge detection of medical images is very important. Other previous methods must sacrifice time for the accurate results. It is because the medical images in the real world have many impurities. In this paper, I propose a method of detecting edges in medical images which have impurities by using augmented lagrangian method to improve the Canny algorithm. My algorithm improves the ability to detect edges faster. Compared with other recent methods, the proposed method is more efficient.
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Abid Hasan, Syed Mohammad, and Kwanghee Ko. "Depth edge detection by image-based smoothing and morphological operations." Journal of Computational Design and Engineering 3, no. 3 (2016): 191–97. http://dx.doi.org/10.1016/j.jcde.2016.02.002.
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Abstract Since 3D measurement technologies have been widely used in manufacturing industries edge detection in a depth image plays an important role in computer vision applications. In this paper, we have proposed an edge detection process in a depth image based on the image based smoothing and morphological operations. In this method we have used the principle of Median filtering, which has a renowned feature for edge preservation properties. The edge detection was done based on Canny Edge detection principle and was improvised with morphological operations, which are represented as combinations of erosion and dilation. Later, we compared our results with some existing methods and exhibited that this method produced better results. However, this method works in multiframe applications with effective framerates. Thus this technique will aid to detect edges robustly from depth images and contribute to promote applications in depth images such as object detection, object segmentation, etc. Highlights A method is proposed that can detect edges from depth images more profoundly. We modified the Canny edge detection method using morphological operations. The proposed method works in multi-frames.
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Yan, Wen Zhong, and Da Zhi Deng. "Study of Image Edge Detection Techniques." Advanced Materials Research 505 (April 2012): 393–96. http://dx.doi.org/10.4028/www.scientific.net/amr.505.393.
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Edges characterize boundaries. Edge detection is a problem of fundamental importance in image processing. The key of edge detection for image is to detect more edge details, reduce the noise impact to the largest degree. In this paper the comparative analysis of various image edge detection techniques is presented. In order to evaluate these techniques, they are used to detect the edge of chromosome image. Firstly, the iterative thresholding algorithm and morphologic erode algorithm together are applied to enhance both the edges of the chromosomes and the contrast of the image. Then, Sobel operator technique, Roberts technique, Prewitt technique and Canny technique are used respectively to detect the edges of the chromosomes in the image.
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Ibrahim, Yahya Ismail, and Israa Mohammed Alhamdani. "Practical study for comparing edge detection filters in digital image processing." Tikrit Journal of Pure Science 28, no. 5 (2023): 201–15. http://dx.doi.org/10.25130/tjps.v28i5.1583.
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Edge detection is a fundamental and important tool in image processing and computer vision. The research topic discusses edge extraction of a digital image using various digital image processing techniques. Digital image edge identification using variety of digital image processing methods. The most popular technique for identifying discontinuities in intensity levels is edge detection. The actual image contains noise that could affect the digital image's quality. Some edge detection filters were used such as Canny, Sobel, and Prewitt. Laplace of Gaussian (LOG) edge recognition, Robert boundary detection, zero edge recognition and analysis. In light of the results of the comparison of edge filters the best edge filter had been obtained. Also, a hybrid method that merges between Canny filter and morphology operations were produced for edge detection. The research aims to reach an appropriate image purification by using different types of filters and to reduce distortions at the edges of the image and to determine which type of filter is the best. As well as, a comparison operation is applied between the traditional method and the hybrid method. Following the results explained by the filters used and displaying the resulting images. The results evaluated from hybrid filter show that this method is optimal than the traditional filters based on the Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) with a value equals 43.43. A smaller score indicates better perceptual quality. Also, the edged images quality consequences from the traditional filters were measured by this metric. The results produced from the traditional filters prove that the best filter for edge image detection is Canny filter based on Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) that reaches 46.01. In addition, the correlation coefficient was estimated to find which of the resulting images are better and closer to the original image. While for correlation coefficient the better filter was Prewitt filter with 0.3133 value. This study program was applied under the MATLAB R2020b system.
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Zou, Lin, Yunlong Zheng, and Jie Lu. "An Edge Detection Method for Welding Pool Based on An Improved Canny Algorithm." Journal of Physics: Conference Series 2785, no. 1 (2024): 012013. http://dx.doi.org/10.1088/1742-6596/2785/1/012013.
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Abstract In this study, an improved Canny algorithm is proposed for detecting welding pool edges in complex environments, considering the issues of inaccurate extraction, discontinuity, and susceptibility to noise in traditional edge detection algorithms. Firstly, the original Gaussian filter is replaced with a hybrid filter that combines dark channel prior defogging and bilateral filtering used for image denoising. Then, a four-direction Sobel operator is utilized to calculate gradient amplitude and direction. Furthermore, the double threshold required for extracting and connecting pool edges is obtained by using an enhanced quadratic Otsu algorithm. Experimental results demonstrate that, compared with the traditional algorithm, the improved Canny algorithm achieves an approximately 51.6% increase in PSNR and about 0.04 improvement in SSIM, enabling more accurate and comprehensive extraction of welding pool edge information.
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Livingston, Merlin L. M., Senthil C. Singh, K. Manojkumar, and Sathish S. Kumar. "A Study on Parallel and Pipelining Simulation Techniques for Edge Detection and Their Performance Analysis." Journal of Computational and Theoretical Nanoscience 16, no. 2 (2019): 568–72. http://dx.doi.org/10.1166/jctn.2019.7770.
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Real processing components along with component simulators are combined together to construct a new virtual prototyping system. The increase in component simulators result in degraded performance of the simulation in distributed systems. The speed of simulation can be increased by doing parallel simulation techniques. Prime number test and Image edge detection are chosen to implement the parallel simulation techniques and achieved the expected results while implementing in real time applications. The prime number test calculates the number of processors in a system and the image edge detection can be done in two stages by Canny Edge detection and Sobel Edge detection. The Canny Edge detection is used to detect the edges in the images by using a multi-stage algorithm. The smaller, separable and integer valued filter in images are combined in horizontal and vertical directions by using the Sobel edge detection resulting in reduction of implementation cost. The tool named OpenMP is used for implementing the parallel simulation techniques by combining both the canny edge and Sobel edge detection. An add-on named MPI is used along with the OpenMP to reduce the implementation time in parallel processing.
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Chao, Yuan, Fan Shi, Wentao Shan, and Dong Liang. "Edge Location and Identification Method for Electronic Components based on Improved Canny Algorithm." International Journal of Circuits, Systems and Signal Processing 16 (January 10, 2022): 209–14. http://dx.doi.org/10.46300/9106.2022.16.25.
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The position identification of SMD electronic components mainly uses Canny edge detection algorithm to detect the edges of specific elements, benefited from its computational simplicity. The traditional Canny algorithm lacks the adaptability in gradient calculation and double thresholds selection, which may affect the location and identification accuracy of specific elements in electronic components. In this paper, an improved canny edge detection algorithm is proposed. The gradient magnitude is calculated in four directions, i.e., horizontal, vertical, and diagonal. Both the high and low thresholds can be adaptively determined based on the grayscale distribution information, to increase the adaptability of edge identification. The experimental results show that the proposed method can better locate the true edges of specific elements in electronic components with a reasonable processing speed, compared with the traditional Canny algorithm, and has been successfully applied on practical real-time vision inspection on SMD electronic components.
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Shambetova, Burul, and Mekia Shigute Gaso. "ANALYSIS OF EDGE DETECTION METHODS IN IMAGE PROCESSING." Alatoo Academic Studies 23, no. 2 (2023): 519–26. http://dx.doi.org/10.17015/aas.2023.232.50.
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The primary goal of computer vision is to interpret the contents of an image, which can be achieved through image segmentation. This technique involves dividing an image into meaningful regions based on the intended application. By detecting and outlining the edges of objects, we can identify them within the image. Edges refer to the boundaries between objects and the background, as well as the boundaries between overlapping objects. Through image segmentation, we can separate the image from the background and extract valuable information. Edge detection is a crucial step in image segmentation, as it involves identifying and locating abrupt changes in the image. This article analyzes various edge detection techniques, such as Sobel, Prewitt, Roberts, Canny, and Laplacian Gaussian (LoG), using an esophageal image in Python.
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Ravi, Babu Devareddi, and Srikrishna Atluri. "Silhouette vanished contour discovery of aerial view images by exploiting pixel divergence." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1312–22. https://doi.org/10.11591/ijai.v12.i3.pp1312-1322.
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An image's edge detection is the process of finding and pinpointing sharp discontinuities in an image. Detecting the edges of an image significantly reduces the quantity of data and removes unnecessary information while keeping the fundamental structural aspects of an image. Edge detection is essential when it comes to image categorization in computer vision and object identification. The primary goal of this research is to investigate several strategies for edge detection and shadow of objects in aerial view images. Machine vision, face detection, medical imaging, and object detection are just a few examples of applications where image segmentation has been widely utilized. Image segmentation is categorizing or identifying sub-patterns in given an image. Many algorithms and strategies for picture segmentation have been presented to improve segmentation issues in each application area. Techniques such as threshold-based and region-based picture segmentation were used in this study. An edge detection method such as Sobel, Prewitt and Roberts and the Canny approach is applied to the benchmark image and compared with the proposed octagonal pixel divergence edge detection (OPDED) algorithm. Results show that the proposed approach is more effective than the other methods, with a quality image with edges.
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Zhang, Jiahao, Wei Wang, and Jianfei Wang. "Edge Detection in Colored Images Using Parallel CNNs and Social Spider Optimization." Electronics 13, no. 17 (2024): 3540. http://dx.doi.org/10.3390/electronics13173540.
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Edge detection is a crucial issue in computer vision, with convolutional neural networks (CNNs) being a key component in various systems for detecting edges within images, offering numerous practical implementations. This paper introduces a hybrid approach for edge detection in color images using an enhanced holistically led edge detection (HED) structure. The method consists of two primary phases: edge approximation based on parallel convolutional neural networks (PCNNs) and edge enhancement based on social spider optimization (SSO). The first phase uses two parallel CNN models to preliminarily approximate image edges. The first model uses edge-detected images from the Otsu-Canny operator, while the second model accepts RGB color images as input. The output of the proposed PCNN model is compared with pairwise combination of color layers in the input image. In the second phase, the SSO algorithm is used to optimize the edge detection result, modifying edges in the approximate image to minimize differences with the resulting color layer combinations. The experimental results demonstrate that our proposed method achieved a precision of 0.95. Furthermore, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values stand at 20.39 and 0.83, respectively. The high PSNR value of our method signifies superior output quality, showing reduced contrast and noise compared to the ground truth image. Similarly, the SSIM value indicates that the method’s edge structure surpasses that of the ground truth image, further affirming its superiority over other methods.
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Zuraini, Othman, Abdullah Azizi, Kasmin Fauziah, and Sakinah Syed Ahmad Sharifah. "Road crack detection using adaptive multi resolution thresholding techniques." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 4 (2019): 1874–81. https://doi.org/10.12928/TELKOMNIKA.v17i4.12755.
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Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist’s knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5x5 partitioned image outperforming the other partitioned scales.
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R G, Ragi, Jayaraj U Kidav, and Roshith K. "VLSI Architecture Design and Implementation of CANNY Edge Detection Subsystem." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 143–50. https://doi.org/10.21275/sr21301101835.
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Li, Qinghua, Wanting Zhao, Siyuan Cheng, and Yi Ji. "Research on Concentricity Detection Method of Automobile Brake Piston Parts Based on Improved Canny Algorithm." Applied Sciences 15, no. 8 (2025): 4397. https://doi.org/10.3390/app15084397.
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The automotive brake piston component is an important part of the automotive brake system, and the concentricity detection of the first piston component is crucial to ensure driving safety. In this paper, an improved Canny algorithm is proposed for non-contact detection of spring concentricity of the first piston component. Firstly, the traditional Canny algorithm is improved by replacing the Gaussian filter with a bilateral filter to fully retain the edge information, and accurate edge detection results are obtained by constructing a multi-scale analysis. After obtaining the edge images, a sub-pixel edge detection method with gray moments is introduced to optimize these edges; secondly, a circle is fitted to the extracted edge points by using the RANSAC algorithm to determine the center position and radius of the circle; and finally, the concentricity of the first piston part is calculated based on the fitting results. The experimental results are compared with those of the CMM and the traditional Canny algorithm, and the results show that the improved Canny algorithm reduces the coaxiality error by 4% and enables effective measurement of the concentricity of the first piston assembly spring.
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Babu Devareddi, Ravi, and Atluri Srikrishna. "Silhouette vanished contour discovery of aerial view images by exploiting pixel divergence." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1312. http://dx.doi.org/10.11591/ijai.v12.i3.pp1312-1322.
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<span lang="EN-US">An Image's Edge Detection is the process of finding and pinpointing sharp discontinuities in an image. Detecting the edges of an image significantly reduces the quantity of data and removes unnecessary information while keeping the fundamental structural aspects of an image. Edge detection is essential when it comes to image categorization in computer vision and object identification. The primary goal of this research is to investigate several strategies for edge detection and shadow of objects in Aerial view Images. Machine vision, face detection, medical imaging, and object detection are just a few examples of applications where image segmentation has been widely utilized. Image segmentation is categorizing or identifying sub-patterns in a given image. Many algorithms and strategies for picture segmentation have been presented to improve segmentation issues in a given application area. Techniques such as threshold-based and region-based picture segmentation were used in this study. An edge detection method such as Sobel, Prewitt and Roberts and the canny approach is applied to the benchmark image and compared with the proposed Octagonal Pixel Divergence Edge Detection (ODED) algorithm. MATLAB R2018b was used for this research, and findings show that the proposed approach is more effective than the other methods, with a quality image with edges. </span>