To see the other types of publications on this topic, follow the link: Canny's edge detection method.
Journal articles on the topic 'Canny's edge detection method'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Canny's edge detection method.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full textAbstract:
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.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
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.
Full textAbstract:
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.
5
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.
Full textAbstract:
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.
6
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.
Full textAbstract:
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.
7
Pahadiya, S., and R. Khatri. "Compare Modify Canny Edge Detection Method with Existing Edge Detection Methods." International Journal of Computer Sciences and Engineering 6, no. 2 (2018): 337–40. http://dx.doi.org/10.26438/ijcse/v6i2.337340.
Full text
8
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.
Full textAbstract:
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.
9
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.
Full textAbstract:
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%.
10
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.
Full textAbstract:
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.
11
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.
Full textAbstract:
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.
12
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.
Full textAbstract:
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.
13
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.
Full textAbstract:
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.
14
Sun, Qindong, Yimin Qiao, Hua Wu, and Jiamin Wang. "An Edge Detection Method Based on Adjacent Dispersion." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 10 (2016): 1655026. http://dx.doi.org/10.1142/s0218001416550260.
Full textAbstract:
Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.
15
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.
Full textAbstract:
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.
16
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.
Full textAbstract:
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.
17
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.
Full textAbstract:
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.
18
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.
Full textAbstract:
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.
19
Wang, Xi Yun, Pan Feng Huang, and Ying Pings Fan. "An Image Edge Detection Method Based on Improved Ant Colony Algorithm." Advanced Engineering Forum 1 (September 2011): 236–40. http://dx.doi.org/10.4028/www.scientific.net/aef.1.236.
Full textAbstract:
This paper raises an improved ant colony algorithm, for the detection of weak edge of complex background image, considering edge positioning accuracy, edge pixels, edge continuity and interference edges. This algorithm is improved in two aspects: first, we improved the expression of pheromone; second, we improved the calculation of Heuristic information. Compared with traditional Canny detector indicates, the improved method is proved to be accurate in edge detection, good continuity and less interference by experiment.
20
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.
Full textAbstract:
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
21
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.
Full textAbstract:
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.
22
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.
Full textAbstract:
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.
23
Yu, Kun Lin, and Zhi Yu Xie. "Polynomial Interpolation Sub-Pixel Edge Detection Method Based on Improved Canny Operator." Applied Mechanics and Materials 563 (May 2014): 203–7. http://dx.doi.org/10.4028/www.scientific.net/amm.563.203.
Full textAbstract:
According to the shortcoming of traditional Canny edge detection algorithm is sensitive to noise and low positioning accuracy, this paper proposes an algorithm of Polynomial interpolation Sub-pixel edge detection based on improved Canny operator: We first use improved Canny operator edge detection algorithm to extract rough image edge, then use the quadratic Polynomial interpolation to calculate on the rough extraction edge, finally refine the edge image. Experiments show that the improved method is better than the traditional detection method can accurately locate the image edge.
24
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.
Full textAbstract:
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.
25
Damanik, Romanus. "IMAGE DETECTION EDGE IMAGE USING CANNY EDGE ALGORITHM." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 1, no. 3 (2022): 248–55. http://dx.doi.org/10.59934/jaiea.v1i3.109.
Full textAbstract:
In general, edge detection is widely used in image processing to find the boundaries of objects in an image. In this study, a technique for detecting edges that is commonly used will be studied, namely the Canny method under different conditions using Visual Studio software. In the testing process that the author carried out, it was concluded that sooner or later a process of converting an object would be, that the following factors affect the conversion process, including; type of image, image resolution, image format, specifications of the camera used to take pictures as well as the specifications of a computer that has a processing speed that is expected to be able to process images (images). The use of the Canny method to detect the edges of an image is considered more accurate and effective because this method is able to provide detailed output by including a calculated execution time
26
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.
Full textAbstract:
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.
27
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.
Full textAbstract:
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.
28
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.
Full textAbstract:
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.
29
Liang, Lei, Junming Chen, Jiawei Shi, Kai Zhang, and Xiaodong Zheng. "Noise-Robust image edge detection based on multi-scale automatic anisotropic morphological Gaussian Kernels." PLOS One 20, no. 5 (2025): e0319852. https://doi.org/10.1371/journal.pone.0319852.
Full textAbstract:
This paper presents a novel multi-scale, noise-robust edge detection method that employs multi-scale automatic anisotropic morphological Gaussian kernels to extract edge maps from input images. It addresses the issue of cross-edge detection failure in the Canny edge detector. Compared to other edge detection methods, the proposed approach offers significant advantages in maintaining noise robustness while achieving high edge resolution and accuracy. The paper is structured into five key sections. First, we propose a multi-scale automatic anisotropic morphological directional derivative (AMDD) to capture local gray-level variations around each pixel at multiple scales. Second, a new fused edge strength map (ESM) is introduced based on the multi-scale AMDD. Third, we analyze why the Canny isotropic Gaussian kernel detector fails to detect cross edges. Additionally, the edge contour is extracted by incorporating the fused ESMs and the edge direction map (EDM), which are processed through spatial and directional matching filters, into the standard Canny detection framework. Finally, we evaluate the proposed method using precision-recall (PR) curves and Pratt’s Figure of Merit (FOM). We compare its performance with existing state-of-the-art detectors on a standard dataset. Experimental results demonstrate that the proposed method effectively reduces noise, mitigates irrelevant signal interference, and smooths the image, showing competitive performance in edge detection tasks.
30
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.
Full textAbstract:
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.
31
Liang, Yan Bing, Xiao Li Meng, and Shu Jiang An. "Canny Edge Detection Method and its Application." Applied Mechanics and Materials 50-51 (February 2011): 483–87. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.483.
Full textAbstract:
Digital Cameras positioning has a wide range of application in the aspect of traffic monitoring (e-police).In this paper, the author builds and solves the mathematical model of positioning of monocular by edge detection methods and physical principles of optical imaging of Gauss, and offers a distortion error algorithm to test models, and finally sets up to solve the problem of relative position of multi-cameras. The introduction of distortion error algorithm, could be used to quantitatively examine the models in the first two steps. In accordance with the image situation of multi-image planes, the relative position between the cameras could be determined. This model of camera generates Multi-Vision Inspecting Technique of general distribution of the relative position. Relative position can be figured out if only the parameters of the pictures to be determined are available to determine the inner and outer parameters of the camera.
32
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.
Full textAbstract:
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.
33
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.
Full textAbstract:
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.
34
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.
Full textAbstract:
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.
35
Mohamed, Asharudeen J., and P. Menon Hema. "Edge Detection Using Histogram Localization." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (2018): 341–55. https://doi.org/10.11591/ijeecs.v11.i1.pp341-355.
Full textAbstract:
Detection of edges under noisy environments has been gaining lot of prominence in the recent past in most of the image and video processing applications. In this work a novel approach based on the distribution of intensity values and their corresponding positions has been proposed for distinguishing the edge pixels from the grey scale images. Separate histogram has been maintained for X and Y coordinates. The first order derivative is applied over these histograms to distinguish the edge pixels. The pixel with gradient distribution below a specific threshold value is selected as an edge pixel. This method is found to work well in case of both noiseless and noisy images. Hence this method is able to perceive the underlying information in case of noisy images. The proposed algorithm can be used for both low and high resolution images. However, the performance of the algorithm is more evident in high resolution image. A general analysis of the proposed method has been conducted for arbitrary images. The major application of the proposed work can be used for the applications that doesn’t need any preprocessing or to avoid any loss of information like in medical image analysis as it contemplate towards every intensity bin to trace the edges present in the histogram of the image rather than the overall image concerning for direct edge tracing. The results have been compared with canny algorithm which is most commonly used for edge detection.
36
Shylashree, N., M. Anil Naik, A. S. Mamatha, and V. Sridhar. "Design and Implementation of Image Edge Detection Algorithm on FPGA." International Journal of Circuits, Systems and Signal Processing 16 (January 15, 2022): 628–36. http://dx.doi.org/10.46300/9106.2022.16.78.
Full textAbstract:
Image processing is an important task in data processing systems for applications such as medical sectors, remote sensing, and microscopy tomography. Edge recognition is a sort of image division method that is used to simplify the image records so as to reduce the amount of data to be processed. Edges are considered the most important in image processing because they are used to characterize the boundaries of an image. The performance of the Canny edge recognition algorithm remarkably surpasses the present edge recognition technology in various computer visualization methods. The main drawback of using Canny edge boundary is that it consumes lot of period due to its complex computation. In order to tackle this problem a hybrid edge recognition method is proposed in block stage to locate edges with no loss. It employs the Sobel operator estimate method to calculate the value and direction of the gradient by substituting complex processes by hardware cost savings, traditional non-maximum suppression adaptive thresholding block organization, and conventional hysteresis thresholding. Pipeline was presented to lessen latency. The planned strategy is simulated using Xilinx ISE Design Suite14.2 running on a Xilinx Spartan-6 FPGA board. The synthesized architecture uses less hardware to detect edges and operates at maximum frequency of 935 MHz.
37
Revathy, N. P., S. Janarthanam, and S. Sukumaran. "Boosted Edge Detection Algorithm for Unstructured Environment in Document Using Optimized Text Region Detection." Asian Journal of Computer Science and Technology 8, S1 (2019): 50–53. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1959.
Full textAbstract:
Document images are more popular in today’s world and being made available over the internet for Information retrieval. The document images becomes a difficult task compared with digital texts and edge detection is an important task in the document image retrieval, edge detection indicates to the process of finding sharp discontinuation of characters in the document images. The single edge detection methods causing the weak gradient and edge missing problems adopts the method of combining global with local edge detection to extract edge. The global edge detection obtains the whole edges and uses to improve adaptive smooth filter algorithm based on canny operator. These combinations increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time document retrieval system to detect the edges in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the objects. This proposed work enhancing the searching ability of the document to move towards the optimal solution and to verify the capability in terms of detection efficiency.
38
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.
Full textAbstract:
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.
39
Lang, Bai He, Ling Yun Shen, Tai Lin Han, and Yu Qun Chen. "An Adaptive Edge Detection Method Based on Canny Operator." Advanced Materials Research 255-260 (May 2011): 2037–41. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2037.
Full textAbstract:
This paper proposes an adaptive Canny operator edge detection algorithm. The proposed method can automatically set the threshold value according to the different image gray-scale gradient histogram adaptively and improve the performance in the detail edge detection and good localization. Experiments show that this method produces better edge detection results performance than the Otsu method. Besides our method, Roberts operator, Prewitt operator, Sobel operator, Log operator and Canny operator based on Otsu algorithm are also tested for comparisons.
40
BHUIYAN, SHARIF M. A., JESMIN F. KHAN, and REZA R. ADHAMI. "A NOVEL APPROACH OF EDGE DETECTION VIA A FAST AND ADAPTIVE BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION METHOD." Advances in Adaptive Data Analysis 02, no. 02 (2010): 171–92. http://dx.doi.org/10.1142/s1793536910000446.
Full textAbstract:
A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of surface interpolation, which enables fast decomposition and well-characterized BIMFs. Binarization and morphological operations are applied to the first BIMF obtained from FABEMD to achieve the desired edges. The proposed approach is compared with several other edge detection methodologies, which include a combination of classical BEMD and morphological processing, the Canny and Sobel edge detectors, as well as combinations of BEMD/FABEMD and Canny/Sobel edge detectors. Simulation results with real images demonstrate the efficacy and potential of the proposed edge detection algorithm employing FABEMD.
41
Surmayanti, Surmayanti, and Sumijan Sumijan. "COMPARATIVE ANALYSIS OF SOBEL AND CANNY METHOD IN BATIK KAWUNG IMAGE." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 10, no. 3 (2024): 435–42. http://dx.doi.org/10.33330/jurteksi.v10i3.3066.
Full textAbstract:
Abstract: Abstract: this study evaluates and compares the performance of two edge detection methods Sobel method and Canny method on batik image. Batik images have unique characteristics and complex patterns, making it difficult to analyze the edges. This study presents a comparison of the results using sobel and canny edge detection methods on batik kawung images both from peak signal-to-noise reatio and from mean squared error. The results showed that canny edge detection was better than sobel method. This can be seen from the results of PSNR and MSE that is 100%. This analysis is determined by considering factors such as the accuracy of edge detection, sensitivity to noise, and the ability to handle the complexity of batik drawing patterns. The results of this study provide a detailed description of the advantages and disadvantages of each method in the image of batik kawung. The conclusions that can be drawn from this study can provide valuable guidance for choosing the optimal edge detection method in image analysis of batik kawung and others. Keywords: batik kawung; canny; MSE; PSNR; sobel Abstrak: Penelitian ini mengevaluasi dan membandingkan kinerja dua metode deteksi tepi metode Sobel dan metode Canny pada citra batik. Gambar batik mempunyai ciri-ciri yang unik dan pola yang kompleks, sehingga menyulitkan analisis tepian. Penelitian ini menyajikan perbandingan hasil menggunakan metode deteksi tepi sobel dan canny pada citra batik kawung baik dari peak signal-to-noise reatio maupun dari mean squared error. Hasil penelitian ini menunjukkan bahwa deteksi tepi canny lebih baik dibandingkan dari metode sobel. Hal ini dapat dilihat dari hasil PSNR dan MSE yang dihasilkan yaitu 100%. Analisis ini ditentukan dengan mempertimbangkan faktor-faktor seperti keakuratan deteksi tepi, kepekaan terhadap noise, dan kemampuan menangani kompleksitas pola gambar batik. Hasil penelitian ini memberikan gambaran secara detail mengenai kelebihan dan kekurangan masing-masing metode pada citra batik kawung. Kesimpulan yang dapat diambil dari penelitian ini dapat memberikan panduan berharga untuk memilih metode deteksi tepi yang optimal dalam analisis citra batik kawung dan yang lainnya. Kata kunci: batik kawung; canny; MSE; PSNR; sobel
42
Li, Bei Zhi, Hua Jiang Chen, and Jian Guo Yang. "An Adaptive Edge Detection Method for Image Polluted by Hybrid Noise in Image Measurement." Advanced Materials Research 214 (February 2011): 156–62. http://dx.doi.org/10.4028/www.scientific.net/amr.214.156.
Full textAbstract:
Edge detection directly affects the accuracy of image measurement. In this paper, focusing on the edge detection of the image of mechanical part polluted by hybrid noise consisting of Gaussian noise and impulse noise, an adaptive edge detection method is proposed. The proposed method combines a new hybrid filter smoothing noise adaptively with Canny operator to avoid the conflict of Canny operator between noise removing and edge locating, and uses Otsu threshold selection method to determine Dual-threshold of Canny operator adaptively. Using the gauge image polluted by hybrid noise as experiment object, the performance of the proposed method is evaluated qualitatively and quantitatively. Experimental results show that the proposed edge detection method has good performance.
43
owotogbe, joshua, and Tunji Ibiyemi. "Edge Detection Techniques on Digital Images - A Review." Edge Detection Techniques on Digital Images - A Review 4, no. 11 (2024): 329–32. https://doi.org/10.5281/zenodo.10853292.
Full textAbstract:
Edge is known to be an important changes of intensity in a digital image. It is a sudden changes of discontinuous noticed in an image. The three types of edges are: Horizontal, Vertical and digital images. In this paper, different edge detection methods are reviewed and comparing of different edge detection method, with their advantages and disadvantages. Implementation was done using MATLAB.
44
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.
Full textAbstract:
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.
45
Sun, Tao, and Chang Zhi Gao. "An Improved Canny Edge Detection Algorithm." Applied Mechanics and Materials 291-294 (February 2013): 2869–73. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2869.
Full textAbstract:
Traditional Canny edge detection algorithm uses a global threshold selection method, when large changes are in the background of the image and the target gray, global threshold method may lose some local edge information. For this problem, this paper therefore proposes an adaptive dynamic threshold improved Canny edge detection algorithm. The method uses image gradient variance as the criterion of the image block according to the four forks tree principle, then uses the Otsu method to get the corresponding sub-block threshold value for each sub-block, and obtains threshold value matrix by interpolation, finally, gets image edge with improved edge connected algorithm. Experimental results show that, the algorithm not only has good anti-noise performance, but also better detection accuracy.
46
You, Xiaoming, Gongxing Yan, and Zhengqiang Yang. "Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete." International Transactions on Electrical Energy Systems 2022 (October 10, 2022): 1–13. http://dx.doi.org/10.1155/2022/1837800.
Full textAbstract:
In recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which directly reflect the stress of the lining, which is very important for the study of tunnel diseases. In view of the current detection status and detection requirements, this article has carried out research work on a vehicle-mounted tunnel lining crack detection system based on image processing. Due to the grayscale difference between the cracks on the lining surface and the lining background, these differences lead to significant crack edge features and relatively stable detection. Therefore, this article designs an intelligent edge algorithm system for cracks on the lining surface to detect the edges of the image, extract the edges of cracks, and remove useless interference information in the lining background. The experiment proves that the paired sample t-test can find that after the experiment is over, the P value of different edge detection operators for global threshold segmentation is less than 0.05, which has a significant difference. The Canny, Deriche, and Lanser filters are relatively strong, and the extracted crack edge noise is relatively small. Finally, the parameter values of the crack image are calculated, and the calculated values of the crack parameters provide a scientific and reliable basis for tunnel safety evaluation.
47
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.
Full textAbstract:
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.
48
R.Vijaya, Kumar Reddy, Prudvi Raju K., Jogendra Kumar M., Ravi Kumar L., Ravi Prakash P, and Sai Kumar S. "Comparative Analysis of Common Edge Detection Algorithms using Pre-processing Technique." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2574–80. https://doi.org/10.11591/ijece.v7i1.pp2574-2580.
Full textAbstract:
Edge detection is the process of segmenting an image by detecting discontinuities in brightness. Several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image pre-processing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then edge detection technique is carried out. Finally, Standard edge detection methods can be applied to the resultant pre-processing image and its Simulation results are show that our pre-processed approach when used with a standard edge detection method enhances its performance.
49
Akbari Sekehravani, Ehsan, Eduard Babulak, and Mehdi Masoodi. "Implementing canny edge detection algorithm for noisy image." Bulletin of Electrical Engineering and Informatics 9, no. 4 (2020): 1404–10. http://dx.doi.org/10.11591/eei.v9i4.1837.
Full textAbstract:
Edge detection is a significant stage in different image processing operations like pattern recognition, feature extraction, and computer vision. Although the Canny edge detection algorithm exhibits high precision is computationally more complex contrasted to other edge detection methods. Due to the traditional Canny algorithm uses the Gaussian filter, which gives the edge detail represents blurry also its effect in filtering salt-and-pepper noise is not good. In order to resolve this problem, we utilized the median filter to maintain the details of the image and eliminate the noise. This paper presents implementing and enhance the accuracy of Canny edge detection for noisy images. Results present that this proposed method can definitely overcome noise disorders, preserve the edge useful data, and likewise enhance the edge detection precision.
50
Ehsan, Akbari Sekehravani, Babulak Eduard, and Masoodi Mehdi. "Implementing canny edge detection algorithm for noisy image." Bulletin of Electrical Engineering and Informatics 9, no. 4 (2020): 1404–10. https://doi.org/10.11591/eei.v9i4.1837.
Full textAbstract:
Edge detection is a significant stage in different image processing operations like pattern recognition, feature extraction, and computer vision. Although the Canny edge detection algorithm which exhibits high precision is computationally more complex contrasted to other edge detection methods. Due to the traditional Canny algorithm uses the Gaussian filter, which gives the edge detail represents blurry also its effect in filtering salt-and-pepper noise is not good. To resolve this problem, we utilized the median filter to maintain the details of the image and eliminate the noise. This paper presents implementing and also enhancing the accuracy of Canny edge detection for noisy images. Results present that this proposed method can definitely overcome noise disorders, preserve the edge useful data, and likewise enhance the edge detection precision.