Journal articles: 'Edge Detection'

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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 (April 17, 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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Poornima, B., Y. Ramadevi, and T. Sridevi. "Threshold Based Edge Detection Algorithm." International Journal of Engineering and Technology 3, no. 4 (2011): 400–403. http://dx.doi.org/10.7763/ijet.2011.v3.260.

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A. M. Saif, Jamil, Mahgoub H. Hammad, and Ibrahim A. A. Alqubati. "Gradient Based Image Edge Detection." International Journal of Engineering and Technology 8, no. 3 (March 2016): 153–56. http://dx.doi.org/10.7763/ijet.2016.v6.876.

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A. M. Saif, Jamil, Mahgoub H. Hammad, and Ibrahim A. A. Alqubati. "Gradient Based Image Edge Detection." International Journal of Engineering and Technology 8, no. 3 (March 2016): 153–56. http://dx.doi.org/10.7763/ijet.2016.v8.876.

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Mapurisa, W., and G. Sithole. "IMPROVED EDGE DETECTION FOR SATELLITE IMAGES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 185–92. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-185-2022.

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Abstract. Edges are a key feature employed in various computer vision applications namely segmentation, object recognition, feature tracking and 3D reconstruction. Edges provide key information with regards to object presence, shape, form and detail which aid in many computer vision tasks. While there are various edge detection techniques in literature, challenges in edge detection remain. Varying image contrast due to non uniform scene illumination and imaging resolution affects the edge information obtained from any given image. The edge detection results are characterised by missing edges, edge fragmentation and some false positive edges. Gradient based edge detectors are the most commonly used detectors. These detectors all suffer from aforementioned challenges. In this, paper we present an edge detection framework that aims to recover long unfragmented edges from satellite images. This is achieved by using an edge accumulator that operates on the entire edge detection parameter space. Gradient based edge detectors rely on thresholding to retrieve salient edges. This usually results in missed or noisy edges. To counter this, the accumulator is run over a wide parameter space, growing edges at each accumulator level while maintaining edge position using a localisation filter. The results are longer unbroken edges that are detected for most objects, even in shadowy regions and low contrast areas. The results show improved edge detection that preserves the form and detail of objects when compared to current gradient based detectors.

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Liu, Xinyu, and Yi-Fei Pu. "Image Edge Detection Based on Fractional-Order Ant Colony Algorithm." Fractal and Fractional 7, no. 6 (May 23, 2023): 420. http://dx.doi.org/10.3390/fractalfract7060420.

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Edge detection is a highly researched topic in the field of image processing, with numerous methods proposed by previous scholars. Among these, ant colony algorithms have emerged as a promising approach for detecting image edges. These algorithms have demonstrated high efficacy in accurately identifying edges within images. For this paper, due to the long-term memory, nonlocality, and weak singularity of fractional calculus, fractional-order ant colony algorithm combined with fractional differential mask and coefficient of variation (FACAFCV) for image edge detection is proposed. If we set the order of the fractional-order ant colony algorithm and fractional differential mask to v=0, the edge detection method we propose becomes an integer-order edge detection method. We conduct experiments on images that are corrupted by multiplicative noise, as well as on an edge detection dataset. Our experimental results demonstrate that our method is able to detect image edges, while also mitigating the impact of multiplicative noise. These results indicate that our method has the potential to be a valuable tool for edge detection in practical applications.

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Lisowska, Agnieszka. "Efficient Edge Detection Method for Focused Images." Applied Sciences 12, no. 22 (November 17, 2022): 11668. http://dx.doi.org/10.3390/app122211668.

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In many areas of image processing, we deal with focused images. Indeed, the most important object is focused and the background is smooth. Finding edges in such images is difficult, since state-of-the-art edge detection methods assume that edges should be sharp. In this way, smooth edges are not detected. Therefore, these methods can detect the main object edges that skip the background. However, we are often also interested in detecting the background as well. Therefore, in this paper, we propose an edge detection method that can efficiently detect the edges of both a focused object and a smooth background alike. The proposed method is based on the local use of the k-Means algorithm from Machine Learning (ML). The local use is introduced by the proposed enhanced image filtering. The k-Means algorithm is applied within a sliding window in such a way that, as a result of filtering, we obtain a given square image area instead of just a simple pixel like in classical filtering. The results of the proposed edge detection method were compared with the best represented methods of different approaches of edge detection like pointwise, geometrical, and ML-based ones.

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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 (February 17, 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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Karnam, Anuradha, Deepti R. Kulkarni, Kshama P. Sunagar, Nikhita G. Revankar, and Mahendra M. Dixit. "Analysis of Various Edge Detection Techniques." Bonfring International Journal of Research in Communication Engineering 6, Special Issue (November 30, 2016): 10–12. http://dx.doi.org/10.9756/bijrce.8190.

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Hiremath, Shivakumar U., Shashank P. Baannadabavi, Shreyansh Kabbin, and Shrikanth Shirakol. "Edge Detection Algorithm Using PI-Computer." Bonfring International Journal of Research in Communication Engineering 6, Special Issue (November 30, 2016): 79–82. http://dx.doi.org/10.9756/bijrce.8206.

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Wen, Xiang, Xing Li, Ce Zhang, Wenquan Han, Erzhu Li, Wei Liu, and Lianpeng Zhang. "ME-Net: A Multi-Scale Erosion Network for Crisp Building Edge Detection from Very High Resolution Remote Sensing Imagery." Remote Sensing 13, no. 19 (September 24, 2021): 3826. http://dx.doi.org/10.3390/rs13193826.

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The detection of building edges from very high resolution (VHR) remote sensing imagery is essential to various geo-related applications, including surveying and mapping, urban management, etc. Recently, the rapid development of deep convolutional neural networks (DCNNs) has achieved remarkable progress in edge detection; however, there has always been the problem of edge thickness due to the large receptive field of DCNNs. In this paper, we proposed a multi-scale erosion network (ME-Net) for building edge detection to crisp the building edge through two innovative approaches: (1) embedding an erosion module (EM) in the network to crisp the edge and (2) adding the Dice coefficient and local cross entropy of edge neighbors into the loss function to increase its sensitivity to the receptive field. In addition, a new metric, Ene, to measure the crispness of the predicted building edge was proposed. The experiment results show that ME-Net not only detects the clearest and crispest building edges, but also achieves the best OA of 98.75%, 95.00% and 95.51% on three building edge datasets, and exceeds other edge detection networks 3.17% and 0.44% at least in strict F1-score and Ene. In a word, the proposed ME-Net is an effective and practical approach for detecting crisp building edges from VHR remote sensing imagery.

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Vitulano, S., C. Di Ruberto, and M. Nappi. "Edge Detection." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 05 (August 1998): 677–93. http://dx.doi.org/10.1142/s0218001498000397.

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The paper describes a technique called ISE for image segmentation using entropy. The relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of gray levels. In order to implement the model, we have introduced a well-known technique of Problem Solving. In our model, the most important roles are played by the Evaluation Function (EF) and the Control Strategy. The EF is related to the ratio between the entropy of one region or zone of the picture and the entropy of the entire picture, while the Control Strategy determines the optimal path in the search tree (quadtree) so that the nodes in the optimal path have minimal entropy. The paper shows some comparisons between ISE and classical edge detection techniques.

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Journal, Baghdad Science. "edge detection using modification prewitt operators." Baghdad Science Journal 2, no. 3 (March 8, 2021): 484–91. http://dx.doi.org/10.21123/bsj.2005.2.3.484-491.

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in this paper we adopted ways for detecting edges locally classical prewitt operators and modification it are adopted to perform the edge detection and comparing then with sobel opreators the study shows that using a prewitt opreators

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Journal, Baghdad Science. "edge detection using modification prewitt operators." Baghdad Science Journal 2, no. 3 (September 4, 2005): 484–91. http://dx.doi.org/10.21123/bsj.2.3.484-491.

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in this paper we adopted ways for detecting edges locally classical prewitt operators and modification it are adopted to perform the edge detection and comparing then with sobel opreators the study shows that using a prewitt opreators

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Yoon, Jiho, and Chulhee Lee. "Edge Detection Using the Bhattacharyya Distance with Adjustable Block Space." Electronic Imaging 2020, no. 10 (January 26, 2020): 133–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-133.

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In this paper, we propose a new edge detection method for color images, based on the Bhattacharyya distance with adjustable block space. First, the Wiener filter was used to remove the noise as pre-processing. To calculate the Bhattacharyya distance, a pair of blocks were extracted for each pixel. To detect subtle edges, we adjusted the block space. The mean vector and covariance matrix were computed from each block. Using the mean vectors and covariance matrices, we computed the Bhattacharyya distance, which was used to detect edges. By adjusting the block space, we were able to detect weak edges, which other edge detections failed to detect. Experimental results show promising results compared to some existing edge detection methods.

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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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Hu, Guo Liang, and Xi Jiang. "Early Fire Detection of Large Space Combining Thresholding with Edge Detection Techniques." Applied Mechanics and Materials 44-47 (December 2010): 2060–64. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2060.

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Image segmentation is a crucial step of the early fire detection in large space based on image processing technology. The image edges contain abundant feature information, and the edge detection has been a main topic of image segmentation algorithm. In this paper, several kinds of traditional edge detectors have been used to detect the edge of frame target in the fire video images, and the results have been contrasted and analyzed. Considering the influence of breaks in the edge caused by noise, nonuniform illumination and spurious intensity discontinuities, proposing the method of combining thresholding with edge detection, using Otsu’s method to compute a threshold for segmentation, extracting the flame area from the background, and then using the traditional edge detectors to detect the flame edge. At the same time, the simulation results based on the MATLAB kits indicate that this kind of method has good effectiveness and strong robustness, the detected flame edges have better effect in integrality and definition, and the relevant result can be the basis of the subsequent extraction and analysis of the fire image features as well as the space positioning of the fire.

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Zhang, Heng Lei, Dhananjay Ravat, Yára R. Marangoni, and Xiang Yun Hu. "NAV-Edge: Edge detection of potential-field sources using normalized anisotropy variance." GEOPHYSICS 79, no. 3 (May 1, 2014): J43—J53. http://dx.doi.org/10.1190/geo2013-0218.1.

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Most existing edge-detection algorithms are based on the derivatives of potential-field data, and thus, enhance high wavenumber information and are sensitive to noise. The normalized anisotropy variance method (NAV-Edge) was proposed for detecting edges of potential-field anomaly sources based on the idea of normalized standard deviation (NSTD). The main improvement over the balanced, windowed normalized variance method (i.e., NSTD) used for similar purposes was the application of an anisotropic Gaussian function designed to detect directional edges and reduce sensitivity to noise. NAV-Edge did not directly use higher-order derivatives and was less sensitive to noise than the traditional methods that use derivatives in their calculation. The utility of NAV-Edge was demonstrated using synthetic potential-field data and real magnetic data. Compared with several existing methods (i.e., the curvature of horizontal gradient amplitude, tilt angle and its total-horizontal derivative, theta map, and NSTD), NAV-Edge produced superior results by locating edges closer to the true edges, resulting in better interpretive images.

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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 (February 5, 2019): 50–53. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1959.

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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.

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Zhao, Hongyang, and Miaoyi Shang. "An adaptive edge-detection method based on histogram." Modern Physics Letters B 32, no. 34n36 (December 30, 2018): 1840088. http://dx.doi.org/10.1142/s0217984918400882.

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In order to solve the problems of poor adaptability when setting threshold and the high probability of detecting pseudo-edges in the existing methods of edge detection, the paper proposes an adaptive edge-detection method based on histogram. Multi-scale wavelet transform is used to preprocess the image, the image details are highlighted obviously, and it also can avoid the effect of manual setting filter coefficients. Difference of gray values between the pixels of local area are used to calculate the gradients comprehensively, it extends the gradient direction to four directions. When calculating the gradient of edge pixel, the four directions make the expression of the gradients of edge points more perfect and avoid the edge points missing. The adaptive method is used to compute the threshold of edge-detection, the image is represented by histogram. Then use the ratio of the number of pixels in the bar and the total numbers of pixels to set the initial threshold. The regions on both sides of the initial threshold are used to calculate the high threshold and low threshold until the reasonable error between the current threshold and the previous threshold is very small iteratively. The acquired threshold makes the self-adaptability more reasonable and stronger, it also avoids the detection errors, the connection errors and the pseudo-edges which are caused by setting threshold artificially. The experimental results show that the proposed algorithm of edge detection has a good effect of preserving edge detail and filtering noise of image.

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Seo, Jeong-Joo, and Jong-Won Park. "Hepatic Vessel Segmentation using Edge Detection." Journal of the Korea Society of Computer and Information 17, no. 3 (March 30, 2012): 51–57. http://dx.doi.org/10.9708/jksci.2012.17.3.051.

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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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R, Ramnarayan, Nikita Saklani, and Vasundhara Verma. "A Review on Edge detection Technique “Canny Edge Detection”." International Journal of Computer Applications 178, no. 10 (May 15, 2019): 28–30. http://dx.doi.org/10.5120/ijca2019918828.

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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 (August 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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Chen, Bo, and Meng Jia. "A Novel Edge Detection Approach Based on Soft Morphological Operations." Applied Mechanics and Materials 220-223 (November 2012): 2828–32. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2828.

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Edge detection and target segmentation is difficult due to noise existing in an image. A novel edge detection method is proposed based on soft morphological operations in this paper. Because soft morphological operations can remove noise while preserving image details, which can be used to construct morphological edge detection operators with high robustness and better edge effect. Experimental results show that, comparing with the existing edge detection operators, the novel edge detection method can get better edge effect while removing pseudo edges.

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Pappachen James, Alex, Anusha Pachentavida, and Sherin Sugathan. "Edge detection using resistive threshold logic networks with CMOS flash memories." International Journal of Intelligent Computing and Cybernetics 7, no. 1 (March 4, 2014): 79–94. http://dx.doi.org/10.1108/ijicc-06-2013-0032.

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Purpose – The purpose of this paper is to present a new approach to edge detection using semiconductor flash memory networks having scalable and parallel hardware architecture. Design/methodology/approach – A flash cell can store multiple states by controlling its voltage threshold. The equivalent resistance of the operation states controlled by threshold voltage of flash cell gives out different combinations of logic 0 and 1 states. The paper explores this basic feature of flash memory in designing a resistance change memory network for implementing novel edge detector hardware. This approach of detecting the edges is inspired from the spatial change detection ability of the human visual system. Findings – The proposed approach consumes less number of electronic components for its implementation, and outperforms the conventional approaches of edge detection with respect to the processing speed, scalability and ease of design. It is also demonstrated to provide edges invariant to changes in the direction of the spatial change in the images. Research limitations/implications – This research brings about a new direction in the development of edge detection, in terms of developing high-speed parallel processing edge detection and imaging circuits. Practical implications – The proposed approach reduces the implementation complexity by removing the need to have convolution operations for spatial edge filtering. Originality/value – This paper presents one of the first edge detection approaches that is purely a hardware oriented design, uses resistance of flash memory to form edge detector cells, and one that does not use computational operations such as additions or multiplications for its implementation.

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Zhou, Liyu, Xianwei Huang, Qin Fu, Xuanpengfan Zou, Yanfeng Bai, and Xiquan Fu. "Fine edge detection in single-pixel imaging." Chinese Optics Letters 19, no. 12 (2021): 121101. http://dx.doi.org/10.3788/col202119.121101.

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Patel, Ishvari S., and Apurva A. Desai. "Lip Segmentation Based on Edge Detection Technique." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 39–41. http://dx.doi.org/10.15373/22778179/may2013/17.

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Nayak, Dillip Ranjan. "Edge Detection Using Fuzzy Double Gradient Morphology." Bonfring International Journal of Advances in Image Processing 04, no. 01 (December 15, 2014): 01–04. http://dx.doi.org/10.9756/bijaip.10357.

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Senthilkumaran, N., and R. Preethi. "Identification of Virus in Microscopic Image Using Genetic Algorithm." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 24–27. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2031.

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In this paper describes a several techniques of effective edge detection by using image segmentation. The image segmentation provides various techniques to detect the edges on image. The paper mainly focused on edge detection using matlab parameters and solved the many problems. Edge detection techniques have a several type of techniques. We have taken microscopic image, which affects the human body by making diseases through viruses and bacteria’s. Now analyze only about the major techniques: a.) Roberts edge detection, b) sobel edge detection, c) prewitt edge detection, d) log (laplacian of gaussian) edge detection, e) genetic edge detection and f) canny edge detection. We have applied above five techniques which are used in edge detection and got a result on microscopic images. Hence, we scope this paper defines and compares the variety of techniques and demand assures the genetic algorithm provides a better performance on edge detection using microscopic image.

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Tang, C. R., and A. Li. "An Edge Detection Algorithm Based on Edge-Preserving." Key Engineering Materials 693 (May 2016): 1321–25. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1321.

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The traditional first-order differential operator is under the influence of the Gaussian noise, therefore, it often implement boundary extraction after average filtering. But the filtering process would often smooth the details of some directions of image too much, so that the edge cannot be extracted correctly. To solve this problem, this paper puts forward the edge detection algorithm based on edges keep, to determine the keeping direction of the edge through matching different directions’ edge template. Instead of average filtering process, it can improve the performance of traditional operator, and provide the simulation results. Experimental results show that the algorithm can eliminate noise, and at the same time, keep more edge information of the image.

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Zhang, Li, Lie Jun Wang, Sen Hai Zhong, and Gang Zhao. "Contents Based Color Image Edge Detection Using Hessian Matrix." Applied Mechanics and Materials 427-429 (September 2013): 1853–60. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1853.

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The edges are the most fundamental and important characteristic of an image, edge detection is the key link and classic topic in machine vision and image processing. Considering the human visual characteristics, the intrinsic gradient direction information of color images was used to obtain the pseudo-color edges of the images by conducting multichannel edge detection. After enhancing the edge information and removing the correlation, the brightness is extracted in order to obtain complete image edge information. To make the edges more smooth and continuous, the Hessian matrix is used to remove coarse edges and edges with redundant background texture. The experiment verifies the effectiveness of the proposed algorithm, and the comparison with other scheme indicates that our scheme can improve the effectiveness, continuity and sharpness of edge detection.

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K, Bhagyashree R., and S. A. Angadi. "Crack Detection in Ceramic Tiles using Zoning and Edge Detection Methods." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2844–47. http://dx.doi.org/10.31142/ijtsrd15724.

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Dai, Junjie, Tianpeng Li, and Zhaolong Xuan. "Guided Filter-Based Edge Detection Algorithm for ICT Images of Solid Rocket Motor Propellant." Electronics 11, no. 14 (July 6, 2022): 2118. http://dx.doi.org/10.3390/electronics11142118.

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As the nondestructive testing method based on industrial computerized tomography (ICT) is widely used in solid rocket motor (SRM) propellant defect detection, the demand for a corresponding image processing algorithm is increasing. In order to extract better defect information on SRM propellants, we studied the edge detection algorithm for their ICT images. This paper proposes a guided filter-based edge detection algorithm for ICT images of SRM propellants with much noise. The algorithm innovatively uses guided filters to converge the detection results of type I edges with good edge continuity to type II edges with clear positioning. The obtained type III edges have good edge continuity and clear positioning. The experimental results show that the proposed algorithm can achieve edge detection effectively.

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Prabhakar, D., and P. K. Garg. "BUILDING EDGE DETECTION FROM VERY HIGH-RESOLUTION REMOTE SENSING IMAGERY USING DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-3-2023 (September 5, 2023): 189–96. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-3-2023-189-2023.

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Abstract. Detection of Building edges is crucial for building information extraction and description. Extracting structures from large-scale aerial images has been utilized for years in cartography. With commercially available high-resolution satellites, many aerial photography usages can now employ satellite imagery. Edge detection is focused on pinpointing distinct transitions between greyscale image regions and attributing their origins to underlying physical processes. Detecting building boundaries from very high-resolution (VHR) remote sensing data is essential for many geo-related applications, such as urban planning and management, surveying and mapping, 3D reconstruction, motion recognition, image registration, image enhancement and restoration, image compression, and more. The rapid evolution of convolutional neural networks (CNNs) has led to substantial breakthroughs in edge detection in recent years. Sharp, localized changes in brightness characterize edges in digital images. In most cases, edge detection requires some kind of image smoothing and separation. Differentiation is an ill-conditioned problem, and smoothing leads to information loss. It is challenging to create an edge detection method that works everywhere and adapts to any future processing stages. Therefore, throughout the development of digital image processing, numerous edge detectors have been created, each with its own unique set of mathematical and algorithmic properties. Several edge detectors have been developed due to application needs and the subjective nature of edge definition and characterization. We propose a deep learning technique, particularly convolutional neural networks(CNNs), that offers a promising approach to automatically learn and extract features from very high-resolution remote sensing imagery, leading to more accurate and efficient building edge detection.

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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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Xiao, Chuan Min, Tian Lei Ma, and Ren Bo Xia. "An Edge Detection Algorithm Based on Human Visual System." Advanced Materials Research 760-762 (September 2013): 1519–23. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1519.

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Edge detection is a fundamental problem in computer vision. In this paper, we present an effective algorithm to find salient edges from infrared scene images based on Human Visual System. The algorithm integrates three basic edge features: edge contrast, edge density and edge length. In this manner, the proposed algorithm works well to detect salient region boundaries and to suppress false edges from background and texture. The experimental results demonstrate the effectiveness of the proposed algorithm.

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Zhu, Zi Min, and Rong Qiang Liu. "Image Edge Detection Based on Solitary Wave Algorithm." Applied Mechanics and Materials 608-609 (October 2014): 489–93. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.489.

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Edge detection is the most basic arithmetic of the local information of image change detection, image edge detection can help people better describe or identify image, is abasic technique in image processing. Edge detection is mainly to find out the important goal of the salient points on the boundary of the image,because the traditional edge detection operators have the same direction, so easily missed in the detection process.In this paper, the solitary wave algorithm for image edge detection, this algorithm can provide both the direction and size of the image edge, a certain extent, can also solve the missing edges, it is possible to extract more accurate image information.

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Asharudeen, Mohamed, and Hema P. Menon. "Edge detection Using Histogram Localization." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (July 1, 2018): 341. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp341-355.

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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 also. 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.

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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 (September 1, 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>

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Meng, Belinda Chong Chiew, Dayang Suhaida Awang Damit, and Nor Salwa Damanhuri. "Comparative studies of multiscale edge detection using different edge detectors for MRI thigh." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 1979–86. http://dx.doi.org/10.11591/eei.v10i4.2220.

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Edge detection plays an important role in computer vision to extract object boundary. Multiscale edge detection method provides a variety of image features by different resolution at multiscale of edges. The method extracts coarse and fine structure edges simultaneously in an image. Due to this, the multiscale method enables more reliable edges are detected. Most of the multiscale methods are not translation invariant due to the decimated process. They mostly depend on the corresponding transform coefficients. These methods need more computation and a larger storage space. This study proposes a multiscale method that uses an average filter to smooth image at three different scales. Three different classical edge detectors namely Prewitt, Sobel and Laplacian were used to extract the edges from the smooth images. The edges extracted from the different scales of smooth images were then combined to form the multiscale edge detection. The performances of the multiscale images extracted from the three classical edge detectors were then compared and discussed.

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Sun, Li Hua, En Liang Zhao, Long Ma, and Li Zheng. "An Edge Detection Method Based on Improved Sobel Operator." Advanced Materials Research 971-973 (June 2014): 1529–32. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1529.

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The edge detection which comes from the classical Sobel operator in the image is based on the horizontal gradient and vertical gradient direction. On the basis of the template from two directions the method which is to detect the edges of eight directions is discussed in this paper. The image edge detection based on multiple directions can improve the accuracy of edge detection. The numerical experimental results show that the proposed method can get the smooth, continuous, multiple directions edges, and can reduce the loss of the information. The method proposed in this paper is to detect the edge of the image, which will result in higher accuracy. Especially for more complex texture image, the effect of edge detection is more obvious, and the pixel width is closer to a single pixel width. It is an effective method for edge detection.

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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 (November 23, 2016): 1655026. http://dx.doi.org/10.1142/s0218001416550260.

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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.

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Spacek, Libor A. "Edge detection and motion detection." Image and Vision Computing 4, no. 1 (February 1986): 43–56. http://dx.doi.org/10.1016/0262-8856(86)90007-7.

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Isar, Alexandru, Corina Nafornita, and Georgiana Magu. "Hyperanalytic Wavelet-Based Robust Edge Detection." Remote Sensing 13, no. 15 (July 23, 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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Shirkande, Aparna S., Sakshi S. Sawant, Neha V. Shinde, and Sharanya S. Rao. "Edge Detection Techniques Using Character Segmentation for OCR of Devanagari Text." Journal of Signal Processing 9, no. 1 (February 15, 2023): 25–32. http://dx.doi.org/10.46610/josp.2023.v09i01.003.

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An image processing technique called edge detection is used to find and identify the edges, curves, and objects in digital images. Edge detection includes various mathematical calculations that focus on locating the edges and curves where a digital image's brightness abruptly shifts or has discontinuities. In the fields of feature detection and feature extraction in image processing and computer vision, edge detection is a fundamental tool. The application of an edge detector results in the development of a set of connected curves that represent surface marks, character boundaries, and discontinuities in surface orientation. Character boundaries are described via edges. The majority of a feature is described by its edges. A group of pixels called an edge is used to describe an area where abrupt variations in intensity occur. Because it removes items of interest for actions like description, segmentation is a vital stage in an image recognition system. This paper focuses on edge detection techniques that can be implemented on characters for effective character recognition. Devanagari text includes curvy edges which need to be detected for proper recognition hence different edge detector operators are implemented to find the most effective amongst them to acquire clear edges of the text. There are several methods for segmenting images, including segmentation based on artificial neural networks, clustering, edges, regions, and thresholds. In this paper, edge-based segmentation is used. The Canny, Laplacian, Sobel, Prewitt, and Robert operators are used to detect edges, and the generated image is compared to the original binary image. The pre-processing phase includes the binarization step.

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Avcı, İsa. "Threshold Values of Different Classical Edge Detection Algorithms." Traitement du Signal 39, no. 5 (November 30, 2022): 1775–80. http://dx.doi.org/10.18280/ts.390536.

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The subject of Detecting edges in images is considered one of the main topics in digital image processing and the most common one, due to its wide applications in many fields. Classical methods for detecting edges in digital images still give excellent results if the threshold is chosen correctly. In this paper, a group of classical edge algorithms was taken and tested on different types of images, Canny edge detection algorithm gave the best results in all circumstances if the threshold value of it was set between 0.30-0.45. The range of the threshold values was from 0.1 to 0.45 in Roberts, Sobel, and Prewitt's edge detection algorithms. In this paper, four famous classical algorithms were tested on some standard RG BA images which had some noises by purpose and by holding different frequencies, containing different types of noise. Since the number of images has reached 50 thousand in total, a lot of data has been obtained and these algorithms have been tested on a large number of images. Indicating the algorithms implemented to perform on different image types, the threshold value was changed from 0 to 1 thousand times with each image by 0.001 value.

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Torre, Vincent, and Tomaso A. Poggio. "On Edge Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, no. 2 (March 1986): 147–63. http://dx.doi.org/10.1109/tpami.1986.4767769.

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Pellegrino, F. A., W. Vanzella, and V. Torre. "Edge Detection Revisited." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 3 (June 2004): 1500–1518. http://dx.doi.org/10.1109/tsmcb.2004.824147.

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Lee, J., R. Haralick, and L. Shapiro. "Morphologic edge detection." IEEE Journal on Robotics and Automation 3, no. 2 (April 1987): 142–56. http://dx.doi.org/10.1109/jra.1987.1087088.

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Journal articles on the topic 'Edge Detection'

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