Relevant bibliographies by topics / Edge Detection

Academic literature on the topic 'Edge Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Edge Detection"

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Hasanaj, Enis, Albert Aveler, and William Söder. "Cooperative edge deepfake detection." Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53790.

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Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient with this approach.  The feasibility of edge training is evaluated by training machine learning models on several different types of iPhone devices. The models are trained using the YOLOv2 object detection system.  To test if the YOLOv2 object detection system is able to distinguish between real and fake human faces in images, several models are trained on a computer. Each model is trained with either different number of iterations or different subsets of data, since these metrics have been identified as important to the performance of the models. The performance of the models is evaluated by measuring the accuracy in detecting deepfakes.  Additionally, the deepfake detection models trained on a computer are ensembled using the bagging ensemble method. This is done in order to evaluate the feasibility of cooperatively training a deepfake detection model by combining several models.  Results show that the proposed solution is not feasible due to the time the training process takes on each mobile device. Additionally, each trained model is about 200 MB, and the size of the ensemble model grows linearly by each model added to the ensemble. This can cause the ensemble model to grow to several hundred gigabytes in size.
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Nes, Preben Gråberg. "Edge-Detection in Signals using the Continuous Wavelet-Transform. : Edge-Detection in Medical UltraSound Images." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9498.

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Today, UltraSound (US) images are often used in medical examination and surgery. An improvement of the quality of these US-images will lead to many advantages, which is a big motivation for research on this field. One obstacle in improving the quality of the images is the presence of noise and texture. In order to distinguish this unwanted information from the interesting objects, different techniques can be used. Characteristic features, such as the ability to find vague contours, small objects or edges of small strength, decides if the technique is suitable for analysing noisy signals. This thesis presents different techniques for finding objects in US-images by using the continuous wavelet-transform. One observation from the analysis is that for edge-detectors using the wavelet-transform at a single scale, there is a compromise between accuracy and reliability. One has to choose between detecting small objects or vague contours. At fine scales one is able to detect small objects, but not objects with a vague contour without including redundant information. At coarse scales one is able to detect vague contours without including redundant information, but one will not detect small objects. The Lipschitz-regularity and the length of a maxima-line in the time-scale plane works well to find the points where the signal changes with a long duration, but is less suitable to find small objects and to remove unwanted information. By using the value of the wavelet-transform at several scales, it is possible to find vague contours in images, small objects, and edges of small strength compared to the strength of the noise. Another important observation from the analysis is that use of the circumference of objects is appropriate in order to find the most important objects in an image. Using this information has been very useful with respect to the analysis of US-images. Medical ultra-sound images are in general of varying quality. In addition the quality of a US-image will typically change within the signal, and changes with respect to the quality of the contour of objects and the influence of noise. The technique which in general is most reliable and produces the best representations of the US-images analysed in this thesis, uses information about the amplitude of the wavelet-transform both within and across scales, in addition to information about the circumference of the objects. This combined edge-detector is reliable with respect to represent the important objects in the image, and this representation is often easily obtained by the edge-detector.

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Ciftci, Serdar. "Improving Edge Detection Using Intersection Consistency." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613846/index.pdf.

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Edge detection is an important step in computer vision since edges are utilized by the successor visual processing stages including many tasks such as motion estimation, stereopsis, shape representation and matching, etc. In this study, we test whether a local consistency measure based on image orientation (which we call Intersection Consistency - IC), which was previously shown to improve detection of junctions, can be used for improving the quality of edge detection of seven different detectors
namely, Canny, Roberts, Prewitt, Sobel, Laplacian of Gaussian (LoG), Intrinsic Dimensionality, Line Segment Detector (LSD). IC works well on images that contain prominent objects which are different in color from their surroundings. IC give good results on natural images that have especially cluttered background. On images involving human made objects, IC leads to good results as well. But, depending on the amount of clutter, the loss of true positives might be more crucial. Through our comprehensive investigation, we show that approximately 21% increase in f-score is obtained whereas some important edges are lost. We conclude from our experiments that IC is suitable for improving the quality of edge detection in some detectors such as Canny, LoG and LSD.
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Ganugapati, Seshu Srilakshmi. "Edge detection methods for speckled images." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23137.pdf.

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Stephens, David A. "Bayesian edge-detection in image processing." Thesis, University of Nottingham, 1990. http://eprints.nottingham.ac.uk/11723/.

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Problems associated with the processing and statistical analysis of image data are the subject of much current interest, and many sophisticated techniques for extracting semantic content from degraded or corrupted images have been developed. However, such techniques often require considerable computational resources, and thus are, in certain applications, inappropriate. The detection localised discontinuities, or edges, in the image can be regarded as a pre-processing operation in relation to these sophisticated techniques which, if implemented efficiently and successfully, can provide a means for an exploratory analysis that is useful in two ways. First, such an analysis can be used to obtain quantitative information relating to the underlying structures from which the various regions in the image are derived about which we would generally be a priori ignorant. Secondly, in cases where the inference problem relates to discovery of the unknown location or dimensions of a particular region or object, or where we merely wish to infer the presence or absence of structures having a particular configuration, an accurate edge-detection analysis can circumvent the need for the subsequent sophisticated analysis. Relatively little interest has been focussed on the edge-detection problem within a statistical setting. In this thesis, we formulate the edge-detection problem in a formal statistical framework, and develop a simple and easily implemented technique for the analysis of images derived from two-region single edge scenes. We extend this technique in three ways; first, to allow the analysis of more complicated scenes, secondly, by incorporating spatial considerations, and thirdly, by considering images of various qualitative nature. We also study edge reconstruction and representation given the results obtained from the exploratory analysis, and a cognitive problem relating to the detection of objects modelled by members of a class of simple convex objects. Finally, we study in detail aspects of one of the sophisticated image analysis techniques, and the important general statistical applications of the theory on which it is founded.
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Ramalho, Mário António da Silva Neves. "Edge detection using neural network arbitration." Thesis, University of Nottingham, 1996. http://eprints.nottingham.ac.uk/12883/.

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A human observer is able to recognise and describe most parts of an object by its contour, if this is properly traced and reflects the shape of the object itself. With a machine vision system this recognition task has been approached using a similar technique. This prompted the development of many diverse edge detection algorithms. The work described in this thesis is based on the visual observation that edge maps produced by different algorithms, as the image degrades. Display different properties of the original image. Our proposed objective is to try and improve the edge map through the arbitration between edge maps produced by diverse (in nature, approach and performance) edge detection algorithms. As image processing tools are repetitively applied to similar images we believe the objective can be achieved by a learning process based on sample images. It is shown that such an approach is feasible, using an artificial neural network to perform the arbitration. This is taught from sets extracted from sample images. The arbitration system is implemented upon a parallel processing platform. The performance of the system is presented through examples of diverse types of image. Comparisons with a neural network edge detector (also developed within this thesis) and conventional edge detectors show that the proposed system presents significant advantages.
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Jirwe, Marcus. "Online Anomaly Detection on the Edge." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299565.

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The society of today relies a lot on the industry and the automation of factory tasks is more prevalent than ever before. However, the machines taking on these tasks require maintenance to continue operating. This maintenance is typically given periodically and can be expensive while sometimes requiring expert knowledge. Thus it would be very beneficial if one could predict when a machine needs maintenance and only employ maintenance as necessary. One method to predict when maintenance is necessary is to collect sensor data from a machine and analyse it for anomalies. Anomalies are usually an indicator of unexpected behaviour and can therefore show when a machine needs maintenance. Due to concerns like privacy and security, it is often not allowed for the data to leave the local system. Hence it is necessary to perform this kind of anomaly detection in an online manner and in an edge environment. This environment imposes limitations on hardware and computational ability. In this thesis we consider four machine learning anomaly detection methods that can learn and detect anomalies in this kind of environment. These methods are LoOP, iForestASD, KitNet and xStream. We first evaluate the four anomaly detectors on the Skoltech Anomaly Benchmark using their suggested metrics as well as the Receiver Operating Characteristic curves. We also perform further evaluation on two data sets provided by the company Gebhardt. The experimental results are promising and indicate that the considered methods perform well at the task of anomaly detection. We finally propose some avenues for future work, such as implementing a dynamically changing anomaly threshold.
Dagens samhälle är väldigt beroende av industrin och automatiseringen av fabriksuppgifter är mer förekommande än någonsin. Dock kräver maskinerna som tar sig an dessa uppgifter underhåll för att forsätta arbeta. Detta underhåll ges typiskt periodvis och kan vara dyrt och samtidigt kräva expertkunskap. Därför skulle det vara väldigt fördelaktigt om det kunde förutsägas när en maskin behövde underhåll och endast göra detta när det är nödvändigt. En metod för att förutse när underhåll krävs är att samla in sensordata från en maskin och analysera det för att hitta anomalier. Anomalier fungerar ofta som en indikator av oväntat beteende, och kan därför visa att en maskin behöver underhåll. På grund av frågor som integritet och säkerhet är det ofta inte tillåtet att datan lämnar det lokala systemet. Därför är det nödvändigt att denna typ av anomalidetektering genomförs sekventiellt allt eftersom datan samlas in, och att detta sker på nätverkskanten. Miljön som detta sker i påtvingar begränsningar på både hårdvara och beräkningsförmåga. I denna avhandling så överväger vi fyra anomalidetektorer som med användning av maskininlärning lär sig och upptäcker anomalier i denna sorts miljö. Dessa metoder är LoOP, iForestASD, KitNet och xStream. Vi analyserar först de fyra anomalidetektorerna genom Skoltech Anomaly Benchmark där vi använder deras föreslagna mått samt ”Receiver Operating Characteristic”-kurvor. Vi genomför även vidare analys på två dataset som vi har tillhandhållit av företaget Gebhardt. De experimentella resultaten är lovande och indikerar att de övervägda metoderna presterar väl när det kommer till detektering av anomalier. Slutligen föreslår vi några idéer som kan utforskas för framtida arbete, som att implementera en tröskel för anomalidetektering som anpassar sig dynamiskt.
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Wang, Ziqing. "Fuzzy neural network for edge detection and Hopfield network for edge enhancement." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0005/MQ42458.pdf.

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Sun, Xiaofang. "Learning optimal linear filters for edge detection." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/30347.

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Edge detection is important both for its practical applications to computer vision as well as its relationship to early processing in the visual cortex. We describe experiments in which the back-propagation learning algorithm was used to learn sets of linear filters for the task of determining the orientation and location of edges to sub-pixel accuracy. A model of edge formation was used to generate novel input-output pairs for each iteration of the training process. The desired output included determining the interpolated location and orientation of the edge. The linear filters that result from this optimization process bear a close resemblance to oriented Gabor or derivative-of-Gaussian filters that have been found in primary visual cortex. In addition, the edge detection results appear to be superior to the existing standard edge detectors and may prove to be of considerable practical value in computer vision.
Science, Faculty of
Computer Science, Department of
Graduate
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Gruber, Stephen S. "Optimizing detection efficiency for transition edge sensors." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1442954.

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Books on the topic "Edge Detection"

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Vara, J. Olivas. Film edge detection. Manchester: UMIST, 1993.

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Seymarc, Eric. Edge detection for vision based sensing. Manchester: UMIST, 1994.

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Suzuki, T. Edge detection methods using neural networks. Manchester: UMIST, 1996.

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Brown, Jane M. Analysis of edge detection algorithms on DIAL. Fort Belvoir, Va: US Army Corps of Engineers, Engineer Topographic Laboratories, 1985.

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Johnson, Anne, Andrew Bremer, and Nancy Connell, eds. Cutting-Edge Scientific Capabilities for Biological Detection. Washington, D.C.: National Academies Press, 2022. http://dx.doi.org/10.17226/26553.

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Green, James A. Optimal edge detection and digital picture processing. 2nd ed. [Wichita, Kan.]: Greenwood Research, 1994.

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Yang, Yi. Colour edge detection and segmentation using vector analysis. Ottawa: National Library of Canada, 1995.

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Yang, Horng-Chang. Multiresolution neural networks for image edge detection and restoration. [s.l.]: typescript, 1994.

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Gonzalez, Claudia I., Patricia Melin, Juan R. Castro, and Oscar Castillo. Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53994-2.

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Bajesy, Ruzena. A common frame work for edge detection and region growing. Philadelphia: University of Pennsylvania, Department of Computer and Information Science, 1986.

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Book chapters on the topic "Edge Detection"

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Nestor, Adrian. "Edge Detection." In Encyclopedia of Clinical Neuropsychology, 926–27. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1360.

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Szymkowicz, Sarah M., Nicole R. Nissim, and Adam J. Woods. "Edge Detection." In Encyclopedia of Clinical Neuropsychology, 1–2. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56782-2_1360-2.

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Szymkowicz, Sarah M., Nicole R. Nissim, and Adam J. Woods. "Edge Detection." In Encyclopedia of Clinical Neuropsychology, 1268–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1360.

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Sundararajan, D. "Edge Detection." In Digital Image Processing, 257–80. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_9.

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Bräunl, Thomas, Stefan Feyrer, Wolfgang Rapf, and Michael Reinhardt. "Edge Detection." In Parallel Image Processing, 27–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04327-1_4.

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Lisowska, Agnieszka. "Edge Detection." In Geometrical Multiresolution Adaptive Transforms, 83–95. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05011-9_7.

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Kovalevsky, Vladimir. "Edge Detection." In Modern Algorithms for Image Processing, 87–99. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4237-7_6.

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Kinser, Jason M. "Edge Detection." In Image Operators, 189–98. First edition. | Boca Raton, FL: CRC Press/Taylor & Francis Group, [2019] |: CRC Press, 2018. http://dx.doi.org/10.1201/9780429451188-13.

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Elde, James H. "Edge Detection." In Computer Vision, 231–35. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_217.

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Louban, Roman. "Edge Detection." In Image Processing of Edge and Surface Defects, 9–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00683-8_2.

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Conference papers on the topic "Edge Detection"

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Peng, Chubing, M. Mansuripur, Kenichi Nagata, and Takeo Ohta. "Edge detection readout signal and cross-talk in phase-change optical data storage." In Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/ods.1998.tub.3.

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Conventionally, readout signal is obtained by differential detection in magneto-optical storage or by direct integration of the reflected light in phase-change optical storage. Mark edges are usually determined by slicing the level detection signal at the standard level, suffering from intersymbol interference when reading densely recorded short marks. Edge detection is a direct optical detection for mark edges. The readout signal is the difference signal from a split detector. Theoretically, edge detection has advantages over conventional level detection, such as high contrast and ability to identify edges of densely spaced marks. These features need to be confirmed experimentally. In magneto-optical storage [1], edge-shift of short marks using edge detection was found to be lower than that using differential level detection [2]. But in other aspects, such as signal and noise levels, edge detection was inferior to differential level detection [2, 3]. In phase-change optical storage [4], theoretical analysis indicates that edge detection has a potential superiority over conventional detection (hereafter referred to as sum detection). Experimentally, edge detection noise level has been confirmed to be lower than sum detection, especially at low and high spatial frequencies. In this work we present results for edge detection readout signal, carrier-to-noise ratio (CNR), and cross-talk characteristics in the scheme of land-groove as well as comparison with sum detection for phase-change optical storage.
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Tandra, Swathi, and Zia-ur Rahman. "Robust edge-detection algorithm for runway edge detection." In Electronic Imaging 2008, edited by Kurt S. Niel and David Fofi. SPIE, 2008. http://dx.doi.org/10.1117/12.766643.

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Deriche, M., F. Assaad, and A. H. Tewfik. "Frequency domain techniques for edge detection." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.tuu26.

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We show that the problem of detecting edges in a digital image is equivalent to the problem of estimating the wavenumber vectors of complex exponentials in the spatial frequency domain. This observation is then used to show that most of the known nonmodel based edge detection algorithms can be interpreted as variations of the per- iodogram method of spectral estimation. The variations include using data windows and smoothing of the resulting power spectral estimate. Next, the above observation is used to derive three new edge detection algorithms. The first algorithm is based on the fact that complex exponentials are the homogeneous solution of a difference equation with proper initial conditions. It derives estimates of the edge locations by performing a singular value decomposition of a Hankel matrix formed from the fast Fourier transform of the underlying image. The second and third approaches use the maximum likelihood spectral estimation method and various maximum entropy spectral estimation techniques on the fast Fourier transform of the underlying image to estimate the edge locations.
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Vincent, Olufunke, and Olusegun Folorunso. "A Descriptive Algorithm for Sobel Image Edge Detection." In InSITE 2009: Informing Science + IT Education Conference. Informing Science Institute, 2009. http://dx.doi.org/10.28945/3351.

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Image edge detection is a process of locating the edge of an image which is important in finding the approximate absolute gradient magnitude at each point I of an input grayscale image. The problem of getting an appropriate absolute gradient magnitude for edges lies in the method used. The Sobel operator performs a 2-D spatial gradient measurement on images. Transferring a 2-D pixel array into statistically uncorrelated data set enhances the removal of redundant data, as a result, reduction of the amount of data is required to represent a digital image. The Sobel edge detector uses a pair of 3 x 3 convolution masks, one estimating gradient in the x-direction and the other estimating gradient in y-direction. The Sobel detector is incredibly sensitive to noise in pictures, it effectively highlight them as edges. Hence, Sobel operator is recommended in massive data communication found in data transfer.
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Aghagolzadeh, Sabzali, and Okan K. Ersoy. "Transform edge detection." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.ww1.

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Many techniques have been proposed for edge detection involving transforms, such as the method of Shanmugam et al.1 and the gradient method of Marr and Hildreth.2 It can be easily shown that such methods are some kind of bandpass filtering. Because of the existence of different kind of edges and different amount of noise in a real image, no unique filter can be optimal. We discuss how to use some novel real fast transforms for edge detection through bandpass filtering. These are discrete cosine transform (DCT), real discrete Fourier transform (RDFT), scrambled real discrete Fourier transform (SRDFT), and discrete cosine-III transform (DC3T). These transforms also show little block effects, compared to the discrete Fourier transform. They can also be used for interpolation to increase the resolution of edge location and decrease the effect of inherent noise in the real image. For both bandpass filtering and interpolation we applied transforms blockwise to decrease computational complexity to achieve parallel implementation.
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Ngoko, Yanik, and Christophe Cerin. "An Edge Computing Platform for the Detection of Acoustic Events." In 2017 IEEE International Conference on Edge Computing (EDGE). IEEE, 2017. http://dx.doi.org/10.1109/ieee.edge.2017.44.

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Filho, Roberto Silva, Ching-Ling Huang, Bo Yu, Raju Venkataramana, Ashraf El-Messidi, Dustin Sharber, John Westerheide, and Nasr Alkadi. "Semi-Autonomous Industrial Robotic Inspection: Remote Methane Detection in Oilfield." In 2018 IEEE International Conference on Edge Computing (EDGE). IEEE, 2018. http://dx.doi.org/10.1109/edge.2018.00010.

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Alam, M. S., K. M. Iftekharuddin, and M. A. Karim. "Roberts operator based edge detection using polarization-encoded optical shadow-casting." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thqq4.

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Polarization-encoded optical shadow-casting (POSC) is found to have remarkable promise for fully utilizing the parallelism of optics in performing arithmetic and logical operations with spatial coded patterns as inputs.1 However, so far little work has been done to exploit its role in realizing 2D image processing techniques in the spatial domain. For image processing applications, it is often necessary to extract the outline features of an image. Recently, POSC schemes have been used for edge detection by using a modified difference operator, i.e., an L-shaped operator.2 However, a difference operator is not necessarily suitable for detecting edges with equal edge strength since it may produce weak response for such cases. On the other hand, because of the symmetric nature of the Roberts mask, all the edges of an image will have equal strength. In this paper, accordingly, we implement the Roberts operator based edge detection by using the POSC technique. The performance of the proposed technique is verified by computer simulation.
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Saleem, Muhammad, Imran Touqir, and Adil Masood Siddiqui. "Novel Edge Detection." In 2007 4th International Conference on Information Technology New Generations. IEEE, 2007. http://dx.doi.org/10.1109/itng.2007.137.

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Amer, Ghassan Mahmoud Husien, and Ahmed Mohamed Abushaala. "Edge detection methods." In 2015 2nd World Symposium on Web Applications and Networking (WSWAN). IEEE, 2015. http://dx.doi.org/10.1109/wswan.2015.7210349.

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Reports on the topic "Edge Detection"

1

Geiger, Davi, and Tomaso Poggio. An Optimal Scale for Edge Detection. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada202747.

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Hanshaw, R. A. Development of a Single-Axis Edge Detection System. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/751343.

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Clarke, J., and L. R. Wright. The uncertainty-aware canny operator edge detection method. National Physical Laboratory, May 2023. http://dx.doi.org/10.47120/npl.ms49.

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Hupp, William, Adarsh Hasandka, Vivek Kumar Singh, and Salam Baniahmed. Advanced Grid Operational Technology Edge-Level Threat Detection. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/1960418.

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Chan, C. W. Edge Detection to Isolate Motion in Adaptive Optics Systems. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/15004551.

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Yoo, Andy, Geoffrey Sanders, Van Henson, and Panayot Vassilevski. Enhancing Community Detection By Affinity-based Edge Weighting Scheme. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1226950.

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Richardson, Thomas J., and Sanjoy K. Mitter. Scaling Results for the Variational Approach to Edge Detection. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada459531.

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Irwin, Kent David. Phonon-mediated particle detection using superconducting tungsten transition-edge sensors. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/1423679.

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Sher, David. Optimal Likelihood Generators for Edge Detection under Gaussian Additive Noise. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada179945.

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Adair, M., and B. Guindon. Methods for evaluating speckle-suppressing filters based on edge detection performance. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1989. http://dx.doi.org/10.4095/217604.

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