Relevant bibliographies by topics / Canny's edge detection method

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Canny's edge detection method'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Canny's edge detection method"

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HUSSAIN, MUHAMMAD, TURGHUNJAN ABDUKIRIM, and YOSHIHIRO OKADA. "WAVELET-BASED EDGE DETECTION IN DIGITAL IMAGES." International Journal of Image and Graphics 08, no. 04 (2008): 513–33. http://dx.doi.org/10.1142/s0219467808003210.

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This paper proposes a wavelet based multilevel edge detection method that exploits spline dyadic wavelets and a frame work similar to that of Canny's edge detector.2 Using the recently proposed dyadic lifting schemes by Turghunjan et al.1 spline dyadic wavelet filters have been constructed, which are characterized by higher order of regularity and have the potential of better inherent noise filtering and detection results. Edges are determined as the local maxima in the subbands at different scales of the dyadic wavelet transform. Comparison reveals that our method performs better than Mallat's and Canny's edge detectors.
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Luh Putu Risma Noviana, I Putu Eka Indrawan, and Gde Iwan Setiawan. "ANALYSIS OF CANNY EDGE DETECTION METHOD FOR FACIAL RECOGNITION IN DIGITAL IMAGE PROCESSING." Jurnal Manajemen dan Teknologi Informasi 15, no. 2 (2024): 29–34. http://dx.doi.org/10.59819/jmti.v15i2.4107.

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The document explores the application of the Canny Edge Detection method in facial recognition systems, specifically for identifying edge patterns in digital images. In the context of technological advancements, the focus is on enhancing data processing through efficient image analysis techniques. The research addresses how different edge detection methods, including Roberts, Prewitt, Sobel, and Canny, function, with the latter being highlighted for its superior ability to minimize error and deliver accurate edge detection results. The study outlines the development of a system designed to identify optimal edge detection parameters using the Canny method, focusing on facial images captured from the front. The system is limited to edge identification in such images, and performance is measured using a correlation coefficient. The process involves several technical steps, such as pre-processing the image (grayscale conversion and noise reduction) and using Gaussian filters and hysteresis thresholding to refine the detection. The research's ultimate aim is to optimize Canny's performance for identifying edges, contributing to advancements in facial recognition technology.
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Latifa Khoirani, Rino Ariansyah, and Supiyandi Supiyandi. "Aplikasi Pengolahan Citra Untuk Peningkatan Deteksi Tepi Melalui Segmentasi Citra." Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer 2, no. 3 (2024): 196–203. http://dx.doi.org/10.61132/mars.v2i3.191.

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An important digital image processing is image segmentation, which separates objects from the background for further analysis. One segmentation technique is edge detection, which looks for boundaries between areas of different brightness. This article compares four edge detection methods: Roberts, Prewitt, Sobel, and Canny. The results show that, despite requiring more complex computations, Canny's method produces the sharpest and best connected edges; Sobel and Prewitt's method, on the other hand, is faster and simpler than Roberts' method, but is less effective in dealing with noise and often produces edges that are not connected to the plane. The choice of edge detection method depends on the application. Sobel and Prewitt are good for speed and stability, and Roberts is suitable for fast processing of images with minimal noise.
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Yu, Zhongdang, and Hamid Reza Karimi. "Edge Detector Design Based on LS-SVR." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/419792.

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For locating inaccurate problem of the discrete localization criterion proposed by Demigny, a new criterion expression of “good localization” is proposed. Firstly, a discrete expression of good detection and good localization criterion of two dimension edge detection operator is employed, and then an experiment to measure optimal parameters of two dimension Canny's edge detection operator is introduced after. Moreover, a detailed performance comparison and analysis of two dimension optimal filter obtained via utilizing tensor product for one dimension optimal filter are provided which can prove that least square support vector regression (LS-SVR) is a smoothness filter and give the construct method of the derivate operator. This paper uses LS-SVR as the object function constructor and then realizes the approximation of two dimension optimal edge detection operator. This paper proposes the utility method of using singleness operator to realize multiscale edge detection by referencing the multiscale analysis technology of the wavelets theory. Experiment shows that the method has utility and efficiency.
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MYAKININ, OLEG O., DMITRY V. KORNILIN, IVAN A. BRATCHENKO, VALERIY P. ZAKHAROV, and ALEXANDER G. KHRAMOV. "NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION." Journal of Innovative Optical Health Sciences 06, no. 02 (2013): 1350009. http://dx.doi.org/10.1142/s1793545813500090.

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In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modifications. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre- and post-processing operations is demonstrated. Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.
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Wicaksono, Damar, Diva Putra Almeyda, Irfan Mikola Muldiyanto Putra, and Letty Malihatuningrum. "Analisis Perbandingan Metode Pra Pemrosesan Citra untuk Deteksi Tepi Canny pada Citra Berbagai Kondisi Jalan menggunakan Bahasa Pemrograman Python." JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) 7, no. 1 (2024): 17–31. http://dx.doi.org/10.34012/jutikomp.v7i1.3872.

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Edge detection is one of the essential techniques in digital image processing used to identify sudden changes in pixel intensity in an object. In the context of road marking detection on highways, accurate edge detection plays a crucial role in improving motorist safety and navigation. The Canny Edge Detection method has been proven effective in detecting edges with high accuracy in digital image processing. However, applying Canny Edge Detection on road images in various conditions still requires further research. This research aims to implement the Canny Edge Detection method in road marking detection on highway images. The main stages of this research include image pre-processing, where noise is removed, and the image is converted into a grayscale image to prepare the image before edge detection using the Canny method. In addition, a comparison will be made with several other image pre-processing methods, such as median and bilateral blur, to determine the most effective method for edge detection on road markings. Based on the research results, applying the Canny Edge Detection method with pre-processing using median blur is a practical approach to road marking detection on highway images. This method can produce accurate and optimized edge detection, which can be the basis for developing automatic road marking detection systems on highways. The findings can contribute to the improvement of motorist safety and navigation as well as the development of more accurate and effective edge detection technology on road markings.
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Pahadiya, S., and R. Khatri. "Compare Modify Canny Edge Detection Method with Existing Edge Detection Methods." International Journal of Computer Sciences and Engineering 6, no. 2 (2018): 337–40. http://dx.doi.org/10.26438/ijcse/v6i2.337340.

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S, Britto Ramesh Kumar, and Bhuvaneshwari A. "AN EFFICIENT BRITWARI TECHNIQUE TO ENHANCE CANNY EDGE DETECTION ALGORITHM USING DEEP LEARNING." ICTACT Journal on Soft Computing 12, no. 3 (2022): 2634–39. http://dx.doi.org/10.21917/ijsc.2022.0377.

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Artificial Intelligence edge detection refers to a set of mathematical techniques used to recognize digital image locations. The picture brightness plays a vital role in detecting dissimilarities and making decisions. Edges are the sharp changes in pictures with respect to the brightness and are commonly categorized into a collection of curved line segments. The main focus of this paper is to find sharp corner edges and the false edges present in the MRI images. The canny edge algorithm is a popular method for detecting these types of edges. The traditional canny edge detection technique has various issues that are discussed in this paper. This study analyses the canny edge algorithm and enhances the smoothing filter, pixel identifier, and feature selection. The proposed Britwari technique, Tabu Search Heuristic Pattern Identifier (TSHPI) enhances the edge detection using SUSAN Filter. Feature Selection is performed to improvise the canny edge method. Deep Learning algorithm is used for classification of pre-trained neural networks to find a greater number of edge pixels. The implementation results show that the Britwari proposed technique (SUSAN Filter Tabu Search Heuristic Pattern Identifier Hill Climbing) reached better accuracy than the traditional Canny Edge Detection algorithms. The results produced better feature set selection using edge detection in MRI images.
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Diman, Suhardiman, Zahir Zainuddin, and Salama Manjang. "Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method." EPI International Journal of Engineering 2, no. 2 (2019): 139–44. http://dx.doi.org/10.25042/epi-ije.082019.08.

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Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV. The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.
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Pinastawa, I. Wayan Rangga, Musthofa Galih Pradana, and Khoironi Khoironi. "Edge Detection Model Performance Using Canny, Prewitt and Sobel in Face Detection." Sinkron 8, no. 2 (2024): 623–31. http://dx.doi.org/10.33395/sinkron.v8i2.13497.

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Detection of objects in the form of objects, humans and other objects at this time has been widely applied in many aspects of life. The help of this technology can facilitate human work, one of which is facial detection to get information about a person's identity. Face identification and detection is closely related to Data Mining science with Image Processing sub-science. This facial detection and recognition can use several technical approaches, one of which is to use edge detection. Edge detection is one of the basic operations of image processing. In the image classification process, edge detection is required before image segmentation processing. There are several methods that can be used to perform edge detection such as Canny, Prewitt and Sobel. These three methods are methods that have accurate and good detection results, with the advantages of each method having its own added value. From the results of previous studies that stated these three methods have good results, it became interesting to conduct a comparative study of these three methods in detecting edges in facial images. Edge detection applied to this study identifies facial images, and will get similarities with the original image from the result analysis process, and is reinforced by measurement results using the Mean Square Error error degree. The final result of this study states that this study the most optimal Mean Square Error measurement results obtained the final results in the Canny method of 10, the Prewitt method of 41 and Sobel of 29. These results show that the value of the Canny method has the smallest Mean Square Error value, which indicates that the Canny method on facial image edge detection has the most optimal results.
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Dissertations / Theses on the topic "Canny's edge detection method"

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Zhu, Yuan. "Extraction of Linear Features Based on Beamlet Transform." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1301616331.

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Beneš, Radek. "Využití metod zpracování signálů pro zvýšení bezpečnosti automobilové dopravy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218105.

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This diploma thesis deals with the issue of the recognition of road signs in the video sequence. Such systems increase the traffic safety and are implemented by major car factories in the manufactured cars (Opel, BMW). First, the motivation for the utilisation of these systems is presented, followed by the survey of the current state of the art methods. Finally, a specific road-sign detection method is chosen and described in detail. The method uses advanced techniques of signal processing. Segmentation method in color space is used for sign detection and subsequent classification is accomplished by linear classification with optional use of PCA method. In addition, the method contains the prediction of road sign positions based on Kalman filtering. Implemented system yields relatively accurate results and overall analysis and discussion is enclosed.
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Mattsson, Per, and Andreas Eriksson. "Segmentation of Carotid Arteries from 3D and 4D Ultrasound Images." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1141.

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<p>This thesis presents a 3D semi-automatic segmentation technique for extracting the lumen surface of the Carotid arteries including the bifurcation from 3D and 4D ultrasound examinations. </p><p>Ultrasound images are inherently noisy. Therefore, to aid the inspection of the acquired data an adaptive edge preserving filtering technique is used to reduce the general high noise level. The segmentation process starts with edge detection with a recursive and separable 3D Monga-Deriche-Canny operator. To reduce the computation time needed for the segmentation process, a seeded region growing technique is used to make an initial model of the artery. The final segmentation is based on the inflatable balloon model, which deforms the initial model to fit the ultrasound data. The balloon model is implemented with the finite element method. </p><p>The segmentation technique produces 3D models that are intended as pre-planning tools for surgeons. The results from a healthy person are satisfactory and the results from a patient with stenosis seem rather promising. A novel 4D model of wall motion of the Carotid vessels has also been obtained. From this model, 3D compliance measures can easily be obtained.</p>
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Dong, Weixiao. "Event Detection in the Terrain Surface." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71792.

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Event Detection is a process of identifying terrain flatness from which localized events such as potholes in the terrain surface can be found and is an important tool in pavement health monitoring and vehicle performance inspection. Repeated detection of terrain surfaces over an extended period of time can be used by highway engineers for long term road health monitoring. An accurate terrain map can allow maintenance personnel for identifying deterioration in road surface for immediate correction. Additionally, knowledge of the events in terrain surface can be used to predict the performance the vehicles would experience while traveling over it. Event detection is composed of two processes: event edging and stitching edges to events. Edge detection is a process of identifying significant localized changes in the terrain surface. Many edge detection methods have been designed capable of capturing edges in terrain surfaces. Gradient searches are frequently used in image processing to recover useful information from images. The issue with using a gradient search method is that it returns deterministic values resulting in edges which are less precise. In order to predict the precision of the terrain surface, the individual nodal probability densities must be quantified and finally combined for the precision of terrain surface. A Comparative Nodal Uncertainty Method is developed in this work to detect edges based on the probability distribution of the nodal heights within some local neighborhood. Edge stitching is developed to group edges to events in a correct sequence from which an event can be determined finally.<br>Master of Science
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Glenn, Timothy Scott 1971. "Velocity measurement of laser energy induced Rayleigh surface waves on bulk substrates employing the optical beam deflection (knife-edge detection) method." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/49947.

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Olokodana, Ibrahim Latunde. "Kriging Methods to Exploit Spatial Correlations of EEG Signals for Fast and Accurate Seizure Detection in the IoMT." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1707311/.

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Epileptic seizure presents a formidable threat to the life of its sufferers, leaving them unconscious within seconds of its onset. Having a mortality rate that is at least twice that of the general population, it is a true cause for concern which has gained ample attention from various research communities. About 800 million people in the world will have at least one seizure experience in their lifespan. Injuries sustained during a seizure crisis are one of the leading causes of death in epilepsy. These can be prevented by an early detection of seizure accompanied by a timely intervention mechanism. The research presented in this dissertation explores Kriging methods to exploit spatial correlations of electroencephalogram (EEG) Signals from the brain, for fast and accurate seizure detection in the Internet of Medical Things (IoMT) using edge computing paradigms, by modeling the brain as a three-dimensional spatial object, similar to a geographical panorama. This dissertation proposes basic, hierarchical and distributed Kriging models, with a deep neural network (DNN) wrapper in some instances. Experimental results from the models are highly promising for real-time seizure detection, with excellent performance in seizure detection latency and training time, as well as accuracy, sensitivity and specificity which compare well with other notable seizure detection research projects.
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Авраменко, Віктор Васильович, Виктор Васильевич Авраменко, Viktor Vasylovych Avramenko, Роман Сергійович Волков, Роман Сергеевич Волков та Roman Serhiiovych Volkov. "Аппроксимация контуров сплайнами в процессе их распознавания". Thesis, Сумский государственный университет, 2011. http://essuir.sumdu.edu.ua/handle/123456789/64886.

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В [1] описан метод локального распознавания контуров, который основан на использовании функции непропорциональности по производной первого порядка для функций, заданных параметрически [2,3]. Растровый формат, в котором часто представлены распознаваемые контуры, затрудняет построение параметрической зависимости в полярной системе координат из-за вносимых им погрешностей. В [1] для решения этой задачи применяется линейная аппроксимация фрагментов контурных изображений. Такой подход позволяет уменьшить погрешность, вносимую растровым форматом, а также производить более эффективный контроль пересечений контуров радиус-векторами.
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Spencer, Benjamin. "On-line C-arm intrinsic calibration by means of an accurate method of line detection using the radon transform." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAS044/document.

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Les ``C-arm'' sont des systémes de radiologie interventionnelle fréquemment utilisés en salle d'opération ou au lit du patient. Des images 3D des structures anatomiques internes peuvent être calculées à partir de multiples radiographies acquises sur un ``C-arm mobile'' et isocentrique décrivant une trajectoire généralement circulaire autour du patient. Pour cela, la géométrie conique d'acquisition de chaque radiographie doit être précisément connue. Malheureusement, les C-arm se déforment en général au cours de la trajectoire. De plus leur motorisation engendre des oscillations non reproductibles. Ils doivent donc être calibrés au cours de l'acquisition. Ma thèse concerne la calibration intrinsèque d'un C-arm à partir de la détection de la projection du collimateur de la source dans les radiographies.Nous avons développé une méthode de détection de la projection des bords linéaires du collimateur. Elle surpasse les méthodes classiques comme le filtre de Canny sur données simulées ou réelles. La précision que nous obtenons sur l'angle et la position (phi,s) des droites est de l'ordre de: phi{RMS}=+/- 0.0045 degrees et s{RMS}=+/- 1.67 pixels. Nous avons évalué nos méthodes et les avons comparés à des méthodes classiques de calibration dans le cadre de la reconstruction 3D<br>Mobile isocentric x-ray C-arm systems are an imaging tool used during a variety of interventional and image guided procedures. Three-dimensional images can be produced from multiple projection images of a patient or object as the C-arm rotates around the isocenter provided the C-arm geometry is known. Due to gravity affects and mechanical instabilities the C-arm source and detector geometry undergo significant non-ideal and possibly non reproducible deformation which requires a process of geometric calibration. This research investigates the use of the projection of the slightly closed x-ray tube collimator edges in the image field of view to provide the online intrinsic calibration of C-arm systems.A method of thick straight edge detection has been developed which outperforms the commonly used Canny filter edge detection technique in both simulation and real data investigations. This edge detection technique has exhibited excellent precision in detection of the edge angles and positions, (phi,s), in the presence of simulated C-arm deformation and image noise: phi{RMS} = +/- 0.0045 degrees and s{RMS} = +/- 1.67 pixels. Following this, the C-arm intrinsic calibration, by means of accurate edge detection, has been evaluated in the framework of 3D image reconstruction
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LI, Songyu. "A New Hands-free Face to Face Video Communication Method : Profile based frontal face video reconstruction." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-152457.

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This thesis proposes a method to reconstruct a frontal facial video basedon encoding done with the facial profile of another video sequence.The reconstructed facial video will have the similar facial expressionchanges as the changes in the profile video. First, the profiles for boththe reference video and for the test video are captured by edge detection.Then, asymmetrical principal component analysis is used to model thecorrespondence between the profile and the frontal face. This allows en-coding from a profile and decoding of the frontal face of another video.Another solution is to use dynamic time warping to match the profilesand select the best matching corresponding frontal face frame for re-construction. With this method, we can reconstructed the test frontalvideo to make it have the similar changing in facial expressions as thereference video. To improve the quality of the result video, Local Lin-ear Embedding is used to give the result video a smoother transitionbetween frames.
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Škrobák, Dalibor. "Detekce tváří v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217297.

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This thesis is focused on face detection in static picture. Theoretical part contains color spaces (RGB, HSI, YCbCr), methods for skin detection (explicit, parametric or non-parametric methods), image metric, edge detection, mathematical morphology, methods for classification faces (appearance-based methods, feature invariant approaches, knowledge-based methods, template matching methods). Practical part of this thesis contains concept and practical realization two algorithms for segmentation skin in static image (simple method based on Cr chroma components and statistical method). Practical part contains concept and practical realization two algorithms for classification face (appearance-based method and template matching method) too.
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Book chapters on the topic "Canny's edge detection method"

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Cao, Zijie, Huangchao Yu, and Xudong Liu. "Fast Road Edge Detection Method Based on Canny Operator and Dynamic Thresholds." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1103-1_19.

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Murali, Gunji Bala, V. Santosh Kumar, Dibya Narayan Behera, Kapil Kumar Mohanta, Omkar Tulankar, and Sanketh S. Salimath. "Pothole Detection on Roads Using Canny Edge Detection Algorithm." In Applications of Computational Methods in Manufacturing and Product Design. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0296-3_60.

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Cao, Su-Qun, Wei-Min Chen, and Hong Zhang. "An Integration Method for Edge Detection." In Advanced Electrical and Electronics Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19712-3_30.

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Kovalevsky, Vladimir. "A New Method of Edge Detection." In Modern Algorithms for Image Processing. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4237-7_7.

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Das, Prabal Deep, and Bhavesh D. Shah. "Detection of Road Sign Using Edge Detection Method." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0507-9_1.

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Halder, Amiya, Pritam Bhattacharya, and Aritra Kundu. "Edge Detection Method Using Richardson’s Extrapolation Formula." In Soft Computing in Data Analytics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_69.

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Zhu, Ruijia, Yiwen Liu, Yanxia Gao, Yuanquan Shi, and Xiaoning Peng. "Edge Intelligence Based Garbage Classification Detection Method." In Edge Computing and IoT: Systems, Management and Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28990-3_10.

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Che, Xiangjiu, Li Wang, and Xiaoxin Guo. "An Improved Edge Detection Method Using Adaptive Threshold." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-50544-1_12.

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Xie, Pengyi, Jiangbin Zheng, Qianru Wei, and Yuke Wang. "Automatic Threshold Selection Method for SAR Edge Detection." In Advances in Brain Inspired Cognitive Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_51.

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Wu, Yun-dong, Wen An, Qiang Zhang, and Shui-li Chen. "An Building Edge Detection Method Using Fuzzy SVM." In Quantitative Logic and Soft Computing 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15660-1_83.

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Conference papers on the topic "Canny's edge detection method"

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Xie, Yue, Hong Su, Tie Li, Gang Li, and Yali Hou. "Smoke Diffusion Feature Extraction and Concentration Prediction Method Based on Canny Edge Detection." In 2024 14th International Symposium on Antennas, Propagation and EM Theory (ISAPE). IEEE, 2024. https://doi.org/10.1109/isape62431.2024.10840975.

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Raviteja, C., N. Gayathri, and N. Thiyaneswaran. "Expression of Concern for: Analysing The Skin Wound Texture With Edge Detection Method Using Prewitt And Comparing Healing Rate With Canny Edge Detection." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10703558.

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Abdullah-Al-Nahid, Yinan Kong, and Md Nazmul Hasan. "Performance analysis of Canny's edge detection method for modified threshold algorithms." In 2015 International Conference on Electrical & Electronic Engineering (ICEEE). IEEE, 2015. http://dx.doi.org/10.1109/ceee.2015.7428227.

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Ramya, R., and P. Srinivasa Babu. "Automatic tuberculosis screening using canny Edge detection method." In 2015 2nd International Conference on Electronics and Communication Systems (ICECS). IEEE, 2015. http://dx.doi.org/10.1109/ecs.2015.7124909.

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Yu Wang, Meichen Fu, and Li Wang. "A robust farmland edge detection method combining anisotropic diffusion smoothing and a Canny edge detector." In 2015 IEEE International Conference on Progress in Informatics and Computing (PIC). IEEE, 2015. http://dx.doi.org/10.1109/pic.2015.7489857.

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Hao, Geng, Luo Min, and Hu Feng. "Improved Self-Adaptive Edge Detection Method Based on Canny." In 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2013. http://dx.doi.org/10.1109/ihmsc.2013.273.

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Liu, Yang, Lingyu Sun, Lijun Li, Yiben Zhang, Zongmiao Dai, and Zhenkai Xiong. "Image Identification of a Moving Object Based on an Improved Canny Edge Detection Algorithm." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86792.

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Abstract:
Edge detection plays an increasingly critical role in image process community, especially for moving object identification problems. For this case, the target object can be captured straightly via the edges beside which there is an obvious jump of grey value or texture. Nowadays, Canny operator has gained great popularity as it shows higher anti-noise performance and presents better detection accuracy in comparison with other edge detection operators like Robert’s, Sobel’s, Prewitt’s etc. However, the Gaussian filter associated with the classic Canny operator is sometimes too simple to decrease the all-type-noise. Additionally, in order to enhance the detection accuracy and lower the pseudo-edges detection ratio, two thresholds, high and low, are chosen artificially which have actually limited the adaptability of the algorithm. In this work, a compound filter, Gaussian-Median filter, is proposed to improve the smoothing effect. The self-adaptive multi-threshold Otsu algorithm is realized to determine the high/low threshold automatically according to the grey value statistic. Image moment method is conducted on basis of the detected moving object edges to locate the centroid and to compute the principal orientation. The experimental results based upon locating the edges of both static and moving objects proved the good robustness and the excellent accuracy of the proposed method.
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Zhengdong Xu, Kui Yuan, and Wenhao He. "An implementation method of Canny edge detection algorithm on FPGA." In 2011 International Conference on Electric Information and Control Engineering (ICEICE). IEEE, 2011. http://dx.doi.org/10.1109/iceice.2011.5778193.

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Tasneem, Tasnuva, and Zeenat Afroze. "A New Method of Improving Performance of Canny Edge Detection." In 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET). IEEE, 2019. http://dx.doi.org/10.1109/iciet48527.2019.9290676.

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Er-sen, Li, Zhu Shu-long, Zhu Bao-shan, Zhao Yong, Xia Chao-gui, and Song Li-hua. "An Adaptive Edge-Detection Method Based on the Canny Operator." In 2009 International Conference on Environmental Science and Information Application Technology, ESIAT. IEEE, 2009. http://dx.doi.org/10.1109/esiat.2009.49.

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Reports on the topic "Canny's edge detection method"

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

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Clausen, Jay, Vuong Truong, Sophia Bragdon, et al. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.
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