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Academic literature on the topic 'EDGE DETECTION MODELS'

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Published: 11 September 2023

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Journal articles on the topic "EDGE DETECTION MODELS"

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Eom, K. B., and R. L. Kashyap. "Composite edge detection with random field models." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 1 (1990): 81–93. http://dx.doi.org/10.1109/21.47811.

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Luo, Shan, and Zehua Chen. "Edge detection in sparse Gaussian graphical models." Computational Statistics & Data Analysis 70 (February 2014): 138–52. http://dx.doi.org/10.1016/j.csda.2013.09.002.

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Yang, Chang Niu, and Xing Bo Sun. "Research on Jumper and Connector Detection of Silk Products." Applied Mechanics and Materials 716-717 (December 2014): 851–53. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.851.

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An improved morphological edge detection algorithm for silk products jumpers and connectors’ test was proposed. With structure elements of different models, we detect the edge information in different directions of silk products respectively; using the proposed adaptive fusion method based on histogram matching, we can obtain ideal image edge, while enhance the blurred edges, and effectively eliminate the silk products inherent texture and noise, then detect the clear jumpers and connectors.
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Gong, Rong Fen, and Mao Xiang Chu. "An Edge Detection Method Based on Adaptive Differential Operator." Applied Mechanics and Materials 713-715 (January 2015): 415–19. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.415.

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An edge detection method based on adaptive differential operator is proposed in this paper. Firstly, standard local edge models are built. And these edge models are described with four-bit-binary code (FBBC) which is obtained from weighted mean values in four directions. Then, based on weighted gray values in four directions, different differential operator templates are defined. And FBBC is used to build the matching between differential operator templates and edge models. Experiments show that this edge detection method with adaptive differential operator can smooth noise and has satisfactory edge detection result.
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Daoud, Mohammad I., Aamer Al-Ali, Rami Alazrai, Mahasen S. Al-Najar, Baha A. Alsaify, Mostafa Z. Ali, and Sahel Alouneh. "An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images." Sensors 22, no. 18 (September 6, 2022): 6721. http://dx.doi.org/10.3390/s22186721.

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Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, but the ROI generated by one model might be better than the ROIs generated by other models. In this study, a new method, called the edge-based selection method, is proposed to analyze the ROIs generated by different deep learning object-detection models with the goal of selecting the ROI that improves the localization of the tumor region. The proposed method employs edge maps computed for BUS images using the recently introduced Dense Extreme Inception Network (DexiNed) deep learning edge-detection model. To the best of our knowledge, our study is the first study that has employed a deep learning edge-detection model to detect the tumor edges in BUS images. The proposed edge-based selection method is applied to analyze the ROIs generated by four deep learning object-detection models. The performance of the proposed edge-based selection method and the four deep learning object-detection models is evaluated using two BUS image datasets. The first dataset, which is used to perform cross-validation evaluation analysis, is a private dataset that includes 380 BUS images. The second dataset, which is used to perform generalization evaluation analysis, is a public dataset that includes 630 BUS images. For both the cross-validation evaluation analysis and the generalization evaluation analysis, the proposed method obtained the overall ROI detection rate, mean precision, mean recall, and mean F1-score values of 98%, 0.91, 0.90, and 0.90, respectively. Moreover, the results show that the proposed edge-based selection method outperformed the four deep learning object-detection models as well as three baseline-combining methods that can be used to combine the ROIs generated by the four deep learning object-detection models. These findings suggest the potential of employing our proposed method to analyze the ROIs generated using different deep learning object-detection models to select the ROI that improves the localization of the tumor region.
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Ledalla, Sukanya, Vijendar Reddy Gurram, Gopala Krishna P, Saiteja Vodnala, Maroof Md, and Raviteja Reddy Annapuredddy. "Density based smart traffic control system using canny edge detection algorithm along with object detection." E3S Web of Conferences 391 (2023): 01061. http://dx.doi.org/10.1051/e3sconf/202339101061.

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It is urgently necessary to combine current advancements to work on the cutting edge inrush hour jam the executives, as urban congestion is one of the world’s biggest concerns. Existing methodologies, for example, traffic police and traffic lights are neither fulfilling nor viable. Consequently, a traffic management system that utilizes sophisticated edge detection and digital image processing to measure vehicle density in real time is developed in this setting. Computerizedimage processing should be used to detect edges. To extract significant traffic data from CCTV images, the edge recognition method is required. The astute edge finder outperforms other processes in terms of accuracy, entropy, PSNR (peak signal to noise ratio), MSE (mean square error), and execution time. There are a number of possible edge recognition calculations. In terms of reaction time, vehicle the board, mechanization, dependability, and overall productivity, this framework performs significantly better than previous models. Utilizing a few model images of various traffic scenarios, appropriate schematics are also provided for a comprehensive approach that includes image collection, edge distinguishing evidence, and green sign classification. Also recommended is a system with object identification and priority for ambulances stuck in traffic.
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De Borba, Anderson A., Arnab Muhuri, Mauricio Marengoni, and Alejandro C. Frery. "Feature Selection for Edge Detection in PolSAR Images." Remote Sensing 15, no. 9 (May 8, 2023): 2479. http://dx.doi.org/10.3390/rs15092479.

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Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects is relevant for image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm is a good choice for finding evidence of edges. It finds the point at which a function of the difference of properties is maximized. This algorithm is very general and accepts many types of objective functions. We use an objective function built with likelihoods. Imaging with active microwave sensors has a revolutionary role in remote sensing. This technology has the potential to provide high-resolution images regardless of the Sun’s illumination and almost independently of the atmospheric conditions. Images from PolSAR sensors are sensitive to the target’s dielectric properties and structures in several polarization states of the electromagnetic waves. Edge detection in polarimetric synthetic-aperture radar (PolSAR) imagery is challenging because of the low signal-to-noise ratio and the data format (complex matrices). There are several known marginal models stemming from the complex Wishart model for the full complex format. Each of these models renders a different likelihood. This work generalizes previous studies by incorporating the ratio of intensities as evidence for edge detection. We discuss solutions for the often challenging problem of parameter estimation. We propose a technique which rejects edge estimates built with thin evidence. Using this idea of discarding potentially irrelevant evidence, we propose a technique for fusing edge pieces of evidence from different channels that only incorporate those likely to contribute positively. We use this approach for both edge and change detection in single- and multilook images from three different sensors.
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Pitas, I. "Markovian image models for image labeling and edge detection." Signal Processing 15, no. 4 (December 1988): 365–74. http://dx.doi.org/10.1016/0165-1684(88)90057-6.

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Naraghi, Mahdi Ghasemi. "Satellite images edge detection based on morphology models fusion." Indian Journal of Science and Technology 5, no. 7 (July 20, 2012): 1–4. http://dx.doi.org/10.17485/ijst/2012/v5i7.5.

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Ahmed, Awa, and Osman Sharif. "Image Processing Techniques-based fire detection." Sulaimani Journal for Engineering Sciences 8, no. 1 (August 1, 2021): 23–34. http://dx.doi.org/10.17656/sjes.10145.

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In this paper different fire detection systems and techniques has been reviewed, many techniques have been developed for the purpose of early fire detection in different scenarios. The most accurate technique used among all these methods is Image Processing based Techniques. Different color models like RGB, HSI, CIE L*a*b and YCbCr have been used along with different edge detection algorithms like Sobel and Novel edge detection, finally the color segmentation technique was discussed in the review paper. All the mentioned methods in these papers have significantly proved to detect fire and flame edges in digital images with a timely manner, which has a huge impact on saving life and reducing loss of life.
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Dissertations / Theses on the topic "EDGE DETECTION MODELS"

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Parekh, Siddharth Avinash. "A comparison of image processing algorithms for edge detection, corner detection and thinning." University of Western Australia. Centre for Intelligent Information Processing Systems, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0073.

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Image processing plays a key role in vision systems. Its function is to extract and enhance pertinent information from raw data. In robotics, processing of real-time data is constrained by limited resources. Thus, it is important to understand and analyse image processing algorithms for accuracy, speed, and quality. The theme of this thesis is an implementation and comparative study of algorithms related to various image processing techniques like edge detection, corner detection and thinning. A re-interpretation of a standard technique, non-maxima suppression for corner detectors was attempted. In addition, a thinning filter, Hall-Guo, was modified to achieve better results. Generally, real time data is corrupted with noise. This thesis also incorporates few smoothing filters that help in noise reduction. Apart from comparing and analysing algorithms for these techniques, an attempt was made to implement correlation-based optic flow
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Rathnayaka, Mudiyanselage Kanchana. "3D reconstruction of long bones utilising magnetic resonance imaging (MRI)." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/49779/1/Kanchana_Rathnayaka_Mudiyanselage_Thesis.pdf.

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The design of pre-contoured fracture fixation implants (plates and nails) that correctly fit the anatomy of a patient utilises 3D models of long bones with accurate geometric representation. 3D data is usually available from computed tomography (CT) scans of human cadavers that generally represent the above 60 year old age group. Thus, despite the fact that half of the seriously injured population comes from the 30 year age group and below, virtually no data exists from these younger age groups to inform the design of implants that optimally fit patients from these groups. Hence, relevant bone data from these age groups is required. The current gold standard for acquiring such data–CT–involves ionising radiation and cannot be used to scan healthy human volunteers. Magnetic resonance imaging (MRI) has been shown to be a potential alternative in the previous studies conducted using small bones (tarsal bones) and parts of the long bones. However, in order to use MRI effectively for 3D reconstruction of human long bones, further validations using long bones and appropriate reference standards are required. Accurate reconstruction of 3D models from CT or MRI data sets requires an accurate image segmentation method. Currently available sophisticated segmentation methods involve complex programming and mathematics that researchers are not trained to perform. Therefore, an accurate but relatively simple segmentation method is required for segmentation of CT and MRI data. Furthermore, some of the limitations of 1.5T MRI such as very long scanning times and poor contrast in articular regions can potentially be reduced by using higher field 3T MRI imaging. However, a quantification of the signal to noise ratio (SNR) gain at the bone - soft tissue interface should be performed; this is not reported in the literature. As MRI scanning of long bones has very long scanning times, the acquired images are more prone to motion artefacts due to random movements of the subject‟s limbs. One of the artefacts observed is the step artefact that is believed to occur from the random movements of the volunteer during a scan. This needs to be corrected before the models can be used for implant design. As the first aim, this study investigated two segmentation methods: intensity thresholding and Canny edge detection as accurate but simple segmentation methods for segmentation of MRI and CT data. The second aim was to investigate the usability of MRI as a radiation free imaging alternative to CT for reconstruction of 3D models of long bones. The third aim was to use 3T MRI to improve the poor contrast in articular regions and long scanning times of current MRI. The fourth and final aim was to minimise the step artefact using 3D modelling techniques. The segmentation methods were investigated using CT scans of five ovine femora. The single level thresholding was performed using a visually selected threshold level to segment the complete femur. For multilevel thresholding, multiple threshold levels calculated from the threshold selection method were used for the proximal, diaphyseal and distal regions of the femur. Canny edge detection was used by delineating the outer and inner contour of 2D images and then combining them to generate the 3D model. Models generated from these methods were compared to the reference standard generated using the mechanical contact scans of the denuded bone. The second aim was achieved using CT and MRI scans of five ovine femora and segmenting them using the multilevel threshold method. A surface geometric comparison was conducted between CT based, MRI based and reference models. To quantitatively compare the 1.5T images to the 3T MRI images, the right lower limbs of five healthy volunteers were scanned using scanners from the same manufacturer. The images obtained using the identical protocols were compared by means of SNR and contrast to noise ratio (CNR) of muscle, bone marrow and bone. In order to correct the step artefact in the final 3D models, the step was simulated in five ovine femora scanned with a 3T MRI scanner. The step was corrected using the iterative closest point (ICP) algorithm based aligning method. The present study demonstrated that the multi-threshold approach in combination with the threshold selection method can generate 3D models from long bones with an average deviation of 0.18 mm. The same was 0.24 mm of the single threshold method. There was a significant statistical difference between the accuracy of models generated by the two methods. In comparison, the Canny edge detection method generated average deviation of 0.20 mm. MRI based models exhibited 0.23 mm average deviation in comparison to the 0.18 mm average deviation of CT based models. The differences were not statistically significant. 3T MRI improved the contrast in the bone–muscle interfaces of most anatomical regions of femora and tibiae, potentially improving the inaccuracies conferred by poor contrast of the articular regions. Using the robust ICP algorithm to align the 3D surfaces, the step artefact that occurred by the volunteer moving the leg was corrected, generating errors of 0.32 ± 0.02 mm when compared with the reference standard. The study concludes that magnetic resonance imaging, together with simple multilevel thresholding segmentation, is able to produce 3D models of long bones with accurate geometric representations. The method is, therefore, a potential alternative to the current gold standard CT imaging.
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Ramesh, Visvanathan. "Model for precise detection of bone edges." Thesis, Virginia Tech, 1987. http://hdl.handle.net/10919/40957.

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A mathematical model which is used to detect bone edges accurately is described in this thesis. This model is derived by assuming the X-ray source to be a square region. It is shown that for an ideal X-ray source (point source), the bone edge lies exactly at the location of maximum first derivative of the imaged objectâ s transmission function. However, for the non-ideal case, it is shown that the bone edge does not lie at the maximum first derivative location. Also, it is shown that an offset can be calculated from the edge parameters. The Marr- Hildreth edge detector is used to detect the initial estimates for edge location. Precise estimates are obtained by using the facet model. The offset is then cal- V culated and applied to these estimates.


Master of Science
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Bilen, Burak. "Model Based Building Extraction From High Resolution Aerial Images." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12604984/index.pdf.

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A method for detecting the buildings from high resolution aerial images is proposed. The aim is to extract the buildings from high resolution aerial images using the Hough transform and the model based perceptual grouping techniques.The edges detected from the image are the basic structures used in the building detection procedure. The method proposed in this thesis makes use of the basic image processing techniques. Noise removal and image sharpening techniques are used to enhance the input image. Then, the edges are extracted from the image using the Canny edge detection algorithm. The edges obtained are composed of discrete points. These discrete points are vectorized in order to generate straight line segments. This is performed with the use of the Hough transform and the perceptual grouping techniques. The straight line segments become the basic structures of the buildings. Finally, the straight line segments are grouped based on predefined model(s) using the model based perceptual grouping technique. The groups of straight line segments are the candidates for 2D structures that may be the buildings, the shadows or other man-made objects. The proposed method was implemented with a program written in C programming language. The approach was applied to several study areas. The results achieved are encouraging. The number of the extracted buildings increase if the orientation of the buildings are nearly the same and the Canny edge detector detects most of the building edges.If the buildings have different orientations,some of the buildings may not be extracted with the proposed method. In addition to building orientation, the building size and the parameters used in the Hough transform and the perceptual grouping stages also affect the success of the proposed method.
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Mickum, George S. "Development of a dedicated hybrid K-edge densitometer for pyroprocessing safeguards measurements using Monte Carlo simulation models." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54358.

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Pyroprocessing is an electrochemical method for recovering actinides from used nuclear fuel and recycling them into fresh nuclear fuel. It is posited herein that proposed safeguards approaches on pyroprocessing for nuclear material control and accountability face several challenges due to the unproven plutonium-curium inseparability argument and the limitations of neutron counters. Thus, the Hybrid K-Edge Densitometer is currently being investigated as an assay tool for the measurement of pyroprocessing materials in order to perform effective safeguards. This work details the development of a computational model created using the Monte Carlo N-Particle code to reproduce HKED assay of samples expected from the pyroprocesses. The model incorporates detailed geometrical dimensions of the Oak Ridge National Laboratory HKED system, realistic detector pulse height spectral responses, optimum computational efficiency, and optimization capabilities. The model has been validated on experimental data representative of samples from traditional reprocessing solutions and then extended to the sample matrices and actinide concentrations of pyroprocessing. Data analysis algorithms were created in order to account for unsimulated spectral characteristics and correct inaccuracies in the simulated results. The realistic assay results obtained with the model have provided insight into the extension of the HKED technique to pyroprocessing safeguards and reduced the calibration and validation efforts in support of that design study. Application of the model has allowed for a detailed determination of the volume of the sample being actively irradiated as well as provided a basis for determining the matrix effects from the pyroprocessing salts on the HKED assay spectra.
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Pálka, Zbyněk. "Detekce automobilů v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218828.

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This thesis dissert on traffic monitoring. There are couple of different methods of background extraction and four methods vehicle detection described here. Furthermore there is one method that describes vehicle counting. All of these methods was realized in Matlab where was created graphical user interface. One whole chapter is dedicated to process of practical realization. All methods are compared by set of testing videos. These videos are resulting in statistics which diagnoses about efficiency of single one method.
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Wesolkowski, Slawomir. "Color Image Edge Detection and Segmentation: A Comparison of the Vector Angle and the Euclidean Distance Color Similarity Measures." Thesis, University of Waterloo, 1999. http://hdl.handle.net/10012/937.

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This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The second shortcoming is the non-examination of the vector angle as a distance measure that is capable of directly evaluating hue similarity without considering intensity especially in RGB. Finally, there is inadequate research on the combination of hue- and intensity-based similarity measures to improve color similarity calculations given the advantages of each color distance measure. These distance measures were used for two image understanding tasks: edge detection, and one strategy for color image segmentation, namely color clustering. Edge detection algorithms using Euclidean distance and vector angle similarity measures as well as their combinations were examined. The list of algorithms is comprised of the modified Roberts operator, the Sobel operator, the Canny operator, the vector gradient operator, and the 3x3 difference vector operator. Pratt's Figure of Merit is used for a quantitative comparison of edge detection results. Color clustering was examined using the k-means (based on the Euclidean distance) and Mixture of Principal Components (based on the vector angle) algorithms. A new quantitative image segmentation evaluation procedure is introduced to assess the performance of both algorithms. Quantitative and qualitative results on many color images (artificial, staged scenes and natural scene images) indicate good edge detection performance using a vector version of the Sobel operator on the h1h2h3 color space. The results using combined hue- and intensity-based difference measures show a slight improvement qualitatively and over using each measure independently in RGB. Quantitative and qualitative results for image segmentation on the same set of images suggest that the best image segmentation results are obtained using the Mixture of Principal Components algorithm on the RGB, XYZ and rgb color spaces. Finally, poor color clustering results in the h1h2h3 color space suggest that some assumptions in deriving a simplified version of the Dichromatic Reflectance Model might have been violated.
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Liu, Chenguang. "Low level feature detection in SAR images." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT015.

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Dans cette thèse, nous développons des détecteurs de caractéristiques de bas niveau pour les images radar à synthèse d'ouverture (SAR) afin de faciliter l'utilisation conjointe des données SAR et optiques. Les segments de droite et les bords sont des caractéristiques de bas niveau très importantes dans les images qui peuvent être utilisées pour de nombreuses applications comme l'analyse ou le stockage d'images, ainsi que la détection d'objets. Alors qu'il existe de nombreux détecteurs efficaces pour les structures bas-niveau dans les images optiques, il existe très peu de détecteurs de ce type pour les images SAR, principalement en raison du fort bruit multiplicatif. Dans cette thèse, nous développons un détecteur de segment de droite générique et un détecteur de bords efficace pour les images SAR. Le détecteur de segment de droite proposé, nommé LSDSAR, est basé sur un modèle Markovien a contrario et le principe de Helmholtz, où les segments de droite sont validés en fonction d'une mesure de significativité. Plus précisément, un segment de droite est validé si son nombre attendu d'occurrences dans une image aléatoire sous l'hypothèse du modèle Markovien a contrario est petit. Contrairement aux approches habituelles a contrario, le modèle Markovien a contrario permet un filtrage fort dans l'étape de calcul du gradient, car les dépendances entre les orientations locales des pixels voisins sont autorisées grâce à l'utilisation d'une chaîne de Markov de premier ordre. Le détecteur de segments de droite basé sur le modèle Markovian a contrario proposé LSDSAR, bénéficie de la précision et l'efficacité de la nouvelle définition du modèle de fond, car de nombreux segments de droite vraie dans les images SAR sont détectés avec un contrôle du nombre de faux détections. De plus, très peu de réglages de paramètres sont requis dans les applications pratiques de LSDSAR.Dans la deuxième partie de cette thèse, nous proposons un détecteur de bords basé sur l'apprentissage profond pour les images SAR. Les contributions du détecteur de bords proposé sont doubles: 1) sous l'hypothèse que les images optiques et les images SAR réelles peuvent être divisées en zones constantes par morceaux, nous proposons de simuler un ensemble de données SAR à l'aide d'un ensemble de données optiques; 2) Nous proposons d'appliquer un réseaux de neurones convolutionnel classique, HED, directement sur les champs de magnitude des images. Ceci permet aux images de test SAR d'avoir des statistiques semblables aux images optiques en entrée du réseau. Plus précisément, la distribution du gradient pour toutes les zones homogènes est la même et la distribution du gradient pour deux zones homogènes à travers les frontières ne dépend que du rapport de leur intensité moyenne valeurs. Le détecteur de bords proposé, GRHED permet d'améliorer significativement l'état de l'art, en particulier en présence de fort bruit (images 1-look)
In this thesis we develop low level feature detectors for Synthetic Aperture Radar (SAR) images to facilitate the joint use of SAR and optical data. Line segments and edges are very important low level features in images which can be used for many applications like image analysis, image registration and object detection. Contrarily to the availability of many efficient low level feature detectors dedicated to optical images, there are very few efficient line segment detector and edge detector for SAR images mostly because of the strong multiplicative noise. In this thesis we develop a generic line segment detector and an efficient edge detector for SAR images.The proposed line segment detector which is named as LSDSAR, is based on a Markovian a contrario model and the Helmholtz principle, where line segments are validated according to their meaningfulness. More specifically, a line segment is validated if its expected number of occurences in a random image under the hypothesis of the Markovian a contrario model is small. Contrarily to the usual a contrario approaches, the Markovian a contrario model allows strong filtering in the gradient computation step, since dependencies between local orientations of neighbouring pixels are permitted thanks to the use of a first order Markov chain. The proposed Markovian a contrario model based line segment detector LSDSAR benefit from the accuracy and efficiency of the new definition of the background model, indeed, many true line segments in SAR images are detected with a control of the number of false detections. Moreover, very little parameter tuning is required in the practical applications of LSDSAR. The second work of this thesis is that we propose a deep learning based edge detector for SAR images. The contributions of the proposed edge detector are two fold: 1) under the hypothesis that both optical images and real SAR images can be divided into piecewise constant areas, we propose to simulate a SAR dataset using optical dataset; 2) we propose to train a classical CNN (convolutional neural network) edge detector, HED, directly on the graident fields of images. This, by using an adequate method to compute the gradient, enables SAR images at test time to have statistics similar to the training set as inputs to the network. More precisely, the gradient distribution for all homogeneous areas are the same and the gradient distribution for two homogeneous areas across boundaries depends only on the ratio of their mean intensity values. The proposed method, GRHED, significantly improves the state-of-the-art, especially in very noisy cases such as 1-look images
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Oldham, Kevin M. "Table tennis event detection and classification." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19626.

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It is well understood that multiple video cameras and computer vision (CV) technology can be used in sport for match officiating, statistics and player performance analysis. A review of the literature reveals a number of existing solutions, both commercial and theoretical, within this domain. However, these solutions are expensive and often complex in their installation. The hypothesis for this research states that by considering only changes in ball motion, automatic event classification is achievable with low-cost monocular video recording devices, without the need for 3-dimensional (3D) positional ball data and representation. The focus of this research is a rigorous empirical study of low cost single consumer-grade video camera solutions applied to table tennis, confirming that monocular CV based detected ball location data contains sufficient information to enable key match-play events to be recognised and measured. In total a library of 276 event-based video sequences, using a range of recording hardware, were produced for this research. The research has four key considerations: i) an investigation into an effective recording environment with minimum configuration and calibration, ii) the selection and optimisation of a CV algorithm to detect the ball from the resulting single source video data, iii) validation of the accuracy of the 2-dimensional (2D) CV data for motion change detection, and iv) the data requirements and processing techniques necessary to automatically detect changes in ball motion and match those to match-play events. Throughout the thesis, table tennis has been chosen as the example sport for observational and experimental analysis since it offers a number of specific CV challenges due to the relatively high ball speed (in excess of 100kph) and small ball size (40mm in diameter). Furthermore, the inherent rules of table tennis show potential for a monocular based event classification vision system. As the initial stage, a proposed optimum location and configuration of the single camera is defined. Next, the selection of a CV algorithm is critical in obtaining usable ball motion data. It is shown in this research that segmentation processes vary in their ball detection capabilities and location out-puts, which ultimately affects the ability of automated event detection and decision making solutions. Therefore, a comparison of CV algorithms is necessary to establish confidence in the accuracy of the derived location of the ball. As part of the research, a CV software environment has been developed to allow robust, repeatable and direct comparisons between different CV algorithms. An event based method of evaluating the success of a CV algorithm is proposed. Comparison of CV algorithms is made against the novel Efficacy Metric Set (EMS), producing a measurable Relative Efficacy Index (REI). Within the context of this low cost, single camera ball trajectory and event investigation, experimental results provided show that the Horn-Schunck Optical Flow algorithm, with a REI of 163.5 is the most successful method when compared to a discrete selection of CV detection and extraction techniques gathered from the literature review. Furthermore, evidence based data from the REI also suggests switching to the Canny edge detector (a REI of 186.4) for segmentation of the ball when in close proximity to the net. In addition to and in support of the data generated from the CV software environment, a novel method is presented for producing simultaneous data from 3D marker based recordings, reduced to 2D and compared directly to the CV output to establish comparative time-resolved data for the ball location. It is proposed here that a continuous scale factor, based on the known dimensions of the ball, is incorporated at every frame. Using this method, comparison results show a mean accuracy of 3.01mm when applied to a selection of nineteen video sequences and events. This tolerance is within 10% of the diameter of the ball and accountable by the limits of image resolution. Further experimental results demonstrate the ability to identify a number of match-play events from a monocular image sequence using a combination of the suggested optimum algorithm and ball motion analysis methods. The results show a promising application of 2D based CV processing to match-play event classification with an overall success rate of 95.9%. The majority of failures occur when the ball, during returns and services, is partially occluded by either the player or racket, due to the inherent problem of using a monocular recording device. Finally, the thesis proposes further research and extensions for developing and implementing monocular based CV processing of motion based event analysis and classification in a wider range of applications.
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Kozina, Lubomír. "Detekce a počítání automobilů v obraze (videodetekce)." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218382.

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In this master’s thesis on the topic Videodetection - traffic monitoring I was engaged in searching moving objects in traffic images sequence. There are described various methods background model computation and moving vehicles marking, counting or velocity calculating in the thesis. It was created a graphical user interface for traffic scene evaluation in MATLAB.
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Book chapters on the topic "EDGE DETECTION MODELS"

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Zhang, Q. H., S. Gao, and Tien D. Bui. "Edge Detection Models." In Lecture Notes in Computer Science, 133–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559573_17.

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Smelyakov, Kirill, Sergiy Smelyakov, and Anastasiya Chupryna. "Adaptive Edge Detection Models and Algorithms." In Advances in Spatio-Temporal Segmentation of Visual Data, 1–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35480-0_1.

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Li, Zhe, and Yindi Wang. "Moving Vehicle Detection Combining Edge Detection and Gaussian Mixture Models." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 229–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_24.

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Azencott, R., C. Graffrigne, and C. Labourdette. "Edge Detection and Segmentation of Textured Plane Images." In Stochastic Models, Statistical Methods, and Algorithms in Image Analysis, 75–88. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2920-9_4.

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Gómez-Moreno, Hilario, Saturnino Maldonado-Bascón, and Francisco López-Ferreras. "Edge Detection in Noisy Images Using the Support Vector Machines." In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, 685–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_82.

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Contreras, Ricardo, M. Angélica Pinninghoff, and Jaime Ortega. "Using Ant Colony Optimization for Edge Detection in Gray Scale Images." In Natural and Artificial Models in Computation and Biology, 323–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38637-4_33.

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Mairal, Julien, Marius Leordeanu, Francis Bach, Martial Hebert, and Jean Ponce. "Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation." In Lecture Notes in Computer Science, 43–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88690-7_4.

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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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Beneš, Nikola, Luboš Brim, Samuel Pastva, and David Šafránek. "Symbolic Coloured SCC Decomposition." In Tools and Algorithms for the Construction and Analysis of Systems, 64–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_4.

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AbstractProblems arising in many scientific disciplines are often modelled using edge-coloured directed graphs. These can be enormous in the number of both vertices and colours. Given such a graph, the original problem frequently translates to the detection of the graph’s strongly connected components, which is challenging at this scale.We propose a new, symbolic algorithm that computes all the monochromatic strongly connected components of an edge-coloured graph. In the worst case, the algorithm performs $$O(p\cdot n\cdot \log n)$$ O ( p · n · log n ) symbolic steps, where p is the number of colours and n the number of vertices. We evaluate the algorithm using an experimental implementation based on Binary Decision Diagrams (BDDs) and large (up to $$2^{48}$$ 2 48 ) coloured graphs produced by models appearing in systems biology.
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Zhang, Qing H., Song Gao, and Tien D. Bui. "A Roof Edge Detection Model." In Pattern Recognition and Image Analysis, 319–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_40.

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Conference papers on the topic "EDGE DETECTION MODELS"

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Shen, Jun, and Serge Castan. "Edge Detection Based On Multi-Edge Models." In 1987 Symposium on the Technologies for Optoelectronics, edited by Jean Besson. SPIE, 1988. http://dx.doi.org/10.1117/12.943385.

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Hariharan, B., R. Siva, S. Sadagopan, Vaibhav Mishra, and Yash Raghav. "Malware Detection Using XGBoost based Machine Learning Models - Review." In 2023 2nd International Conference on Edge Computing and Applications (ICECAA). IEEE, 2023. http://dx.doi.org/10.1109/icecaa58104.2023.10212327.

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Sadu, Vijaya Bhaskar, T. Mahalakshmi, Nellore Manoj Kumar, Neha Singh, and K. Lakshmi Sarada. "Statistical Analysis of Big Data Models in Android Malware Detection." In 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE, 2022. http://dx.doi.org/10.1109/icecaa55415.2022.9936326.

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Sullivan, Josephine, Oscar Danielsson, and Stefan Carlsson. "Exploiting Part-Based Models and Edge Boundaries for Object Detection." In 2008 Digital Image Computing: Techniques and Applications. IEEE, 2008. http://dx.doi.org/10.1109/dicta.2008.88.

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Pei, W., and Y. Y. Zhu. "Wavelet transform-based edge detection of non-uniform illumination image." In Geoinformatics 2008 and Joint Conference on GIS and Built environment: Advanced Spatial Data Models and Analyses, edited by Lin Liu, Xia Li, Kai Liu, and Xinchang Zhang. SPIE, 2009. http://dx.doi.org/10.1117/12.813145.

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Sun, Qiang, Biao Hou, and Licheng Jiao. "SAR image edge detection based on contourlet-domain hidden Markov models." In MIPPR 2005 Image Analysis Techniques, edited by Deren Li and Hongchao Ma. SPIE, 2005. http://dx.doi.org/10.1117/12.654554.

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Liu, Diyi, Shogo Arai, Fuyuki Tokuda, Yajun Xu, Jun Kinugawa, and Kazuhiro Kosuge. "Deep-Learning based Robust Edge Detection for Point Pair Feature-based Pose Estimation with Multiple Edge Appearance Models." In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. http://dx.doi.org/10.1109/robio49542.2019.8961752.

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Harth-Kitzerow, Christopher, and Gonzalo Munilla Garrido. "Verifying Outsourced Computation in an Edge Computing Marketplace." In 4th International Conference on Machine Learning & Applications (CMLA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121112.

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An edge computing marketplace could enable IoT devices (Outsourcers) to outsource computation to any participating node (Contractors) in their proximity. In return, these nodes receive a reward for providing computation resources. In this work, we propose a scheme that verifies the integrity of arbitrary deterministic functions in the presence of both dishonest Outsourcers and Contractors who try to maximize their expected payoff. We compile a comprehensive set of threats for this adversary model and show that not all of these threats are addressed when combining verification techniques of related work. Our verification scheme fills the gap by detecting or preventing each identified threat. We tested our verification scheme with state-of-the-art pre-trained Convolutional Neural Network models designed for object detection. On all devices, our verification scheme causes less than 1ms computational overhead and a negligible network bandwidth overhead of at most 84 bytes per frame. Our implementation can also perform our verification scheme’s tasks parallel to the object detection to eliminate any latency overhead.
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Kumar, Aditya, and C. Fancy. "Enhancing Security in SMS by Combining NLP Models Using Ensemble Learning for Spam Detection with Image Steganography Integration." In 2023 2nd International Conference on Edge Computing and Applications (ICECAA). IEEE, 2023. http://dx.doi.org/10.1109/icecaa58104.2023.10212103.

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Holtzhausen, PJ, V. Crnojevic, and BM Herbst. "The detection of naval vessels by fusion of edge and color background models." In 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2012. http://dx.doi.org/10.1109/ipta.2012.6469522.

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Reports on the topic "EDGE DETECTION MODELS"

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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