Gotowe bibliografie tematyczne / EDGE DETECTION MODELS

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Gotowa bibliografia na temat „EDGE DETECTION MODELS”

Autor: Grafiati
Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "EDGE DETECTION MODELS"

1

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 satisfactor
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Daoud, Mohammad I., Aamer Al-Ali, Rami Alazrai, et al. "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 (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, bu
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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 recogniti
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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 (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 sensi
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Pitas, I. "Markovian image models for image labeling and edge detection." Signal Processing 15, no. 4 (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 (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 (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 flam
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