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Journal articles on the topic 'Descripteur de Radon'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Yang, Jianwei, Liang Zhang, and Peiyao Li. "Radon–Fourier descriptor for invariant pattern recognition." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (2019): 1940004. http://dx.doi.org/10.1142/s0219691319400046.

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Radon transform is not only robust to noise, but also independent on the calculation of pattern centroid. In this paper, Radon–Mellin transform (RMT), which is a combination of Radon transform and Mellin transform, is proposed to extract invariant features. RMT converts any object into a closed curve. Radon–Fourier descriptor (RFD) is derived by applying Fourier descriptor to the obtained closed curve. The obtained RFD is invariant to scaling and rotation. (Generic) R-transform and some other Radon-based methods can be viewed as special cases of the proposed method. Experiments are conducted o

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2

Hamdi, Dhekra El, Ines Elouedi, Mai K. Nguyen, and Atef Hamouda. "A Conic Radon-based Convolutional Neural Network for Image Recognition." International Journal of Intelligent Systems and Applications 15, no. 1 (2023): 1–12. http://dx.doi.org/10.5815/ijisa.2023.01.01.

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This article presents a new approach for image recognition that proposes to combine Conical Radon Transform (CRT) and Convolutional Neural Networks (CNN). In order to evaluate the performance of this approach for pattern recognition task, we have built a Radon descriptor enhancing features extracted by linear, circular and parabolic RT. The main idea consists in exploring the use of Conic Radon transform to define a robust image descriptor. Specifically, the Radon transformation is initially applied on the image. Afterwards, the extracted features are combined with image and then entered as an

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3

SANTOSH, K. C., BART LAMIROY, and LAURENT WENDLING. "DTW–RADON-BASED SHAPE DESCRIPTOR FOR PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 03 (2013): 1350008. http://dx.doi.org/10.1142/s0218001413500080.

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In this paper, we present a pattern recognition method that uses dynamic programming for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalization based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several publ

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4

Miciak, Mirosław. "Invariant Radon-Moment Descriptor for Postal Applications." Image Processing & Communications 20, no. 4 (2015): 13–21. http://dx.doi.org/10.1515/ipc-2015-0040.

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Abstract In this article a new solution of handwritten digits recognition system for postal applications is presented. Moreover, in this paper, a new approach of handwritten characters recognition was presented. The implemented algorithm is applied to recognition of postal items on the basis of postcode information. In connection with this article the research was carried with all digit characters used in authentic zip code of various mail pieces. Additionally, the paper contains some preliminary image processing for example normalization of the character. The main objective of this article is

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5

Guangcan Liu, Zhouchen Lin, and Yong Yu. "Radon Representation-Based Feature Descriptor for Texture Classification." IEEE Transactions on Image Processing 18, no. 5 (2009): 921–28. http://dx.doi.org/10.1109/tip.2009.2013072.

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6

Yudong Zhang, and Lenan Wu. "A Rotation Invariant Image Descriptor based on Radon Transform." International Journal of Digital Content Technology and its Applications 5, no. 4 (2011): 209–17. http://dx.doi.org/10.4156/jdcta.vol5.issue4.26.

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7

Tabbone, S., L. Wendling, and J. P. Salmon. "A new shape descriptor defined on the Radon transform." Computer Vision and Image Understanding 102, no. 1 (2006): 42–51. http://dx.doi.org/10.1016/j.cviu.2005.06.005.

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8

Ma, Jinlin, and Ziping Ma. "3D Radon Transform for Shape Retrieval Using Bag-of-Visual-Features." International Arab Journal of Information Technology 17, no. 4 (2019): 471–79. http://dx.doi.org/10.34028/iajit/17/4/5.

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In order to improve the accuracy and efficiency of extracting features for 3D models retrieval, a novel approach using 3D radon transform and Bag-of-Visual-Features is proposed in this paper. Firstly the 3D radon transform is employed to obtain a view image using the different features in different angels. Then a set of local descriptor vectors are extracted by the SURF algorithm from the local features of the view. The similarity distance between geometrical transformed models is evaluated by using K-means algorithm to verify the geometric invariance of the proposed method. The numerical expe

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9

BECHAR, Hassane, Abdelhafid BESSAID, and Mahammed MESSADI. "Rearranged Descriptor Approach based on Radon Transform to Digits Recognition." Electrotehnica, Electronica, Automatica 69, no. 2 (2021): 83–91. http://dx.doi.org/10.46904/eea.21.69.2.1108010.

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In this paper, the Radon transform method is used to generate a set of rotation invariant characteristics. Experiments of our approach were carried out on a database of ten decimal digits (0 to 9) in 24 different orientations from 0° to 360 ° by a step of 15 °. A multilayer perceptron neural network is used in the classification phase to test the effectiveness of our approach. The proposed approach is noise-effective and leads to a classification rate equal to 100 % for images without noise and a classification rate equal to 95.2 for images with noise.

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10

Obaidullah, Sk, Sajib Ahmed, Teresa Gonçalves, and Luís Rato. "Radon-Wavelet Based Novel Image Descriptor for Mammogram Mass Classification." Journal of Automation, Mobile Robotics and Intelligent Systems 14, no. 2 (2020): 74–80. http://dx.doi.org/10.14313/jamris/2-2020/22.

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11

Hasegawa, Makoto, and Salvatore Tabbone. "Amplitude-only log Radon transform for geometric invariant shape descriptor." Pattern Recognition 47, no. 2 (2014): 643–58. http://dx.doi.org/10.1016/j.patcog.2013.07.024.

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12

Berenguer, Yerai, Luis Payá, David Valiente, Adrián Peidró, and Oscar Reinoso. "Relative Altitude Estimation Using Omnidirectional Imaging and Holistic Descriptors." Remote Sensing 11, no. 3 (2019): 323. http://dx.doi.org/10.3390/rs11030323.

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Currently, many tasks can be carried out using mobile robots. These robots must be able to estimate their position in the environment to plan their actions correctly. Omnidirectional vision sensors constitute a robust choice to solve this problem, since they provide the robot with complete information from the environment where it moves. The use of global appearance or holistic methods along with omnidirectional images constitutes a robust approach to estimate the robot position when its movement is restricted to the ground plane. However, in some applications, the robot changes its altitude w

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13

Hasegawa, Makoto, and Salvatore Tabbone. "Histogram of Radon transform with angle correlation matrix for distortion invariant shape descriptor." Neurocomputing 173 (January 2016): 24–35. http://dx.doi.org/10.1016/j.neucom.2015.04.100.

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14

Stanic, Karmen, Nina Turnsek, and Martina Vrankar. "Incorporation of EGFR mutation status into M descriptor of new TNM classification influences survival curves in non-small cell lung cancer patients." Radiology and Oncology 53, no. 4 (2019): 453–58. http://dx.doi.org/10.2478/raon-2019-0053.

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Abstract Background The 8th edition of tumor node metastasis (TNM) staging system for lung cancer introduced a revision of M descriptor. The limitation of new classification to predict prognosis is its focus on anatomical extent of the disease only. Information on molecular status of the tumor significantly influences treatment response and survival; however, data addressing this issue is scarce. This report points to the impact of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC) patients on survival in view of new M descriptors of TNM classification syste

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15

Singh, Yashbir, Shadi Atalla, Wathiq Mansoor, Rahul Paul, and Deepa Deepa. "To predict the left ventricular endocardial scar tissue pattern using Radon descriptor-based machine learning." BMC Research Notes 16, no. 1 (2023). http://dx.doi.org/10.1186/s13104-023-06466-0.

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Abstract Objective Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated Left ventricle (LV) segmentation to find the LV endocardial wall, performed morphological operations, and marked the region of the scar tissue on the endocardial wall of LV. Motivated by a Radon descriptor-based machine learning approach; the patches

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16

Deepa, Deepa, Yashbir Singh, Weichih Hu, and Ming Chen Wang. "Radon descriptor-based machine learning using CT images to predict the fat tissue on left atrium in the heart." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, July 5, 2022, 095441192211106. http://dx.doi.org/10.1177/09544119221110657.

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Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one’s life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the left atrium fat tissue pattern using Radon descriptor-based machine learning. This study developed a bridge between the Radon transform framework and machine learning to distinguish two distinct patterns. Motivated by a Radon descriptor-based machine le

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17

Yuan, Hao, Cheng Wu, and Feng Xie. "Sim-radon-based shape descriptor for deformable pattern recognition." Optics Letters, October 17, 2022. http://dx.doi.org/10.1364/ol.472622.

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