Relevant bibliographies by topics / Descripteur de Radon

Contents

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

Academic literature on the topic 'Descripteur de Radon'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Descripteur de Radon"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Descripteur de Radon"

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K, C. Santosh. "Graphics Recognition using Spatial Relations and Shape Analysis." Thesis, Vandoeuvre-les-Nancy, INPL, 2011. http://www.theses.fr/2011INPL096N/document.

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Dans l’état de l’art actuel, la reconnaissance de symboles signifie généralement la reconnaissance des symboles isolés. Cependant, ces méthodes de reconnaissance de symboles isolés ne sont pas toujours adaptés pour résoudre les problèmes du monde réel. Dans le cas des documents composites qui contiennent des éléments textuels et graphiques, on doit être capable d’extraire et de formaliser les liens qui existent entre les images et le texte environnant, afin d’exploiter les informations incorporées dans ces documents.Liés à ce contexte, nous avons d’abord introduit une méthode de reconnaissance
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K, C. Santosh. "Graphics Recognition using Spatial Relations and Shape Analysis." Electronic Thesis or Diss., Vandoeuvre-les-Nancy, INPL, 2011. http://www.theses.fr/2011INPL096N.

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Dans l’état de l’art actuel, la reconnaissance de symboles signifie généralement la reconnaissance des symboles isolés. Cependant, ces méthodes de reconnaissance de symboles isolés ne sont pas toujours adaptés pour résoudre les problèmes du monde réel. Dans le cas des documents composites qui contiennent des éléments textuels et graphiques, on doit être capable d’extraire et de formaliser les liens qui existent entre les images et le texte environnant, afin d’exploiter les informations incorporées dans ces documents.Liés à ce contexte, nous avons d’abord introduit une méthode de reconnaissance
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Book chapters on the topic "Descripteur de Radon"

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Babaie, Morteza, Hany Kashani, Meghana D. Kumar, and H. R. Tizhoosh. "A New Local Radon Descriptor for Content-Based Image Search." In Artificial Intelligence in Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59137-3_41.

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Hasegawa, Makoto, and Salvatore Tabbone. "A Local Adaptation of the Histogram Radon Transform Descriptor: An Application to a Shoe Print Dataset." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_74.

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Conference papers on the topic "Descripteur de Radon"

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Tabbone, S., O. Ramos Terrades, and S. Barrat. "Histogram of radon transform. A useful descriptor for shape retrieval." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761555.

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Kumar, Soorya S., and Jiji C.V. "Histogram of Radon Projections: A new descriptor for object detection." In 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2015. http://dx.doi.org/10.1109/ncvpripg.2015.7489996.

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Babaie, Morteza, Mohammad Ebrahim Shiri, and Mahdi Bahaghighat. "A new descriptor for UAV images mapping by applying discrete local radon." In 2018 8th Conference of AI & Robotics and 10th RoboCup Iranopen International Symposium (IRANOPEN). IEEE, 2018. http://dx.doi.org/10.1109/rios.2018.8406631.

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Pourghassem, Hossein. "Invariant pattern descriptor-based logo recognition using radon transform and complex moments." In 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. http://dx.doi.org/10.1109/indicon.2015.7443135.

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Hoang, Thai V., and Salvatore Tabbone. "A Geometric Invariant Shape Descriptor Based on the Radon, Fourier, and Mellin Transforms." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.512.

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Bathina, Yogesh Babu, M. V. Kartheek Medathati, and Jayanthi Sivaswamy. "Robust matching of multi-modal retinal images using radon transform based local descriptor." In the ACM international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1882992.1883108.

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Nacereddine, Nafaa, Salavatore Tabbone, Djemel Ziou, and Latifa Hamami. "Shape-Based Image Retrieval Using a New Descriptor Based on the Radon and Wavelet Transforms." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.492.

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Hasegawa, Makoto, and Salvatore Tabbone. "A Shape Descriptor Combining Logarithmic-Scale Histogram of Radon Transform and Phase-Only Correlation Function." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.45.

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Shi, Jian-Yu, and Yan-Ning Zhang. "Using Texture Descriptor and Radon Transform to Characterize Protein Structure and Build Fast Fold Recognition." In 2009 International Association of Computer Science and Information Technology - Spring Conference. IEEE, 2009. http://dx.doi.org/10.1109/iacsit-sc.2009.56.

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Journal articles
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