Relevant bibliographies by topics / LEGENDRE MOMENTS

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

Academic literature on the topic 'LEGENDRE MOMENTS'

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Published: 11 September 2023

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Journal articles on the topic "LEGENDRE MOMENTS"

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HOSNY, KHALID M. "EFFICIENT COMPUTATION OF LEGENDRE MOMENTS FOR GRAY LEVEL IMAGES." International Journal of Image and Graphics 07, no. 04 (October 2007): 735–47. http://dx.doi.org/10.1142/s021946780700288x.

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Direct computation of Legendre orthogonal moments requires huge arithmetic operations, which is very time consuming. Many works have described methods for reducing the computations involved in evaluating Legendre moments. Nevertheless, reduction computational complexity is still an open problem and needs more investigation. Existing algorithms mainly focused on binary images and compute Legendre moments using a set of geometric moments. We propose a fast and efficient method for computation of Legendre moments for binary and gray level images. A recurrence formula of one-dimensional Legendre moments will be established using the recursive property of Legendre polynomials; then the method will be extended to calculate the two-dimensional Legendre moments. This method is completely independent on geometric moment. The complexity analysis shows that the proposed method computes Legendre moments more efficiently than the direct method and the other conventional methods.
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Tamtsia, Aurelien Yeremou, Youcef Mezouar, Philippe Martinet, Haman Djalo, and Emmanuel Tonye. "2D Legendre Moments-Based Visual Control." Applied Mechanics and Materials 162 (March 2012): 487–96. http://dx.doi.org/10.4028/www.scientific.net/amm.162.487.

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Among region-based descriptors, geometric moments have been widely exploited to design visual servoing schemes. However, they present several disadvantages such as high sensitivity to noise measurement, high dynamic range and information redundancy (since they are not computed onto orthogonal basis). In this paper, we propose to use a class of orthogonal moments (namely Legendre moments) instead of geometric moments to improve the behavior of moment-based control schemes. The descriptive form of the interaction matrix related to the Legendre moments computed from a set of points is rst derived. Six visual features are then selected to design a partially-decoupled control scheme. Finally simulated and experimental results are presented to illustrate the validity of our proposal.
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SHEN, JUN, WEI SHEN, and DANFEI SHEN. "ON GEOMETRIC AND ORTHOGONAL MOMENTS." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 07 (November 2000): 875–94. http://dx.doi.org/10.1142/s0218001400000581.

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Moments are widely used in pattern recognition, image processing, computer vision and multiresolution analysis. To clarify and to guide the use of different types of moments, we present in this paper a study on the different moments and compare their behavior. After an introduction to geometric, Legendre, Hermite and Gaussian–Hermite moments and their calculation, we analyze at first their behavior in spatial domain. Our analysis shows orthogonal moment base functions of different orders having different number of zero-crossings and very different shapes, therefore they can better separate image features based on different modes, which is very interesting for pattern analysis and shape classification. Moreover, Gaussian–Hermite moment base functions are much more smoothed, they are thus less sensitive to noise and avoid the artifacts introduced by window function discontinuity. We then analyze the spectral behavior of moments in frequency domain. Theoretical and numerical analyses show that orthogonal Legendre and Gaussian–Hermite moments of different orders separate different frequency bands more effectively. It is also shown that Gaussian–Hermite moments present an approach to construct orthogonal features from the results of wavelet analysis. The orthogonality equivalence theorem is also presented. Our analysis is confirmed by numerical results, which are then reported.
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Lakshmi Deepika, C., A. Kandaswamy, C. Vimal, and B. Satish. "Palmprint authentication using modified legendre moments." Procedia Computer Science 2 (2010): 164–72. http://dx.doi.org/10.1016/j.procs.2010.11.021.

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Fu, Bo, Jianzhong Zhou, Yuhong Li, Guojun Zhang, and Cheng Wang. "Image analysis by modified Legendre moments." Pattern Recognition 40, no. 2 (February 2007): 691–704. http://dx.doi.org/10.1016/j.patcog.2006.05.020.

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Prévost, Marc. "Legendre modified moments for Euler's constant." Journal of Computational and Applied Mathematics 219, no. 2 (October 2008): 484–92. http://dx.doi.org/10.1016/j.cam.2007.09.015.

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Dasari, Sridhar, and I. V. Murali Krishna. "Combined Classifier for Face Recognition using Legendre Moments." Computer Engineering and Applications Journal 1, no. 2 (December 29, 2012): 107–18. http://dx.doi.org/10.18495/comengapp.v1i2.12.

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In this paper, a new combined Face Recognition method based on Legendre moments with Linear Discriminant Analysis and Probabilistic Neural Network is proposed. The Legendre moments are orthogonal and scale invariants hence they are suitable for representing the features of the face images. The proposed face recognition method consists of three steps, i) Feature extraction using Legendre moments ii) Dimensionality reduction using Linear Discrminant Analysis (LDA) and iii) classification using Probabilistic Neural Network (PNN). Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Legendre moments and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from Olivetty Research Laboratories (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers.DOI:Â 10.18495/comengapp.12.107118
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Hui, Fan, Hai Feng Wang, and Jin Jiang Li. "Image Registration Based on Feature Points Krawtchouk Moments." Applied Mechanics and Materials 40-41 (November 2010): 584–89. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.584.

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An image registration based on feature points Krawtchouk moments is proposed. Moments are the shape descriptors based on region. Krawtchouk moments are a set of discrete orthogonal moments and are more suitable for describing two-dimensional images compared to Zemike, Legendre moments. In the image registration based on feature points Krawtchouk moments, Krawtchouk moment invariants of the feature points neighborhood that have been extracted are solved, and then these Krawtchouk moment invariants constitute feature vectors used to describe the feature points, finally feature points are matched by calculating the Euclidean distance of feature vectors. The results of experiments show that Krawtchouk moment is simple and effective to describe image and is independent of rotation, scaling, and translation of the image.
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Dhekane, Manasi, Ayan Seal, and Pritee Khanna. "Illumination and Expression Invariant Face Recognition." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 12 (September 17, 2017): 1756018. http://dx.doi.org/10.1142/s0218001417560183.

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An illumination and expression invariant face recognition method based on uniform local binary patterns (uLBP) and Legendre moments is proposed in this work. The proposed method exploits uLBP texture features and Legendre moments to make a feature representation with enhanced discriminating power. The input images are preprocessed to extract the face region and normalized. From normalized image, uLBP codes are extracted to obtain texture image which overcomes the effect of monotonic temperature changes. Legendre moments are computed from this texture image to get the required feature vector. Legendre moments conserve the spatial structure information of the texture image. The resultant feature vector is classified using k-nearest neighbor classifier with [Formula: see text] norm. To evaluate the proposed method, experiments are performed on IRIS and NVIE databases. The proposed method is tested on both visible and infrared images under different illumination and expression variations and performance is compared with recently published methods in terms of recognition rate, recall, length of feature vector, and computational time. The proposed method gives better recognition rates and outperforms other recent face recognition methods.
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Farouk, R. M., and Qamar A. A. Awad. "Image representation based on fractional order Legendre and Laguerre orthogonal moments." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 2 (February 2021): 54–59. http://dx.doi.org/10.21833/ijaas.2021.02.007.

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In this paper, we have introduced new sets of fractional order orthogonal basis moments based on Fractional order Legendre orthogonal Functions (FLeFs) and Fractional order Laguerre orthogonal Functions (FLaFs) for image representation. We have generated a novel set of Fractional order Legendre orthogonal Moments (FLeMs) from fractional order Legendre orthogonal functions and a new set of Fractional order Laguerre orthogonal Moments (FLaMs) from the fractional order Laguerre orthogonal functions. The new presented sets of (FLeMs) and (FLaMs) are tested with the recently introduced Fractional order Chebyshev orthogonal Moments (FCMs). This edge detection filter can be used successfully in the gray level image and color images. The new sets of fractional moments are used to reconstruct the gray level image. The numerical results show FLeMs and FLaMs are promised techniques for image representation. The computational time of the proposed techniques is compared with the computational time of Chebyshev orthogonal Moments techniques and gives better results. Also, the fractional parameters give the flexibility of studying global features of the image at different positions of moments.
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Dissertations / Theses on the topic "LEGENDRE MOMENTS"

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Al-Mohamad, Diaa. "Estimation d'un modèle de mélange paramétrique et semiparamétrique par des phi-divergences." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066291/document.

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L’étude des modèles de mélanges est un champ très vaste en statistique. Nous présentons dans la première partie de la thèse les phi-divergences et les méthodes existantes qui construisent des estimateurs robustes basés sur des phi-divergences. Nous nous intéressons en particulier à la forme duale des phi-divergences et nous construisons un nouvel estimateur robuste basant sur cette formule. Nous étudions les propriétés asymptotiques de cet estimateur et faisons une comparaison numérique avec les méthodes existantes. Dans un seconde temps, nous introduisons un algorithme proximal dont l’objectif est de calculer itérativement des estimateurs basés sur des critères de divergences statistiques. La convergence de l’algorithme est étudiée et illustrée par différents exemples théoriques et sur des données simulées. Dans la deuxième partie de la thèse, nous construisons une nouvelle structure pour les modèles de mélanges à deux composantes dont l’une est inconnue. La nouvelle approche permet d’incorporer une information a priori linéaire de type moments ou L-moments. Nous étudions les propriétés asymptotiques des estimateurs proposés. Des simulations numériques sont présentées afin de montrer l’avantage de la nouvelle approche en comparaison avec les méthodes existantes qui ne considèrent pas d’information a priori à part une hypothèse de symétrie sur la composante inconnue
The study of mixture models constitutes a large domain of research in statistics. In the first part of this work, we present phi-divergences and the existing methods which produce robust estimators. We are more particularly interested in the so-called dual formula of phi-divergences. We build a new robust estimator based on this formula. We study its asymptotic properties and give a numerical comparison with existing methods on simulated data. We also introduce a proximal-point algorithm whose aim is to calculate divergence-based estimators. We give some of the convergence properties of this algorithm and illustrate them on theoretical and simulated examples. In the second part of this thesis, we build a new structure for two-component mixture models where one component is unknown. The new approach permits to incorporate a prior linear information about the unknown component such as moment-type and L-moments constraints. We study the asymptotic properties of the proposed estimators. Several experimental results on simulated data are illustrated showing the advantage of the novel approach and the gain from using the prior information in comparison to existing methods which do not incorporate any prior information except for a symmetry assumption over the unknown component
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MATHUR, PALLAVI. "STUDY OF FACE RECOGNITION TECHNIQUES USING VARIOUS MOMENTS." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14115.

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The field of face recognition has been explored a lot and the work is still going on. In the presented work we have proposed a novel approach for face recognition using moments. Four methods have been used for feature extraction: Hu moments, Zernike moments, Legendre moments and Cumulants. Hu moments are a set of seven moments which have been derived from the conventional geometric moments. These are invariant against rotation, scaling and translation. Legendre moments and Zernike moments have an orthogonal basis set and can be used to represent an image with a minimum amount of information redundancy. They are based on the theory of orthogonal polynomials and can be used to recover an image from moment invariants. Cumulants are sensitive to the image details and therefore are suitable for representing the features of images. For feature extraction, moments of different orders are calculated which form the feature vectors. The obtained feature vectors are stored in the database and are classified using three classifiers: Minimum Distance Classifier, Support Vector Machine and K Nearest Neighbor. For testing the proposed approach, the ORL (Olivetty Research Laboratories) database is used. It consists of 40 subjects, each having 10 orientations.
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Schleif, Frank-Michael. "Momentbasierte Methoden der Schriftzeichenerkennung." 2002. https://ul.qucosa.de/id/qucosa%3A16758.

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Die maschinelle Erkennung von gedruckten Zeichen ist weitestgehend gelöst, bei handschriftlichen Daten ergeben sich jedoch nach wie vor hohe Fehlerraten. Die Ermittlung von diskriminanten Merkmalen aus Schriftzeichen zum Zweck der anschliessenden Klassifikation stellt ein schwieriges Teilproblem der optischen Schriftzeichenerkennung dar. In der vorliegenden Arbeit werden verschiedene Verfahren zur Merkmalsgewinnung mit dem Schwerpunkt 'statistische Momente' verglichen. Die verschiedenen Verfahren werden beschrieben und umfassend auf ihre Eignung im Kontext der OCR untersucht. Konkret werden Fragen der Diskriminanzfaehigkeit, der Sensibilitaet auf das Datenmaterial, der Invarianzeigenschaften und der effizienten Berechnung beantwortet. Dabei wurde ein System zur Erkennung handschriftlicher numerischer Symbole entwickelt, bei dem auch verschiedenste Klassifikationsverfahren und Methoden zur Merkmalsselektion und Merkmalsreduktion Anwendung fanden. Im Ergebnis der Arbeit zeigte sich eine prinzipielle Eignung aller betrachteten Verfahren, wobei die Zernikemomente, Legendremomente und Waveletmomente am besten abschnitten und zu Erkennungsraten von bis zu 97 % führten. Es wurden Empfehlungen zu den Verfahren und geeignete Parametrisierungen angegeben.
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Books on the topic "LEGENDRE MOMENTS"

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Chaturvedi, Ravi. Legendary Indian cricketers: Men, moments, and memories. New Delhi: Ocean Books, 2009.

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Legendary Indian cricketers: Men, moments, and memories. New Delhi: Ocean Books, 2009.

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H, Isaacs Ronald, and Olitzky Kerry M, eds. Sacred moments: Tales from the Jewish life cycle. Northvale, N.J: Jason Aronson, 1995.

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The New York Yankees: Legendary heroes, magical moments, and amazing statistics through the decades. New York: W. Morrow, 1997.

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Butcher, Samuel J. 1939- ill., ed. Precious moments Caleb: A very shy angel. Grand Rapids, MI: Baker Books, 1997.

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Yechiel, Spero. One shining moment: Heartwarming stories of courage and character to raise your spirits. Brooklyn, NY: Mesorah Publications, 2005.

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Lydia, Goehr, and Herwitz Daniel Alan 1955-, eds. The Don Giovanni moment: Essays on the legacy of an opera. New York: Columbia University Press, 2006.

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Byron, Motley, ed. Ruling over Monarchs, Giants & Stars: An umpire's true tales of incredible moments, legendary players, and wild adventures in the Negro Leagues. New York: Sports Pub., 2011.

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1968-, Arvesú Jorge, and Lopez Lagomasino Guillermo 1948-, eds. Recent advances in orthogonal polynomials, special functions, and their applications: 11th International Symposium on Orthogonal Polynomials, Special Functions, and Their Applications, August 29-September 2, 2011, Universidad Carlos III de Madrid, Leganes, Spain. Providence, R.I: American Mathematical Society, 2012.

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Park, Jack, Maureen Zappala, Archie Griffin, and Luke Fickell. Buckeye Reflections: Legendary Moments From Ohio State Football. Lexington Press, 2018.

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Book chapters on the topic "LEGENDRE MOMENTS"

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Machhour, Abderrahmane, Amal Zouhri, Mostafa El Mallahi, Zakia Lakhliai, Ahmed Tahiri, and Driss Chenouni. "Plants Classification Using Neural Shifted Legendre-Fourier Moments." In Advances in Smart Technologies Applications and Case Studies, 149–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53187-4_18.

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Zenkouar, K., H. El Fadili, and H. Qjidaa. "Skeletonization of Noisy Images via the Method of Legendre Moments." In Advanced Concepts for Intelligent Vision Systems, 452–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11558484_57.

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Machhour, Abderrahmane, Mostafa El Mallahi, Zakia Lakhliai, Ahmed Tahiri, and Driss Chenouni. "Image Classification Using Legendre–Fourier Moments and Artificial Neural Network." In Embedded Systems and Artificial Intelligence, 305–15. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6_29.

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Wahi, Amitabh, S. Sundaramurthy, and P. Poovizhi. "Handwritten Tamil Character Recognition Using Zernike Moments and Legendre Polynomial." In Advances in Intelligent Systems and Computing, 595–603. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2135-7_63.

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Cai, Huinan, and Youyao Liu. "Research on Extraction of Light Strip Centers Based on Legendre Moments." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 247–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_26.

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Alice, K., M. Sindhuja, and B. Pandeeswari. "Circular BlockEntropies and Fast Legendre Moments Based Content Preserving Image Authentication." In Communications in Computer and Information Science, 119–32. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27622-4_10.

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Darwish, Mohamed M., Khalid M. Hosny, and Sara T. Kamal. "Improved Color Image Watermarking Using Logistic Maps and Quaternion Legendre-Fourier Moments." In Multimedia Security Using Chaotic Maps: Principles and Methodologies, 137–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38700-6_6.

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Zhang, Yan, Bogdan J. Matuszewski, Aymeric Histace, and Frédéric Precioso. "Statistical Shape Model of Legendre Moments with Active Contour Evolution for Shape Detection and Segmentation." In Computer Analysis of Images and Patterns, 51–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_7.

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Marengo, Emilio, Elisa Robotti, and Marco Demartini. "The Use of Legendre and Zernike Moment Functions for the Comparison of 2-D PAGE Maps." In Methods in Molecular Biology, 271–88. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3255-9_15.

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"Legendre Moments." In Moment Functions in Image Analysis — Theory and Applications, 49–56. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789812816092_0004.

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Conference papers on the topic "LEGENDRE MOMENTS"

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Jun Shen and Danfei Shen. "Orthogonal Legendre moments and their calculation." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546825.

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Bustacara-Medina, Cesar, and Enrique Ruiz-García. "Accuracy of Legendre Moments for Image Representation." In WSCG 2023 – 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision. University of West Bohemia, Czech Republic, 2023. http://dx.doi.org/10.24132/csrn.3301.29.

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Existing works on orthogonal moments are mainly focused on optimizing classical orthogonal Cartesian moments, such as Legendre moments, Gauss-Hermite moments, Gegenbauer moments, and Chebyshev moments. Research in this area generally includes accurate calculation, fast computation, robustness/invariance optimization, and the application of orthogonal moments. This paper presents the inclusion of the integration method proposed by Holoborodko to calculate the Legendre moments. The results obtained are compared with the traditional equation and the methods proposed by Hosny and Pawlak to approximate the integration computation.
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Ong, Lee-Yeng, Chee-Way Chong, and Rosli Besar. "Scale Invariants of Three-Dimensional Legendre Moments." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.344160.

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Boucetta, A., and K. E. Melkemi. "Hand shape recognition using Hu and Legendre moments." In the 6th International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2523514.2523547.

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Jaisakthi, S. M., and C. Aravindan. "Face detection based on eigenfaces and legendre moments." In TENCON 2009 - 2009 IEEE Region 10 Conference. IEEE, 2009. http://dx.doi.org/10.1109/tencon.2009.5396153.

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Aymaz, Samet, Seyma Aymaz, and Guzin Ulutas. "Detection of copy move forgery using Legendre Moments." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495942.

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Shen, Jun, and Dan-Fei Shen. "Image characterization by fast calculation of Legendre moments." In Satellite Remote Sensing III, edited by Jacky Desachy. SPIE, 1996. http://dx.doi.org/10.1117/12.262898.

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Chiang, Amy, and Simon Liao. "Image analysis with symmetry properties of Legendre moments." In 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, 2017. http://dx.doi.org/10.1109/icivc.2017.7984583.

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Ban-Hoe Kwan, Kok-Meng Ong, and R. Paramesran. "Noise Removal of ECG Signals using Legendre Moments." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615762.

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Padilla-Vivanco, Alfonso, and A. Colunga-Ruiz. "Digital image reconstruction using the Legendre and Chebyshev moments." In Optical Science and Technology, the SPIE 49th Annual Meeting, edited by Andrew G. Tescher. SPIE, 2004. http://dx.doi.org/10.1117/12.558776.

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