Relevant bibliographies by topics / Matrices de cooccurrence

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

Academic literature on the topic 'Matrices de cooccurrence'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Matrices de cooccurrence"

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Figueiras-Vidal, A., J. Paez-Borrallo, and R. Garcia-Gomez. "On using cooccurrence matrices to detect periodicities." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 1 (1987): 114–16. http://dx.doi.org/10.1109/tassp.1987.1165031.

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Nataraj, Lakshmanan, Michael Goebel, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, and B. S. Manjunath. "Holistic Image Manipulation Detection using Pixel Cooccurrence Matrices." Electronic Imaging 2021, no. 4 (2021): 277–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.4.mwsf-277.

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Digital image forensics aims to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods. While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations. In this paper, we propose a novel approach to holistically detect tampered images using a combination of pixel co-occurrence matrices and deep learning. We extract horizontal and vertical co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Our method is agnostic to the type of manipulation and classifies an image as tampered or untampered. We train and validate our model on a dataset of more than 86,000 images. Experimental results show that our approach is promising and achieves more than 0.99 area under the curve (AUC) evaluation metric on the training and validation subsets. Further, our approach also generalizes well and achieves around 0.81 AUC on an unseen test dataset comprising more than 19,740 images released as part of the Media Forensics Challenge (MFC) 2020. Our score was highest among all other teams that participated in the challenge, at the time of announcement of the challenge results.
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Honda, Katsuhiro, Toshiya Oda, Daiji Tanaka, and Akira Notsu. "A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices." Advances in Fuzzy Systems 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/729072.

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In many real world data analysis tasks, it is expected that we can get much more useful knowledge by utilizing multiple databases stored in different organizations, such as cooperation groups, state organs, and allied countries. However, in many such organizations, they often hesitate to publish their databases because of privacy and security issues although they believe the advantages of collaborative analysis. This paper proposes a novel collaborative framework for utilizing vertically partitioned cooccurrence matrices in fuzzy co-cluster structure estimation, in which cooccurrence information among objects and items is separately stored in several sites. In order to utilize such distributed data sets without fear of information leaks, a privacy preserving procedure is introduced to fuzzy clustering for categorical multivariate data (FCCM). Withholding each element of cooccurrence matrices, only object memberships are shared by multiple sites and their (implicit) joint co-cluster structures are revealed through an iterative clustering process. Several experimental results demonstrate that collaborative analysis can contribute to revealing global intrinsic co-cluster structures of separate matrices rather than individual site-wise analysis. The novel framework makes it possible for many private and public organizations to share common data structural knowledge without fear of information leaks.
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Zhang, Tao, Yu Qing Chen, and Xiang Yu Yu. "Texture Feature-Based Particle Filter Video Tracking Using Cooccurrence Matrices." Applied Mechanics and Materials 457-458 (October 2013): 1294–97. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1294.

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.In this paper, a video tracking approach based on particle filter is proposed. Texture information is used instead of color. In the proposed approach, gray cooccurrence matrices are used as the texture metric. Experimental results show that the proposed algorithm lead to better result than color feature-based particle filter-based video tracking algorithm and is an effective tool for complicated video tracking application.
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Londhe, Renuka R. "Plant Leaf Analysis Based on Color Histogram and Cooccurrence Matrices." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 153–57. http://dx.doi.org/10.26438/ijcse/v7i2.153157.

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Mitrea, Delia, Paulina Mitrea, Sergiu Nedevschi, et al. "Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images." Computational and Mathematical Methods in Medicine 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/348135.

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The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue.
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Zhang, Yongping, Ruili Wang, and Peter Hunter. "Airborne pollen texture discrimination using wavelet transforms in combination with cooccurrence matrices." International Journal of Intelligent Systems Technologies and Applications 1, no. 1/2 (2005): 143. http://dx.doi.org/10.1504/ijista.2005.007312.

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Arvis, Vincent, Christophe Debain, Michel Berducat, and Albert Benassi. "GENERALIZATION OF THE COOCCURRENCE MATRIX FOR COLOUR IMAGES: APPLICATION TO COLOUR TEXTURE CLASSIFICATION." Image Analysis & Stereology 23, no. 1 (2011): 63. http://dx.doi.org/10.5566/ias.v23.p63-72.

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Three different approaches to colour texture analysis are tested on the classification of images from the VisTex and Outex databases. All the methods tested are based on extensions of the cooccurrence matrix method. The first method is a multispectral extension since cooccurrence matrices are computed both between and within the colour bands. The second uses joint colour-texture features: colour features are added to grey scale texture features in the entry of the classifier. The last uses grey scale texture features computed on a previously quantized colour image. Results show that the multispectral method gives the best percentages of good classification (VisTex: 97.9%, Outex: 94.9%). The joint colour-texture method is not far from it (VisTex: 96.8%, Outex: 91.0%), but the quantization method is not very good (VisTex:83.6%, Outex:68.4%). Each method is decomposed to try to understand each one deeper, and computation time is estimated to show that multispectral method is fast enough to be used in most real time applications.
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Nielsen, Birgitte, Fritz Albregtsen, Wanja Kildal, and Håvard E. Danielsen. "Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections." Analytical Cellular Pathology 23, no. 2 (2001): 75–88. http://dx.doi.org/10.1155/2001/683747.

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In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images. The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images.
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LAGHARI, M. S., and A. BOUJARWAH. "WEAR PARTICLE TEXTURE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 03 (1999): 415–28. http://dx.doi.org/10.1142/s0218001499000240.

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Analysis of wear debris carried by a lubricant in an oil-wetted system provides important information about the condition of a machine. This paper describes the analysis of microscopic metal particles generated by wear using computer vision and image processing. The aim is to classify these particles according to their morphology and surface texture and by using the information obtained, to predict wear failure modes in engines and other machinery. This approach obviates the need for specialists and reliance on human visual inspection techniques. The procedure reported in this paper, is used to classify surface features of the wear particles by using artificial neural networks. A visual comparison between cooccurrence matrices representing five different texture classes is described. Based on these comparisons, matrices of reduced sizes are utilized to train a feed-forward neural classifier in order to distinguish between the various texture classes.
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Dissertations / Theses on the topic "Matrices de cooccurrence"

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Khazzi, Ahmed. "Le PCO et la cooccurrence des consonnes coronales dans la théorie des matrices et des étymons." Lyon, École normale supérieure lettres et sciences humaines, 2004. http://www.theses.fr/2004ENSF0018.

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Notre thèse s'inscrit dans le cadre général de l'organisation du lexique de l'arabe qui est structuré, selon la plupart des auteurs, autour d'une base trilitère. Nous avons démontré, en adoptant la Théorie des Matrices et des Etymons (TME), élaborée par G. Bohas, que la racine trilitère ne joue aucun rôle dans cette organisation du fait qu'elle n'explique pas, entre autres, les nombreuses régularités sémantiques qui existent entre les entrées lexicales. Plus exactement, notre présent travail a été presque entièrement consacré aux restrictions de cooccurrence entre deux consonnes identiques ou homorganiques dans une même "racine verbale trilitère". Nous avons démontré que la TME combinée aux principes de la phonologie autosegmentale (principalement avec le Principe du Contour obligatoire : PCO) permet de donner une explication plus cohérente à ce processus phonologique. Les explications que nous avons proposées prennent en considération non seulement les propriétés formelles (phonologiques), mais aussi les propriétés lexico-sémantiques des entrées lexicales. Sur la base d'un dépouillement exhaustif des formes verbales R1R2R3 du Dictionnaire Kazimerski, nous avons montré que le PCO s'applique à la "racine trilitère" quand la paire de consonnes en question est en R1R2 et R2R3, et à l'étymon lorsque les consonnes homorganiques sont en R1R3. C'est ce qui explique le nombre assez important des formes attestées R1R2R3 où R1 et R2 sont des coronales. L'étymon est un domaine plus abstrait que celui de la "racine", il est composé de deux consonnes associées à un sens spécifique qui peuvent former les "racines trilitères" par ajout d'une troisième consonne ou par croisement avec un autre étymon. L'étymon ne comporte donc pas, par exemple, deux consonnes ayant les mêmes spécifications pour les traits [Coronal], [±son] (*єrn, *єrl, *єln), [±cont] et [±pharyngal] (*єsS, *єsz, *єSZ, *єtd, *єtd)
This thesis deals with the organization of the Arabic lexicon, which, according to most authors (ex: J. Cantineau, D. Cohen. . . ), is structured on the basis of "trilitere roots". We have demonstrated, while adopting the Theory of the Matrices and the Etymons (TME), elaborated by G. Bohas (), that the trilitere roots" do not play any role in this organization, mainly because they do not explain the numerous semantic relations that exist betwenn the lexical entries. More precisely, our present work has been almost entirely devoted to the process of cooccurence restrictions between two identical or homorganic consonants in a same "trilitere verbal root". We demonstrated that the TME combined with principles of autosegmental phonology (mainly with the Obligatory Contour Principle: OCP) permits a more coherent explanation of this phonological process The explanations that we proposed not only take into consideration the formal (phonological), but also the lexico-semantic properties of the lexical entries. On the basis of an exhaustive inventory of the verbal forms R1R2R3 of the Kazimerski Dictionary, we showed that the domain of PCO is "the trilitere roots" when the pair of consonants are in R1R2 and R2R3 positions, and the etymon when the homorganic consonants are in R1R3. This explains the important number of attested forms R1R2R3 when R1 and R2 are coronales. The etymon is a more abstract morphological category than that of the "root", it is composed of two consonants associated to a specific meaning. An etymon can form "trilitere roots" by, among others, the addition of a third consonant (at the initial, median or final position), or by crossing with another etymon. Therefor, the etymons do not include, for example, two consonants having the same specifications for the features [Coronal], [±son] (*єrn, *єrl, *єln), or [±cont] and [±pharyngla] (*єsS, *єsz, *єSZ, *єtd, *єtT)
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Ravikumar, Rahul. "Multi-scale texture analysis of remote sensing images using gabor filter banks and wavelet transforms." Thesis, [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3175.

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Inam, Ul Haq Muhammad. "Texture analysis in the Logarithmic Image Processing (LIP) framework." Phd thesis, Université Jean Monnet - Saint-Etienne, 2013. http://tel.archives-ouvertes.fr/tel-00998492.

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This thesis looks at the evaluation of textures in two different perspectives using logarithmic image processing (LIP) framework. The first case after introducing the concept of textures and giving some classical approaches of textures evaluation, it gives an original approach of textures evaluation called covariogram which is derived from similarity metrics like distances or correlations etc. The classical covariogram which is derived from the classical similarity metrics and LIP covariogram are then applied over several images and the efficiency of the LIP one is clearly shown for darkened images. The last two chapters offer a new approach by considering the gray levels of an image as the phases of a medium. Each phase simulates like a percolation of a liquid in a medium defining the percolation trajectories. The propagation from one pixel to another is taken as easy or difficult determined by the difference of the gray level intensities. Finally different parameters like fractality from fractal dimensions, mean histogram etc associated to these trajectories are derived, based on which the primary experiment for the classification of random texture is carried out determining the relevance of this idea. Obviously, our study is only first approach and requires additional workout to obtain a reliable method of classification
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Hanifi, Majdoulayne. "Extraction de caractéristiques de texture pour la classification d'images satellites." Toulouse 3, 2009. http://thesesups.ups-tlse.fr/675/.

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Cette thèse s'inscrit dans le cadre général du traitement des données multimédias. Nous avons plus particulièrement exploité les images satellitaires pour la mise en application de ces traitements. Nous nous sommes intéressés à l'extraction de variables et de caractéristiques texturelles ; nous avons proposé une nouvelle méthode de pré-traitement des textures afin d'améliorer l'extraction de ces attributs caractéristiques. L'augmentation de la résolution des satellites récents a, paradoxalement, perturbé les chercheurs lors des premières classifications sur des données à haute résolution. Les cartes très homogènes, obtenues jusqu'alors en moyenne résolution, devenaient très fragmentées et difficiles à utiliser avec les mêmes algorithmes de classification. Une façon de remédier à ce problème consiste à caractériser le pixel en cours de classification par des paramètres mesurant l'organisation spatiale des pixels de son voisinage. Il existe plusieurs approches à l'analyse de texture dans les images. Dans le cadre des images satellitaires, l'approche statistique semble être habituellement retenue, ainsi que les méthodes des matrices de cooccurrences et du corrélogramme, basées sur l'analyse statistique au deuxième ordre (au sens des probabilités sur des couples de pixels). Et ce sont les deux dernières méthodes sur lesquelles nous allons se baser pour en extraire l'information texturelle sous forme d'un vecteur. Ces matrices présentent des inconvénients, tels que la taille mémoire nécessaire et le temps de calcul des paramètres élevé. Pour contourner ce problème, nous avons cherché une méthode de réduction du nombre de niveaux de gris appelée codage de rang (permettant de passer, dans un premier temps de 256 niveaux à 9 niveaux de gris, puis ensuite pour améliorer la qualité de l'image, passer de 9 à 16 niveaux de gris), tout en conservant la structure et la texture de l'image. L'ensemble de cette thèse a donc permis de montrer que la méthode de codage est une meilleure façon pour compresser une image sans toutefois perdre de l'information texturelle. Il permet de réduire la taille des données, ce qui réduira le temps de calcul des caractéristiques
This thesis joins in the general frame of the multimedia data processing. We particularly exploited the satellite images for the application of these treatments. We were interested in the extraction of variables and texturelles characteristics; we proposed a new method of pre-treatment of textures to improve the extraction of these characteristic attributes. The increase of the resolution of the satellites disrupted, paradoxically, the researchers during the first classifications on high-resolution data. The very homogeneous maps, obtained until then on average resolution, became very split up and difficult to use with the same algorithms of classification. A way of remedying this problem consists in characterizing the pixel in the classification by parameters measuring the spatial organization of the pixels of its neighbourhood. There are several approaches in the analysis of texture in the images. Within the framework of the satellite images, the statistical approach seems to be usually retained, as well as the methods of the cooccurrence matrix and the corrélogramme, based on the statistical analysis in the second order (in the sense of the probability on couples of pixels). And they are the last two methods on which we are going to base to extract the texturelle information from it in the form of a vector. These matrices present some drawbacks, such as the required memory size and the high calculation time of the parameters. To by-pass this problem, we looked for a method of reduction of the number of grey levels called rank coding (allowing to pass, at first of 256 levels at 9 grey levels, and then to improve the quality of the image, passing to 16 grey levels, while keeping the structure and the texture of the image. This thesis allowed to show that the method of coding is a better way to compress an image without losing however of the texturelle information. It allows to reduce the size of the data, what will reduce the calculation time of the characteristics
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AMICHI, ABDELAZIZ. "Importance des parametres de texture en imagerie medicale : applications aux images echographiques et aux images d'irm." Paris 11, 1998. http://www.theses.fr/1998PA11T019.

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Durga, Duruvasula Kanaka. "Texture analysis using cooccurrence matrices." 1989. http://hdl.handle.net/2097/23733.

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Fiset, Robert. "Système prototype pour le suivi des changements de l'occupation du sol en milieu urbain fondé sur les images du satellite RADARSAT-1." Thèse, 2005. http://hdl.handle.net/1866/17608.

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Book chapters on the topic "Matrices de cooccurrence"

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Honda, Katsuhiro, Akira Notsu, and Chi-Hyon Oh. "Handling Very Large Cooccurrence Matrices in Fuzzy Co-clustering by Sampling Approaches." In Soft Computing in Artificial Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05515-2_3.

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Conference papers on the topic "Matrices de cooccurrence"

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Tanaka, Daiji, Toshiya Oda, Katsuhiro Honda, and Akira Notsu. "Privacy preserving fuzzy co-clustering with distributed cooccurrence matrices." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044660.

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Setia, Lokesh, Alexandra Teynor, Alaa Halawani, and Hans Burkhardt. "Image classification using cluster cooccurrence matrices of local relational features." In the 8th ACM international workshop. ACM Press, 2006. http://dx.doi.org/10.1145/1178677.1178703.

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Ronsin, J., D. Barba, and S. Raboisson. "Comparison Between Cooccurrence Matrices, Local Histograms And Curvilinear Integration For Texture Characterization." In 1985 International Technical Symposium/Europe, edited by Francis J. Corbett, Howard J. Siegel, and Michael J. Duff. SPIE, 1986. http://dx.doi.org/10.1117/12.952294.

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Mitrea, Delia, Sergiu Nedevschi, Mihail Abrudean, and Radu Badea. "Colorectal cancer recognition from ultrasound images, using complex textural microstructure cooccurrence matrices, based on Laws' features." In 2015 38th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2015. http://dx.doi.org/10.1109/tsp.2015.7296304.

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Mitrea, Delia, Sergiu Nedevschi, and Radu Badea. "The role of the Textural Microstructure Cooccurrence Matrices in the classification of the abdominal tumors, based on ultrasound images." In 2014 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2014. http://dx.doi.org/10.1109/iccp.2014.6936972.

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Mitrea, Delia, Sergiu Nedevschi, and Radu Badea. "The role of the superior order GLCM and of the generalized cooccurrence matrices in the characterization and automatic diagnosis of the hepatocellular carcinoma, based on ultrasound images." In 2011 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2011. http://dx.doi.org/10.1109/iccp.2011.6047869.

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