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Статті в журналах з теми "Illustrations – classification":
Luo, Yang, and Yuewu Lin. "Effects of Illustration Types on the English Reading Performance of Senior High School Students with Different Cognitive Styles." English Language Teaching 10, no. 9 (July 31, 2017): 1. http://dx.doi.org/10.5539/elt.v10n9p1.
Ha, Yeoncheol, and Seung-Sep Kim. "Classification of large ornithopod dinosaur footprints using Xception transfer learning." PLOS ONE 18, no. 11 (November 29, 2023): e0293020. http://dx.doi.org/10.1371/journal.pone.0293020.
Hong, Wenting, Yongmei Liu, Weiqing Tong, and Yonghao Ma. "Automatic Recognition of Garment Illustrations Based on CNN." AATCC Journal of Research 8, no. 1_suppl (September 2021): 128–34. http://dx.doi.org/10.14504/ajr.8.s1.16.
Shi, Rui. "GSAIC: GeoScience Articles Illustration and Caption Dataset." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 289–97. http://dx.doi.org/10.54097/hset.v9i.1858.
Lan, Ziwen, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama. "Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships." Sensors 23, no. 10 (May 16, 2023): 4798. http://dx.doi.org/10.3390/s23104798.
Low, Christopher M., Jonathan M. Morris, Jane S. Matsumoto, Janalee K. Stokken, Erin K. O’Brien, and Garret Choby. "Use of 3D-Printed and 2D-Illustrated International Frontal Sinus Anatomy Classification Anatomic Models for Resident Education." Otolaryngology–Head and Neck Surgery 161, no. 4 (July 9, 2019): 705–13. http://dx.doi.org/10.1177/0194599819860832.
Marretta, Sandra Manfra, Alexander J. Schloss, and Undo S. Klippert. "Classification and Prognostic Factors of Endodontic-Periodontic Lesions in the Dog." Journal of Veterinary Dentistry 9, no. 2 (June 1992): 27–30. http://dx.doi.org/10.1177/089875649200900203.
SCHLAGINTWEIT, FELIX, and MIKE SIMMONS. "THE ORIGINAL PUBLISHED DESCRIPTION OF AN EMBRYONIC APPARATUS FROM THE ORBITOLINIDAE (FORAMINIFERA) (LOWER CRETACEOUS OF BORNEO) WITH A BRIEF COMMENTARY ON THE AGE OF ORBITOLINID OCCURRENCES IN BORNEO." Acta Palaeontologica Romaniae, no. 19 (2) (May 15, 2023): 21–24. http://dx.doi.org/10.35463/j.apr.2023.02.04.
Yue, Qing. "The Use of Textbook Illustrations in Teaching English Reading in Junior Secondary School." Frontiers in Humanities and Social Sciences 3, no. 4 (April 20, 2023): 17–22. http://dx.doi.org/10.54691/fhss.v3i4.4726.
THAMBUGALA, KASUN M., HIRAN A. ARIYAWANSA, ZUO-YI LIU, EKACHAI CHUKEATIROTE, and KEVIN D. HYDE. "Towards a natural classification of Dothideomycetes 6: The genera Dolabra, Placostromella, Pleosphaerellula, Polysporidiella and Pseudotrichia (Dothideomycetes incertae sedis)." Phytotaxa 176, no. 1 (August 20, 2014): 55. http://dx.doi.org/10.11646/phytotaxa.176.1.8.
Дисертації з теми "Illustrations – classification":
Lefebvre, Grégoire. "Sélection et fusion de signatures visuelles parcimonieuses : application à la classification d'images naturelles." Bordeaux 2, 2007. http://www.theses.fr/2007BOR21463.
This thesis is concerned with automatic classification. The objective is to assign an identity to a test image among a set of known category. The underlying approach aim at extracting a specific set of parsimonious visual signatures, then selecting and melting discriminative information, before designing a classification scheme adapted to the context. Many methods have been proposed in order to describe visual content. One of the most effective is based on points of interest extraction and local singularity description. In the thesis, this principle is used to define next local signature and combination, based on self-organizing neural maps. A novel image information support s then proposed, being the activation of a multimodal neural model. The proposed methods focus on specific elements of one image class versus the other categories. It permits robustness to viewpoint changes, illumination variations and partial occlusions. The proposed techniques are evaluated and compared to usual methods using various international databases. These experiments show the effectiveness of the proposed approaches, in particular, in the domains of image classification, face recognition and objectionable content exclusion
Le, Saux Bertrand Honoré Henri. "Classification non exclusive et personnalisation par apprentissage : application à la navigation dans les bases d'images." Versailles-St Quentin en Yvelines, 2003. http://www.theses.fr/2003VERS0013.
Dans le cadre de la recherche d'images par le contenu, nous nous sommes intéressés aux méthodes de résumé et d'aide à la navigation pour les bases d'images. Nous avons développé une méthode de classification non-exclusive capable de catégoriser l'espace de description des images pour regrouper les images d'apparences visuelles similaires. En définissant une nouvelle fonction de Compétition Agglomérative où la compétition s'adapte à la densité des atégories, l'algorithme ARC (Adaptive Robust Competition) permet de résoudre les difficultés suivantes : * déterminer automatiquement le nombre de classes,* gérer les données bruitées diffuses,* prendre en compte les densités et les formes variables des classes. Dans un deuxième temps, nous permettons à l'utilisateur de contrôler la pertinence des classes obtenues. Un apprentissage basé sur une machine à vecteurs de support permet de personnaliser les classes d'images
Etievent, Emmanuel. "Assistance à l'indexation vidéo par analyse du mouvement." Lyon, INSA, 2002. http://theses.insa-lyon.fr/publication/2002ISAL0015/these.pdf.
Lu, Ying. "Transfer Learning for Image Classification." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC045/document.
When learning a classification model for a new target domain with only a small amount of training samples, brute force application of machine learning algorithms generally leads to over-fitted classifiers with poor generalization skills. On the other hand, collecting a sufficient number of manually labeled training samples may prove very expensive. Transfer Learning methods aim to solve this kind of problems by transferring knowledge from related source domain which has much more data to help classification in the target domain. Depending on different assumptions about target domain and source domain, transfer learning can be further categorized into three categories: Inductive Transfer Learning, Transductive Transfer Learning (Domain Adaptation) and Unsupervised Transfer Learning. We focus on the first one which assumes that the target task and source task are different but related. More specifically, we assume that both target task and source task are classification tasks, while the target categories and source categories are different but related. We propose two different methods to approach this ITL problem. In the first work we propose a new discriminative transfer learning method, namely DTL, combining a series of hypotheses made by both the model learned with target training samples, and the additional models learned with source category samples. Specifically, we use the sparse reconstruction residual as a basic discriminant, and enhance its discriminative power by comparing two residuals from a positive and a negative dictionary. On this basis, we make use of similarities and dissimilarities by choosing both positively correlated and negatively correlated source categories to form additional dictionaries. A new Wilcoxon-Mann-Whitney statistic based cost function is proposed to choose the additional dictionaries with unbalanced training data. Also, two parallel boosting processes are applied to both the positive and negative data distributions to further improve classifier performance. On two different image classification databases, the proposed DTL consistently out performs other state-of-the-art transfer learning methods, while at the same time maintaining very efficient runtime. In the second work we combine the power of Optimal Transport and Deep Neural Networks to tackle the ITL problem. Specifically, we propose a novel method to jointly fine-tune a Deep Neural Network with source data and target data. By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data. Furthermore, by using different kind of metric as cost matrix for the OT loss, JTLN can incorporate different prior knowledge about the relatedness between target categories and source categories. We carried out experiments with JTLN based on Alexnet on image classification datasets and the results verify the effectiveness of the proposed JTLN in comparison with standard consecutive fine-tuning. To the best of our knowledge, the proposed JTLN is the first work to tackle ITL with Deep Neural Networks while incorporating prior knowledge on relatedness between target and source categories. This Joint Transfer Learning with OT loss is general and can also be applied to other kind of Neural Networks
Nettl, Bruno. "Gender (and Other) Identities in Singing Style and Vocal Tone Color. Ethnomusicological Perspectices and Two Brief Illustrations." Bärenreiter Verlag, 2012. https://slub.qucosa.de/id/qucosa%3A71817.
Augereau, Olivier. "Reconnaissance et classification d’images de documents." Thesis, Bordeaux 1, 2013. http://www.theses.fr/2013BOR14764/document.
The aim of this research is to contribute to the document image classification problem. More specifically, these studies address digitizing company issues which objective is to provide the digital version of paper document with information relating to them. Given the diversity of documents, information extraction can be complex. This is why the classification and the indexing of documents are often performed manually. This research provides several solutions based on knowledge of the images that the user has. The first contribution of this thesis is a method for classifying interactively document images, where the content of documents and classes are unknown. The second contribution of this work is a new technique for document image retrieval by giving one example of researched document. This technique is based on the extraction and matching of interest points. The last contribution of this thesis is a method for classifying document images by using bags of visual words techniques
Goh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.
Recent advancements in the areas of deep learning and visual information processing have presented an opportunity to unite both fields. These complementary fields combine to tackle the problem of classifying images into their semantic categories. Deep learning brings learning and representational capabilities to a visual processing model that is adapted for image classification. This thesis addresses problems that lead to the proposal of learning deep visual representations for image classification. The problem of deep learning is tackled on two fronts. The first aspect is the problem of unsupervised learning of latent representations from input data. The main focus is the integration of prior knowledge into the learning of restricted Boltzmann machines (RBM) through regularization. Regularizers are proposed to induce sparsity, selectivity and topographic organization in the coding to improve discrimination and invariance. The second direction introduces the notion of gradually transiting from unsupervised layer-wise learning to supervised deep learning. This is done through the integration of bottom-up information with top-down signals. Two novel implementations supporting this notion are explored. The first method uses top-down regularization to train a deep network of RBMs. The second method combines predictive and reconstructive loss functions to optimize a stack of encoder-decoder networks. The proposed deep learning techniques are applied to tackle the image classification problem. The bag-of-words model is adopted due to its strengths in image modeling through the use of local image descriptors and spatial pooling schemes. Deep learning with spatial aggregation is used to learn a hierarchical visual dictionary for encoding the image descriptors into mid-level representations. This method achieves leading image classification performances for object and scene images. The learned dictionaries are diverse and non-redundant. The speed of inference is also high. From this, a further optimization is performed for the subsequent pooling step. This is done by introducing a differentiable pooling parameterization and applying the error backpropagation algorithm. This thesis represents one of the first attempts to synthesize deep learning and the bag-of-words model. This union results in many challenging research problems, leaving much room for further study in this area
Blot, Michaël. "Étude de l'apprentissage et de la généralisation des réseaux profonds en classification d'images." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS412.
Artificial intelligence is experiencing a resurgence in recent years. This is due to the growing ability to collect and store a considerable amount of digitized data. These huge databases allow machine learning algorithms to respond to certain tasks through supervised learning. Among the digitized data, images remain predominant in the modern environment. Huge datasets have been created. moreover, the image classification has allowed the development of previously neglected models, deep neural networks or deep learning. This family of algorithms demonstrates a great facility to learn perfectly datasets, even very large. Their ability to generalize remains largely misunderstood, but the networks of convolutions are today the undisputed state of the art. From a research and application point of view of deep learning, the demands will be more and more demanding, requiring to make an effort to bring the performances of the neuron networks to the maximum of their capacities. This is the purpose of our research, whose contributions are presented in this thesis. We first looked at the issue of training and considered accelerating it through distributed methods. We then studied the architectures in order to improve them without increasing their complexity. Finally, we particularly study the regularization of network training. We studied a regularization criterion based on information theory that we deployed in two different ways
Vuillerot, Carole. "Métrologie et évaluation fonctionnelle motrice dans les maladies neuromusculaires de l’enfance : Illustrations à partir de la Mesure de Fonction Motrice (MFM) et d’une classification en grades de sévérité d’atteinte fonctionnelle motrice (NM-Score)." Thesis, Lyon 1, 2012. http://www.theses.fr/2012LYO10081/document.
Advances in the research and treatment of childhood neuromuscular diseases have led to longer patient survivals. Evaluation is thus required not only in clinical practice for patient follow-up but also in medical research because the results of long-awaited clinical trials are beginning to emerge. A rigorous and appropriate metrology is then necessary because rough estimates or the use of improper assessment tools are no more satisfactory. We summarize here the current knowledge on the metrology applied to motor function assessment of patients with neuromuscular diseases. We propose a review of the literature on the tools available to monitor motor function with detailed analyses of their metrological properties. Developped since 1998, the Motor Function Measure presents interesting properties in terms of validity and reliability. We analyzed its sensitivity to change in different patient populations of adults and children. We then propose, the NM-Score, a classification in levels of severity of motor function decline.Validation studies have confirmed the interest of this score as well as its ease of use, validity,and reproducibility. The NM-Score is able to describe the patients precisely and discriminantly in terms of motor function for standing position and transfers, axial / proximal motor function and distal motor function. Being interested in evaluation and measurement in medicine is a sign of rigor necessary for decision-making regarding vulnerable persons with special need
Chebbi, Imen. "Modèles de stockage et d’analyse des données massives appliquées à l’imagerie satellitaire." Electronic Thesis or Diss., Paris 8, 2021. http://www.theses.fr/2021PA080106.
Our work forming part of the spatiotemporal remote sensing images, the analysis of the large volume of images is becoming more difficult with the appearance of sensors with very high spatial, spectral and temporal resolutions. In order to be able to situate our thesis in relation to the literature, we studied the main stages of the large volume data pipeline and we focused on two main contributions which are data storage and data processing. Among the objectives of our thesis is to develop a suitable architecture for our system from the perspective of storage and processing. For the implementation of this platform we developed a local master-slave cluster with several machines including one dedicated for the master node and the others for the slave nodes. The first contribution is the idea of a physical storage system that is intelligent and takes into account heterogeneous data. For this, several methods of big data storage and data representation methods based on the hadoop distributed file system (HDFS) and the benefits of Nosql allowing to store, retrieve and query massive data were investigated. We tried to adapt them to our context of satellite images based on our physical architecture and then test them with in-house satellite data collection.The second contribution of our thesis is the processing of massive satellite images after having stored them in order to classify them, where the aim is to develop an approach to classify satellite images by learning the existing truth-labels. We used deep learning techniques and more particularly the adaptation of the Unet and Vggnet algorithms based on the Apache Spark and Tensorflow platform
Книги з теми "Illustrations – classification":
Saiṇī, Malakīata Siṅgha. Indian sawflies biodiversity: Keys, catalogue & illustrations. Dehra Dun: Bishen Singh Mahendra Pal Singh, 2006.
W, Berry Michael, ed. Survey of text mining: Clustering, classification, and retrieval ; with 57 illustrations. New York: Springer, 2004.
Watanabe, Masayuki. Nihon aoko daizukan: The freshwater planktonic blue-greens of Japan with photographs and illustrations / by Masayuki Watanabe. Tōkyō: Seibundō Shinkōsha, 2007.
Lesueur, Justine. Conflits de droits, illustrations dans le champ des propriétés incorporelles. Aix-en-Provence: Presses universitaires d'Aix-Marseille, 2009.
Gergus, Erik W. A. Labs for vertebrate zoology: An evolutionary approach / Erik W.A. Gergus, Gordon W. Schuett ; illustrations by Laura White Schuett. Carmel, IN: Cooper Publishing Group, 1997.
Watson, Leslie. Grass genera of the world: Illustrations of characters, descriptions, classification, interactive identification, information retrieval : with microfiches and floppy disks for MS-DOS microcomputers. Canberra: Australian National University, Research School of Biological Sciences, 1988.
Zomlefer, Wendy B. Flowering plants of Florida: A guide to common families. Gainesville, FL: Biological Illustrations, 1989.
Zomlefer, Wendy B. Guide to flowering plant families. Chapel Hill: University of North Carolina Press, 1994.
Li, Lulu. Tu shuo Zhongguo chuan tong hang ye =: An illustration to Chinese traditional trades. 8th ed. Xi'an: Shi jie tu shu chu ban Xi'an gong si, 2006.
Boelcke, Osvaldo. Plantas vasculares de la Argentina, nativas y exóticas. 2nd ed. Buenos Aires, Argentina: Editorial Hemisferio Sur, 1992.
Частини книг з теми "Illustrations – classification":
Li, Ziyang. "Research on Digital Concept Art Illustrations Style Classification based on Deep Learning." In Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023), 379–88. Dordrecht: Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-370-2_40.
Page, Joanna. "3. Floras, Herbaria, and Botanical Illustration." In Decolonial Ecologies, 93–136. Cambridge, UK: Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0339.03.
Abdullah, A. Sheik. "Data Classification by Decision Trees – An Illustration." In Swarm Intelligence and its Applications in Biomedical Informatics, 38–56. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003330189-3.
Holzer, Stephan, and Oliver Labs. "Illustrating the classification of real cubic surfaces." In Algebraic Geometry and Geometric Modeling, 119–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-33275-6_8.
Ghaly, Mohammed. "Constructing a Comprehensive Discourse." In Islamic Ethics and Incidental Findings, 13–23. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59405-2_2.
Id-youss, Lahousseine, and Abied Alsulaiman. "On the interaction between legal and religious concepts." In Handbook of Terminology, 224–41. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/hot.3.int2.
García, Francisco Sanz, Maite Pelacho, Tim Woods, Dilek Fraisl, Linda See, Mordechai Haklay, and Rosa Arias. "Finding What You Need: A Guide to Citizen Science Guidelines." In The Science of Citizen Science, 419–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58278-4_21.
de Castro Moreira, Gabriel, João Felipe Coimbra Leite Costa, and Diego Machado Marques. "Applying Clustering Techniques and Geostatistics to the Definition of Domains for Modelling." In Springer Proceedings in Earth and Environmental Sciences, 199–219. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_16.
Sadurski, Wojciech. "Standards of Scrutiny, Equal Protection, and Illicit Motives for Discrimination." In Constitutional Public Reason, 242—C8.N90. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869678.003.0008.
Cohen, Robert B., Richard W. Ferguson, and Michael F. Oppenheimer. "The Classification of NTBs: Some Illustrations of Impacts." In Nontariff Barriers to High-Technology Trade, 23–51. Routledge, 2019. http://dx.doi.org/10.4324/9780429038259-4.
Тези доповідей конференцій з теми "Illustrations – classification":
Horvat, Saša A., Tamara N. Rončević, Ivana Z. Bogdanović, and Dušica D. Rodić. "DIFFERENCES IN GRAPHIC ILLUSTRATIONS IN THE CONTENTS OF NATURAL SCIENCES IN REGULAR TEXTBOOKS AND TEXTBOOKS FOR STUDENTS WITH SPECIAL EDUCATIONAL NEEDS IN THE REPUBLIC OF SERBIA." In 5th International Baltic Symposium on Science and Technology Education. Scientia Socialis Press, 2023. http://dx.doi.org/10.33225/balticste/2023.88.
Radulescu, Victorita. "Numerical Modeling and Prediction of the Significant Parameters for Wind Monitoring." In ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/iowtc2019-7518.
Antoine, Axel, Sylvain Malacria, Nicolai Marquardt, and Géry Casiez. "Interaction Illustration Taxonomy: Classification of Styles and Techniques for Visually Representing Interaction Scenarios." In CHI '21: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3411764.3445586.
Lan, Ziwen, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama. "GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9898071.
Zhang, Daokun, and Wenyong Tang. "The application of Structural Reliability Analysis on hull girder ultimate strength of bulk carriers-a trial on Safety Level Approach." In SNAME 5th World Maritime Technology Conference. SNAME, 2015. http://dx.doi.org/10.5957/wmtc-2015-052.
Кутепов, Илья, Ilya Kutepov, Вадим Крысько, Vadim Krysko, Антон Крысько, Anton Krysko, Сергей Павлов, et al. "Complexity of EEG Signals in Schizophrenia Syndromes." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-140-143.
Kolat, Tom. "Thermocouple Testing Methods, Data Analysis and Reporting Calibration Results with Emphasis on Noble Metal Types." In NCSL International Workshop & Symposium. NCSL International, 2018. http://dx.doi.org/10.51843/wsproceedings.2018.26.
Shieh, Win-Bin, Dar-Zen Chen, and Yen-Chun Chen. "Kinematic Synthesis of One-DOF Geared Mechanisms According to Specified Gain Types." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49510.
Sharma, Gehendra, Janet K. Allen, and Farrokh Mistree. "Classification and Execution of Coupled Decision Problems in Engineering Design for Exploration of Robust Design Solutions." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97372.
Zhou, Bin, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye. "BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/616.