Relevant bibliographies by topics / ImageNet Database

Contents

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

Academic literature on the topic 'ImageNet Database'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "ImageNet Database"

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Fei-Fei, L., J. Deng, and K. Li. "ImageNet: Constructing a large-scale image database." Journal of Vision 9, no. 8 (2010): 1037. http://dx.doi.org/10.1167/9.8.1037.

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Huang, Yuming. "Multiple SOTA Convolutional Neural Networks for Facial Expression Recognition." Applied and Computational Engineering 8, no. 1 (2023): 240–45. http://dx.doi.org/10.54254/2755-2721/8/20230135.

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Facial Expression Recognition (FER) has been a popular topic in the field of computer vision. Various and plentiful facial expression datasets emerged every year for people to train their models and compete. ImageNet, as a massive database for image classification, became a standard benchmark for new computer vision models. Many excellent models such as VGG, ResNet, and EfficientNet managed to excel and were regarded as state-of-the-art models (SOTAs). This study aims to investigate whether SOTA models trained on ImageNet can perform exceptionally well in FER tasks. The models are categorized
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Sobti, Priyal, Anand Nayyar, Niharika, and Preeti Nagrath. "EnsemV3X: a novel ensembled deep learning architecture for multi-label scene classification." PeerJ Computer Science 7 (May 25, 2021): e557. http://dx.doi.org/10.7717/peerj-cs.557.

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Convolutional neural network is widely used to perform the task of image classification, including pretraining, followed by fine-tuning whereby features are adapted to perform the target task, on ImageNet. ImageNet is a large database consisting of 15 million images belonging to 22,000 categories. Images collected from the Web are labeled using Amazon Mechanical Turk crowd-sourcing tool by human labelers. ImageNet is useful for transfer learning because of the sheer volume of its dataset and the number of object classes available. Transfer learning using pretrained models is useful because it
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Manoj krishna, M., M. Neelima, M. Harshali, and M. Venu Gopala Rao. "Image classification using Deep learning." International Journal of Engineering & Technology 7, no. 2.7 (2018): 614. http://dx.doi.org/10.14419/ijet.v7i2.7.10892.

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The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.
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Varga, Domonkos. "Multi-Pooled Inception Features for No-Reference Image Quality Assessment." Applied Sciences 10, no. 6 (2020): 2186. http://dx.doi.org/10.3390/app10062186.

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Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run
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T., Tritva Jyothi Kiran. "Deep Transform Learning Vision Accuracy Analysis on GPU using Tensor Flow." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 224–27. https://doi.org/10.35940/ijrte.C4402.099320.

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Transfer learning is one of the most amazing concepts in machine learning and A.I. Transfer learning is completely unsupervised model. Transfer learning is a machine learning technique in which a network that has been trained to perform a specific task is being reused or repurposed as a starting point to perform another similar task. For this work I used ImageNet Dataset and MobileNet model to analyse Accuracy performance of my Deep Transform learning model on GPU of Intel® Core™ i3-7100U CPU using TensorFlow 2.0 Hub and Keras. ImageNet is an open source Large-Scale dataset of images
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Chen, Yao-Mei, Yenming J. Chen, Yun-Kai Tsai, Wen-Hsien Ho, and Jinn-Tsong Tsai. "Classification of human electrocardiograms by multi-layer convolutional neural network and hyperparameter optimization." Journal of Intelligent & Fuzzy Systems 40, no. 4 (2021): 7883–91. http://dx.doi.org/10.3233/jifs-189610.

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A multi-layer convolutional neural network (MCNN) with hyperparameter optimization (HyperMCNN) is proposed for classifying human electrocardiograms (ECGs). For performance tests of the HyperMCNN, ECG recordings for patients with cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) were obtained from three PhysioNet databases: MIT-BIH Arrhythmia Database, BIDMC Congestive Heart Failure Database, and MIT-BIH Normal Sinus Rhythm Database, respectively. The MCNN hyperparameters in convolutional layers included number of filters, filter size, padding, and filter s
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TİRYAKİ, Volkan Müjdat. "Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 1 (2023): 57–65. http://dx.doi.org/10.17798/bitlisfen.1190134.

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The number of breast cancer diagnosis is the biggest among all cancers, but it can be treated if diagnosed early. Mammography is commonly used for detecting abnormalities and diagnosing the breast cancer. Breast cancer screening and diagnosis are still being performed by radiologists. In the last decade, deep learning was successfully applied on big image classification databases such as ImageNet. Deep learning methods for the automated breast cancer diagnosis is under investigation. In this study, breast cancer mass and calcification pathologies are classified by using deep transfer learning
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Krasteva, Vessela, Todor Stoyanov, Stefan Naydenov, Ramun Schmid, and Irena Jekova. "Detection of Atrial Fibrillation in Holter ECG Recordings by ECHOView Images: A Deep Transfer Learning Study." Diagnostics 15, no. 7 (2025): 865. https://doi.org/10.3390/diagnostics15070865.

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Background/Objectives: The timely and accurate detection of atrial fibrillation (AF) is critical from a clinical perspective. Detecting short or transient AF events is challenging in 24–72 h Holter ECG recordings, especially when symptoms are infrequent. This study aims to explore the potential of deep transfer learning with ImageNet deep neural networks (DNNs) to improve the interpretation of short-term ECHOView images for the presence of AF. Methods: Thirty-second ECHOView images, composed of stacked heartbeat amplitudes, were rescaled to fit the input of 18 pretrained ImageNet DNNs with the
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Li, Fuqiang, Tongzhuang Zhang, Yong Liu, and Feiqi Long. "Deep Residual Vector Encoding for Vein Recognition." Electronics 11, no. 20 (2022): 3300. http://dx.doi.org/10.3390/electronics11203300.

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Vein recognition has been drawing more attention recently because it is highly secure and reliable for practical biometric applications. However, underlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. Recent successes of convolutional neural networks (CNNs) for large-scale image recognition tasks motivate us to replace the traditional hand-engineered features with the superior CNN to design a robust and discriminative vein recognition system.
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Dissertations / Theses on the topic "ImageNet Database"

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Trevino, Hector Guillermo 1965. "ImageNet communications and user interface for a distributed color image database." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277925.

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High speed networking technology has evolved tremendously over the past few years. As a result, network applications which require the transfer of large amounts of data over large geographical areas are now possible using fiber optic networks. One of these such applications is the transfer of color image data at the regional, national, and international levels. ImageNet is a distributed color image database system with multiple database nodes and user workstations linked by a communications network. Each database node serves a number of user workstations within a predefined region. The databas
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Zampieri, Carlos Elias Arminio. "Recuperação de imagens multiescala intervalar." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275789.

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Orientador: Jorge Stolfi<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-16T21:27:57Z (GMT). No. of bitstreams: 1 Zampieri_CarlosEliasArminio_M.pdf: 4003666 bytes, checksum: a730c8935e9f68bc9c1cd9a6e9d68c8c (MD5) Previous issue date: 2010<br>Resumo: Neste trabalho apresentamos um método geral para busca de imagem por conteúdo (BIPC, CBIR) em grandes coleções de imagens, usando estimação intervalar multiescala de distância. Consideramos especificamente buscas por exemplo, em que o objetivo é encontrar a imagem da co
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Bergamasco, Leila Cristina Carneiro. "Recuperação de imagens cardiacas tridimensionais por conteúdo." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-23092013-152421/.

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Os modelos tridimensionais fornecem uma visão mais completa dos objetos analisados por considerarem a profundidade de cada um deles. Com o crescimento de modelos tridimensionais disponíveis atualmente na área de saúde, se faz necessária a implementação de mecanismos eficientes de busca, que ofereçam formas alternativas para localizar casos de pacientes com determinadas características. A disponibilização de um histórico de imagens similares em relação àquelas pertencentes ao exame do paciente pode auxiliar no diagnóstico oferecendo casos semelhantes. O presente projeto visou desenvolver técnic
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Santos, Marcelo dos. "Ambiente para avaliação de algoritmos de processamento de imagens médicas." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-19042007-165507/.

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Constantemente, uma variedade de novos métodos de processamento de imagens é apresentada à comunidade. Porém poucos têm provado sua utilidade na rotina clínica. A análise e comparação de diferentes abordagens por meio de uma mesma metodologia são essenciais para a qualificação do projeto de um algoritmo. Porém, é difícil comparar o desempenho e adequabilidade de diferentes algoritmos de uma mesma maneira. A principal razão deve-se à dificuldade para avaliar exaustivamente um software, ou pelo menos, testá-lo num conjunto abrangente e diversificado de casos clínicos. Muitas áreas - como o desen
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Fedel, Gabriel de Souza. "Busca multimodal para apoio à pesquisa em biodiversidade." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275751.

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Orientador: Cláudia Maria Bauzer Medeiros<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-18T07:07:49Z (GMT). No. of bitstreams: 1 Fedel_GabrieldeSouza_M.pdf: 14390093 bytes, checksum: 63058da33a22121e927f1cdbaff297d3 (MD5) Previous issue date: 2011<br>Resumo: A pesquisa em computação aplicada à biodiversidade apresenta muitos desafios, que vão desde o grande volume de dados altamente heterogêneos até a variedade de tipos de usuários. Isto gera a necessidade de ferramentas versáteis de recuperação. As ferramentas d
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Almeida, Junior Jurandy Gomes de 1983. "Recuperação de imagens por cor utilizando analise de distribuição discreta de caracteristicas." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276206.

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Orientadores: Siome Klein Goldenstein, Ricardo da Silva Torres<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-09T20:33:35Z (GMT). No. of bitstreams: 1 AlmeidaJunior_JurandyGomesde_M.pdf: 4495355 bytes, checksum: 23f3f269bbf0d0e9336b8f3d53677c93 (MD5) Previous issue date: 2007<br>Resumo: A evolução das tecnologias de aquisição, transmissão e armazenamento de imagens tem permitido a construção dc bancos dc imagens cada vez maiores. À medida em que cresce o volume de imagens nessas coleções, cresce também o intcresse
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Souza, Gabriel Gustavo Barros de [UNESP]. "Proposta de atualização de cadastro urbano a partir de detecção de alterações em imagens QUICK BIRD tomadas em diferentes épocas." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/86780.

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Made available in DSpace on 2014-06-11T19:22:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-06-19Bitstream added on 2014-06-13T18:49:02Z : No. of bitstreams: 1 souza_ggb_me_prud.pdf: 3569562 bytes, checksum: 1967b82d0ff7aaed4984bec889309e19 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>A atualização cadastral de área urbana é uma das questões mais importantes a ser considerada no planejamento municipal. Por esta área tratar de uma riqueza de detalhes acentuada, quando comparada as área rurais e de expansão urbana, torna-se difícil traçar uma metod
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Rodrigues, Silvia Cristina Martini. "Organização automática de bancos de mamografias no padrão de densidade BI-RADS." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-11112015-152323/.

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Este trabalho apresenta um método computacional que classifica as mamografias no padrão de densidade BI-RADS, visando auxiliar a detecção precoce do câncer de mama, seja essa realizada por análise visual ou por auxílio computadorizado. A classificação das mamografias em bancos padronizados objetiva eliminar conflitos entre laudos mamográficos de diferentes profissionais, bem como quanto à conduta médica a ser seguida. Entretanto, o estabelecimento de bancos feito visualmente e principalmente em períodos diferentes dificulta sua uniformização, proporcionando uma classificação muito subjetiva e
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Matheus, Bruno Roberto Nepomuceno. "BancoWeb: base de imagens mamográficas para auxílio em avaliações de esquemas CAD." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-24062010-155737/.

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Este trabalho teve como objetivo desenvolver uma base de imagens mamográficas online com acesso público para desenvolvimento, testes e avaliação comparativa de esquemas computadorizados de auxilio ao diagnóstico (CADs). A base contem imagens de vários hospitais, com grande variedade de laudos, também disponíveis na base assim como informações sobre dados clínicos (não confidenciais) dos pacientes. Uma interface detalhada foi criada para permitir o fácil acesso público, permitindo o uso de ferramentas de busca, recorte, analise estatística e inserção remota de imagens, entre outras. Testes comp
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Souza, Luiz Eduardo Christovam de. "Organização e armazenamento de imagens multitemporais georreferenciadas para suporte ao processo de detecção de mudanças /." Presidente Prudente, 2018. http://hdl.handle.net/11449/180729.

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Orientador: Maria de Lourdes Bueno Trindade Galo<br>Resumo: Atualmente o volume de dados produzidos tem atingido patamares nunca imaginados, sobretudo em decorrência da multiplicação do número de sensores e da popularização da internet, com a web 2.0 e as redes sociais. Dentre os diversos tipos de sensores existentes, os de imageamento, transportados principalmente por satélites, produzem vastos conjuntos de observações da superfície da Terra. A observação contínua da Terra por satélites possibilita o monitoramento de mudanças no uso e cobertura da terra. Contudo, em diversas pesquisas relacio
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Books on the topic "ImageNet Database"

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Trevor, Paglen, and Barbican Art Gallery, eds. Trevor Paglen: From 'Apple' to 'Anomaly' : selections from the ImageNet database for object recognition. Barbican, 2019.

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Deb, Sagarmay. Multimedia Systems and Content-Based Image Retrieval. Information Science Publishing, 2003.

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Book chapters on the topic "ImageNet Database"

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He, Biao, Dongming Zhang, and Zili Li. "Tunnel ImageNet: A comprehensive annotated image database of tunnel defects for structural condition maintenance." In Tunnelling into a Sustainable Future – Methods and Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559047-532.

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Alaeddine, Hmidi, and Malek Jihene. "A Comparative Study of Popular CNN Topologies Used for Imagenet Classification." In Deep Neural Networks for Multimodal Imaging and Biomedical Applications. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3591-2.ch007.

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Deep Learning is a relatively modern area that is a very important key in various fields such as computer vision with a trend of rapid exponential growth so that data are increasing. Since the introduction of AlexNet, the evolution of image analysis, recognition, and classification have become increasingly rapid and capable of replacing conventional algorithms used in vision tasks. This study focuses on the evolution (depth, width, multiple paths) presented in deep CNN architectures that are trained on the ImageNET database. In addition, an analysis of different characteristics of existing top
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Dandotiya, Monika, and Madhukar Dubey. "A VGG-16 Framework for an Efficient Indoor-Outdoor." In SCRS CONFERENCE PROCEEDINGS ON INTELLIGENT SYSTEMS. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-93-91842-08-6-32.

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Computer vision had reached a new level that allows robots from the limits of laboratories to explore the outside world. Even with progress in this area, robots are struggling to understand their location. The classification of the scene is an important step in understanding the scene. In many applications, a scene classifi- cation can be used such as a surveillance camera, self-driving, a household robot, and a database imaging system. Monitoring cameras are now everywhere installed. The accuracy of scene classification of indoor-outdoor techniques is weak. Using the Convolution Neural Net-wo
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Conference papers on the topic "ImageNet Database"

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Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "ImageNet: A large-scale hierarchical image database." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206848.

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Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "ImageNet: A large-scale hierarchical image database." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206848.

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Souza, Victor, Luan Silva, Adam Santos, and Leandro Araújo. "Análise Comparativa de Redes Neurais Convolucionais no Reconhecimento de Cenas." In Computer on the Beach. Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p419-426.

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This paper aims to compare the convolutional neural networks(CNNs): ResNet50, InceptionV3, and InceptionResNetV2 tested withand without pre-trained weights on the ImageNet database in orderto solve the scene recognition problem. The results showed that thepre-trained ResNet50 achieved the best performance with an averageaccuracy of 99.82% in training and 85.53% in the test, while theworst result was attributed to the ResNet50 without pre-training,with 88.76% and 71.66% of average accuracy in training and testing,respectively. The main contribution of this work is the direct comparisonbetween t
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Barcellos, William, and Adilson Gonzaga. "Periocular authentication in smartphones applying uLBP descriptor on CNN Feature Maps." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18890.

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The outputs of CNN layers, called Activations, are composed of Feature Maps, which show textural information that can be extracted by a texture descriptor. Standard CNN feature extraction use Activations as feature vectors for object recognition. The goal of this work is to evaluate a new methodology of CNN feature extraction. In this paper, instead of using the Activations as a feature vector, we use a CNN as a feature extractor, and then we apply a texture descriptor directly on the Feature Maps. Thus, we use the extracted features obtained by the texture descriptor as a feature vector for a
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Albuquerque, Amanda Cristina Fraga de, and Helyane Bronoski Borges. "Evaluation of Deep Learning Transfer Techniques for Mangrove Segmentation with Images of the Sentinel-2A." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/sibgrapi.est.2024.31659.

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Fine-tuning techniques allow the use of weights from pre-trained networks in other models across different contexts, potentially improving training performance as it generally requires fewer computational resources and less data. Finetuning has become more widespread in the natural domain (RGB) with the availability of pre-trained model weights from the ImageNet database. However, pre-trained models in the same domain are not readily available for the remote sensing domain, such as in mangrove identification. Both nationally and in the state of Paraná, there are few studies employing deep lear
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Mikhalevich, Yurij. "CLIP-Based Search Engine for Retrieval of Label-Free Images Using a Text Query." In 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004021.

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In January 2021, OpenAI released the Contrastive Language-Image Pre-Training (CLIP) model, able to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the Internet. This model enables researchers to use natural language to reference learned visual concepts (or describe new ones), enabling the zero-shot transfer of the model to downstream tasks. One of the possible applications of CLIP is to look up images using natural language queries. This application is especially important in the context of the constantly growing amount of visual inf
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Almondes, Camila Catiely de Sá, and Flávio Henrique Duarte de Araújo. "Análise da Segmentação e Extração de Características na Detecção de COVID-19 em imagens de Raio-x de Tórax." In Encontro Unificado de Computação do Piauí. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/enucompi.2021.17747.

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O COVID-19 afeta principalmente os pulmões, causando falta de ar, tosse e até falência de múltiplos órgãos, deixando as pessoas gravemente doentes. A radiografia de tórax se torna necessária para testes e para avaliar o pulmão e o progresso do vírus quanto aos seus efeitos. Este trabalho apresenta a avaliação dos descritores Dense Net201, VGG16, RESNET50 e Xception, e os classificadores Multi-layer Perceptron (MLP) e Random Forest (RF), com a utilização da base COVID-19 chest x-ray database para o diagnóstico do COVID-19. Para avaliar a segmentação foi utilizada a base Tuberculosis (TB) Chest
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Costa, Leonardo, Caio Menezes, Antony Santos, Matheus Araújo, and Gustavo Campos. "A comparative study involving classifiers and dimensionality reduction techniques applied to facial recognition." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9338.

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Neste trabalho é apresentado um estudo comparativo entre as técnicas Eigenfaces e Fisherfaces combinadas com os classificadores KNN, SVM e MLP. As técnicas Eigenfaces e Fisherfaces foram utilizadas para projeção das imagens dos bancos de imagens AT\&amp;T (The database of faces) e Extended Yale } em um novo espaço de forma a se obter uma redução da dimensionalidade desses dados. Os classificadores mencionados utilizaram os dados projetados para executar a tarefa de treinamento e posterior identificação das classes dos dados de teste. Os resultados foram bastante promissores em ambos os casos,
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Zhuo, Li'an, Baochang Zhang, Hanlin Chen, et al. "CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/144.

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Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architectures, which are still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binarized weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework. To this end, a Child-Parent model is introduced to a dif
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Pereira, Fernando Roberto, and Lucas Ferrari De Oliveira. "Proposta de uma solução computacional para detecção de nódulos pulmonares." In XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcas.2018.3672.

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O câncer ainda é um dos principais motivos de óbitos em todo o mundo. Só o câncer de pulmão causou mais de 1 milhão de mortes recentemente. A detecção precoce aumenta a probabilidade de cura, tentando auxiliar o diagnóstico é apresentada uma proposta de solução computacional para detecção de nódulos pulmonares em imagens de Tomografia Computadorizada de tórax. A base de dados de imagens utilizada foi Lung Imaging Database Consortium. A solução proposta utiliza a segmentação da área pulmonar, segmentação e rotulação de objetos candidatos a nódulos pulmonares, extração de características emprega
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