Relevant bibliographies by topics / Neural networks; Visual information

Contents

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

Academic literature on the topic 'Neural networks; Visual information'

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

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Journal articles on the topic "Neural networks; Visual information"

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Hertz, J. A., T. W. Kjær, E. N. Eskandar, and B. J. Richmond. "MEASURING NATURAL NEURAL PROCESSING WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Neural Systems 03, supp01 (January 1992): 91–103. http://dx.doi.org/10.1142/s0129065792000425.

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We show how to use artificial neural networks as a quantitative tool in studying real neuronal processing in the monkey visual system. Training a network to classify neuronal signals according to the stimulus that elicited them permits us to calculate the information transmitted by these signals. We illustrate this for neurons in the primary visual cortex with measurements of the information transmitted about visual stimuli and for cells in inferior temporal cortex with measurements of information about behavioral context. For the latter neurons we also illustrate how artificial neural networks can be used to model the computation they do.
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Kawato, Mitsuo, Takatoshi Ikeda, and Sei Miyake. "Learning in neural networks for visual information processing." Journal of the Institute of Television Engineers of Japan 42, no. 9 (1988): 918–24. http://dx.doi.org/10.3169/itej1978.42.918.

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Seeland, Marco, and Patrick Mäder. "Multi-view classification with convolutional neural networks." PLOS ONE 16, no. 1 (January 12, 2021): e0245230. http://dx.doi.org/10.1371/journal.pone.0245230.

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Humans’ decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object’s class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision. We propose a classification scheme that relies on fusing visual information captured through images depicting the same object from multiple perspectives. Convolutional neural networks are used to extract and encode visual features from the multiple views and we propose strategies for fusing these information. More specifically, we investigate the following three strategies: (1) fusing convolutional feature maps at differing network depths; (2) fusion of bottleneck latent representations prior to classification; and (3) score fusion. We systematically evaluate these strategies on three datasets from different domains. Our findings emphasize the benefit of integrating information fusion into the network rather than performing it by post-processing of classification scores. Furthermore, we demonstrate through a case study that already trained networks can be easily extended by the best fusion strategy, outperforming other approaches by large margin.
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MAINZER, KLAUS. "CELLULAR NEURAL NETWORKS AND VISUAL COMPUTING." International Journal of Bifurcation and Chaos 13, no. 01 (January 2003): 1–6. http://dx.doi.org/10.1142/s0218127403006534.

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Brain-like information processing has become a challenge to modern computer science and chip technology. The CNN (Cellular Neural Network) Universal Chip is the first fully programmable industrial-sized brain-like stored-program dynamic array computer which dates back to an invention of Leon O. Chua and Lin Yang in Berkeley in 1988. Since then, many papers have been written on the mathematical foundations and technical applications of CNN chips. They are already used to model artificial, physical, chemical, as well as living biological systems. CNN is now a new computing paradigm of interdisciplinary interest. In this state of development a textbook is needed in order to recruit new generations of students and researchers from different fields of research. Thus, Chua's and Roska's textbook is a timely and historic publication. On the background of their teaching experience, they have aimed at undergraduate students from engineering, physics, chemistry, as well as biology departments. But, actually, it offers more. It is a brilliant introduction to the foundations and applications of CNN which is distinguished with conceptual and mathematical precision as well as with detailed explanations of applications in visual computing.
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Hartono, Pitoyo. "A transparent cancer classifier." Health Informatics Journal 26, no. 1 (December 31, 2018): 190–204. http://dx.doi.org/10.1177/1460458218817800.

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Recently, many neural network models have been successfully applied for histopathological analysis, including for cancer classifications. While some of them reach human–expert level accuracy in classifying cancers, most of them have to be treated as black box, in which they do not offer explanation on how they arrived at their decisions. This lack of transparency may hinder the further applications of neural networks in realistic clinical settings where not only decision but also explainability is important. This study proposes a transparent neural network that complements its classification decisions with visual information about the given problem. The auxiliary visual information allows the user to some extent understand how the neural network arrives at its decision. The transparency potentially increases the usability of neural networks in realistic histopathological analysis. In the experiment, the accuracy of the proposed neural network is compared against some existing classifiers, and the visual information is compared against some dimensional reduction methods.
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Et. al., K. P. Moholkar,. "Visual Question Answering using Convolutional Neural Networks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 170–75. http://dx.doi.org/10.17762/turcomat.v12i1s.1602.

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The ability of a computer system to be able to understand surroundings and elements and to think like a human being to process the information has always been the major point of focus in the field of Computer Science. One of the ways to achieve this artificial intelligence is Visual Question Answering. Visual Question Answering (VQA) is a trained system which can answer the questions associated to a given image in Natural Language. VQA is a generalized system which can be used in any image-based scenario with adequate training on the relevant data. This is achieved with the help of Neural Networks, particularly Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In this study, we have compared different approaches of VQA, out of which we are exploring CNN based model. With the continued progress in the field of Computer Vision and Question answering system, Visual Question Answering is becoming the essential system which can handle multiple scenarios with their respective data.
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Deng, Yu Qiao, and Ge Song. "A Verifiable Visual Cryptography Scheme Using Neural Networks." Advanced Materials Research 756-759 (September 2013): 1361–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1361.

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This paper proposes a new verifiable visual cryptography scheme for general access structures using pi-sigma neural networks (VVCSPSN), which is based on probabilistic signature scheme (PSS), which is considered as security and effective verification method. Compared to other high-order networks, PSN has a highly regular structure, needs a much smaller number of weights and less training time. Using PSNs capability of large-scale parallel classification, VCSPSN reduces the information communication rate greatly, makes best known upper bound polynomial, and distinguishes the deferent information in secret image.
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Merilaita, Sami. "Artificial neural networks and the study of evolution of prey coloration." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1479 (January 11, 2007): 421–30. http://dx.doi.org/10.1098/rstb.2006.1969.

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In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolution of prey coloration. Therefore, the capability to process patterns as well as to imitate aspects of predator's information processing and responses to visual information makes neural networks a well-suited modelling approach for the study of prey coloration. In addition, their suitability for evolutionary simulations is an advantage when complex or dynamic interactions are modelled. Since not all behaviours of neural network models are necessarily biologically relevant, it is important to validate a neural network model with empirical data. Bringing together knowledge about neural networks with knowledge about topics of prey coloration would provide a potential way to deepen our understanding of the specific appearances of prey coloration.
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Wolfrum, Philipp, and Christoph von der Malsburg. "What Is the Optimal Architecture for Visual Information Routing?" Neural Computation 19, no. 12 (December 2007): 3293–309. http://dx.doi.org/10.1162/neco.2007.19.12.3293.

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Analyzing the design of networks for visual information routing is an underconstrained problem due to insufficient anatomical and physiological data. We propose here optimality criteria for the design of routing networks. For a very general architecture, we derive the number of routing layers and the fanout that minimize the required neural circuitry. The optimal fanout l is independent of network size, while the number k of layers scales logarithmically (with a prefactor below 1), with the number n of visual resolution units to be routed independently. The results are found to agree with data of the primate visual system.
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Medvedev, Viktor, Gintautas Dzemyda, Olga Kurasova, and Virginijus Marcinkevičius. "Efficient Data Projection for Visual Analysis of Large Data Sets Using Neural Networks." Informatica 22, no. 4 (January 1, 2011): 507–20. http://dx.doi.org/10.15388/informatica.2011.339.

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Dissertations / Theses on the topic "Neural networks; Visual information"

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Song, Yue. "Towards Multi-Scale Visual Explainability for Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281359.

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Explainability methods seek to find out visual explanations for neural network decisions. Existing techniques mainly fall into two categories: backpropagation- based methods and occlusion-based methods. The former category selectively highlights the computed gradients, while the latter occludes the input to maximally confuse the classifier and visualize the distinct regions. Motivated by the occlusion methods, we propose an explainability model which to our knowledge is the first attempt to extract multi-scale explanations by perturbing the intermediate representations. Furthermore, we present two vi- sualization techniques that can fuse the multi-scale explanations into a single image and suggest an general evaluation metric to assess the explanation’s quality. Both qualitative and quantitative experimental results on several kinds of datasets demonstrate the efficacy of our model.
Förklarbarhetsmetoder försöker ta reda på visuella förklaringar till beslut om neurala nätverk. Befintliga tekniker faller huvudsakligen i två kategorier: backpropagationsbaserade metoder och okklusionsbaserade metoder. Den förra kategorin belyser selektivt de beräknade gradienterna, medan den senare slår in ingången för att maximera förvirra klassificera och visualisera de distinkta regionerna. Motiverade av ocklusionsmetoderna föreslår vi en förklarbarhetsmodell som enligt vår kunskap är det första försöket att extrahera flerskaliga förklaringar genom att störa de mellanliggande representationerna. Vidare presenterar vi två visualiseringstekniker som kan smälta multi -skala förklaringar till en enda bild och föreslå en utvärderingsmetrik för att bedöma förklaringens kvalitet. Både kvalitativa och kvantitativa experimentella resultat på flera typer av datasätt visar effektiviteten hos vår modell.
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Newman, Rhys A. "Automatic learning in computer vision." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390526.

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Mayer, Nikolaus [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Synthetic training data for deep neural networks on visual correspondence tasks." Freiburg : Universität, 2020. http://d-nb.info/1216826692/34.

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Yavari, Najib. "Few-Shot Learning with Deep Neural Networks for Visual Quality Control: Evaluations on a Production Line." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283119.

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Having a well representative and adequate amount of data samples plays an important role in the success of deep learning algorithms used for image recognition. On the other hand, collecting and manually labeling a large-scale dataset requires a great deal of human interaction which in turn is very timeconsuming. In this thesis project, we explore the possibilities of new deeplearning approaches used for image recognition that do not require a big amount of data. Since Few-Shot Learning (FSL) models are known to be the most promising approach to tackle the problem of not having an adequate dataset, a hand full of the state-of-the-art algorithms based on FSL approach such as Model-Agnostic Meta-Learning (MAML), Prototypical Networks (ProtoNet), Relation Networks (RelationNet), Baseline, and Baseline++ are implemented and analyzed. These models are used to classify a series of issues for the automation of the visual quality inspection in a production line. Moreover, the performance of the deeper networks in comparison to the shallower networks is explored. Our experiment results on the available dataset show that the Baseline++ model has the best performance among the other models. Furthermore, Baseline++ with a six-layer convolutional network as a feature backbone is a relatively simple model to train that does not require a high computational power compared to the other models.
Inom maskininlärning spelar tillgången till en bra och lämplig mängd data en viktig roll i framgången för djupa inlärningsalgoritmer som används för bildigenkänning. Insamling och manuell märkning av ett storskaligt dataset kräver däremot en hel del mänsklig interaktion som är mycket tidskrävande. I detta examensarbete undersöker vi möjligheterna med nya djupinlärningsmetoder som används för bildigenkänning som inte kräver ett storskaligt dataset. Eftersom Few-Shot Learning (FSL) modeller är kända för att vara den mest lovande metoden för att hantera problemet med att inte ha ett tillräckligt dataset, implementerar och analyserar vi några av de senaste modellerna baserad på FSL, såsom: Model-Agnostic Meta-Learning (MAML), PrototypicalNetworks (ProtoNet), Relation Networks (RelationNet), Baseline, och Baseline++. Dessa modeller används för att klassificera en rad olika defekta produkter för automatisering av den visuella kvalitetskontrollen i en produktionslinje. Vidare undersöks även de djupare nätverkens prestanda i jämförelse med de grundare nätverken. Experimentresultaten på det tillgängliga datasetet visar att Baseline++ modellen har bäst prestanda bland de olika modellerna. Dessutom är Baseline++ med ett sex-lagers faltningsnätverk, en relativt enkel modell att träna som inte kräver en hög beräkningskraft jämfört med de andra modellerna.
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Aboudib, Ala. "Neuro-inspired Architectures for the Acquisition and Processing of Visual Information." Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0419/document.

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L'apprentissage automatique et la vision par ordinateur sont deux sujets de recherche d'actualité. Des contributions clés à ces domaines ont été les fruits de longues années d'études du cortex visuel et de la fonction des réseaux cérébraux. Dans cette thèse, nous nous intéressons à la conception des architectures neuro-inspirées pour le traitement de l'information sur trois niveaux différents du cortex visuel. Au niveau le plus bas, nous proposons un réseau de neurones pour l'acquisition des signaux visuels. Ce modèle est étroitement inspiré par le fonctionnement et l'architecture de la retine et les premières couches du cortex visuel chez l'humain. Il est également adapté à l'émulation des mouvements oculaires qui jouent un rôle important dans notre vision. Au niveau le plus haut, nous nous intéressons à la mémoire. Nous traitons un modèle de mémoire associative basée sur une architecture neuro-inspirée dite `Sparse Clustered Network (SCN)'. Notre contribution principale à ce niveau est de proposer une amélioration d'un algorithme utilisé pour la récupération des messages partiellement effacés du SCN. Nous suggérons également une formulation générique pour faciliter l'évaluation des algorithmes de récupération, et pour aider au développement des nouveaux algorithmes. Au niveau intermédiaire, nous étendons l'architecture du SCN pour l'adapter au problème de la mise en correspondance des caractéristiques d'images, un problème fondamental en vision par ordinateur. Nous démontrons que la performance de notre réseau atteint l'état de l'art, et offre de nombreuses perspectives sur la façon dont les architectures neuro-inspirées peuvent servir de substrat pour la mise en oeuvre de diverses tâches de vision
Computer vision and machine learning are two hot research topics that have witnessed major breakthroughs in recent years. Much of the advances in these domains have been the fruits of many years of research on the visual cortex and brain function. In this thesis, we focus on designing neuro-inspired architectures for processing information along three different stages of the visual cortex. At the lowest stage, we propose a neural model for the acquisition of visual signals. This model is adapted to emulating eye movements and is closely inspired by the function and the architecture of the retina and early layers of the ventral stream. On the highest stage, we address the memory problem. We focus on an existing neuro-inspired associative memory model called the Sparse Clustered Network. We propose a new information retrieval algorithm that offers more flexibility and a better performance over existing ones. Furthermore, we suggest a generic formulation within which all existing retrieval algorithms can fit. It can also be used to guide the design of new retrieval approaches in a modular fashion. On the intermediate stage, we propose a new way for dealing with the image feature correspondence problem using a neural network model. This model deploys the structure of Sparse Clustered Networks, and offers a gain in matching performance over state-of-the-art, and provides a useful insight on how neuro-inspired architectures can serve as a substrate for implementing various vision tasks
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Ajamlou, Kevin, and Max Sonebäck. "Multimodal Convolutional Graph Neural Networks for Information Extraction from Visually Rich Documents." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445457.

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Monotonous and repetitive tasks consume a lot of time and resources in businesses today and the incentive to fully or partially automate said tasks, in order to relieve office workers and increase productivity in the industry, is therefore high. One such task is to process and extract information from Visually Rich Documents (VRD:s), e.g., documents where the visual attributes contain important information about the contents of the document. A lot of recent studies have focused on information extraction from invoices, where graph based convolutional nerual networks have shown a lot of promise for extracting relevant entities. By modelling the invoice as a graph, the text of the invoice can be modelled as nodes and the topological relationship between nodes, i.e., the visual representation of the document, can be preserved by connecting the nodes through edges. The idea is then to propagate the features of neighboring nodes to each other in order to find meaningful patterns for distinct entities in the document, based on both the features of the node itself as well as the features of its neighbors.   This master thesis aims to investigate, analyze and compare the performances of state-of-the-art multimodal graph based convolutional neural networks, as well as evaluate how well the models generalize across unseen invoice templates. Three models, with two different model architecture designs, have been trained with either underlying ChebNet or GCN convolutional layers. Two of these models have been re-trained, and compared to their predecessors, using the over-smoothing combatting technique DropEdge. All models have been tested on two datasets - one containing both seen and unseen templates and a subset of the previous dataset, containing only invoices with unseen templates.  The results show that multimodal graph based convolutional neural networks are a viable option for information extraction from invoices and that the models built in this thesis show great potential to generalize across unseen invoice templates. Moreover, due to an inherent sparse nature of graphs modeled from invoices, DropEdge does not yield an overall better performance for the models.
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Michler, Frank [Verfasser], and Thomas [Akademischer Betreuer] Wachtler. "Self-Organization of Spiking Neural Networks for Visual Object Recognition / Frank Michler ; Betreuer: Thomas Wachtler." Marburg : Philipps-Universität Marburg, 2020. http://d-nb.info/1204199876/34.

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Dercksen, Vincent Jasper [Verfasser]. "Visual computing techniques for the reconstruction and analysis of anatomically realistic neural networks / Vincent Jasper Dercksen." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1081935391/34.

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Tong, Song. "Informatics Approaches for Understanding Human Facial Attractiveness Perception and Visual Attention." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/264679.

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京都大学
新制・課程博士
博士(情報学)
甲第23398号
情博第767号
新制||情||131(附属図書館)
京都大学大学院情報学研究科知能情報学専攻
(主査)教授 熊田 孝恒, 教授 西田 眞也, 教授 齋木 潤, 准教授 延原 章平
学位規則第4条第1項該当
Doctor of Informatics
Kyoto University
DFAM
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Salem, Tawfiq. "Learning to Map the Visual and Auditory World." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/86.

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The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabilistic models and a weakly-supervised, multi-task training strategy to provide an estimate of the expected visual and auditory ground-level attributes consisting of the type of scenes, objects, and sounds a person can experience at a location. Through a large-scale evaluation on real data, we show that our learned models can be used for applications including mapping, image localization, image retrieval, and metadata verification.
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Books on the topic "Neural networks; Visual information"

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Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.

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Venkatesan, Ragav, and Baoxin Li. Convolutional Neural Networks in Visual Computing. Boca Raton ; London : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.4324/9781315154282.

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Rosandich, Ryan G. Intelligent visual inspection: Using artificial neural networks. London: Chapman & Hall, 1997.

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Rosandich, Ryan G. Intelligent Visual Inspection: Using artificial neural networks. Boston, MA: Springer US, 1996.

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Zhang, Xiang-Sun. Neural Networks in Optimization. Boston, MA: Springer US, 2000.

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Information theoretic neural computation. New Jersey: World Scientific, 2002.

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Govindaraju, R. S. Artificial Neural Networks in Hydrology. Dordrecht: Springer Netherlands, 2000.

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Kaynak, Okyay, Ethem Alpaydin, Erkki Oja, and Lei Xu, eds. Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2.

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T, Roska, ed. Cellular neural networks and visual computing: Foundation and applications. Cambridge: Cambridge University Press, 2002.

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T. V. S. M. olde Scheper. Chaos and information in dynamic neural networks. Oxford: Oxford Brookes University, 2002.

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Book chapters on the topic "Neural networks; Visual information"

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Yu, Ying, Bin Wang, and Liming Zhang. "Hebbian-Based Neural Networks for Bottom-Up Visual Attention Systems." In Neural Information Processing, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_1.

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Turcsany, Diana, and Andrzej Bargiela. "Learning Local Receptive Fields in Deep Belief Networks for Visual Feature Detection." In Neural Information Processing, 462–70. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_58.

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Chen, Yanyin, Xing Chen, Huibin Tan, Xiang Zhang, Long Lan, Xuhui Huang, and Zhigang Luo. "Cross-Layer Convolutional Siamese Network for Visual Tracking." In Neural Information Processing, 146–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04179-3_13.

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Lu, H. B., and Y. J. Zhang. "Detecting Abrupt Scene Change Using Neural Network." In Visual Information and Information Systems, 291–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48762-x_37.

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Yuan, Zejian, Lei Yang, Yanyun Qu, Yuehu Liu, and Xinchun Jia. "A Boosting SVM Chain Learning for Visual Information Retrieval." In Advances in Neural Networks - ISNN 2006, 1063–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_156.

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Mallot, Hanspeter A., and Werner Von Seelen. "Why Cortices? Neural Networks for Visual Information Processing." In Visuomotor Coordination, 357–82. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-0897-1_11.

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Ning, Xiaodong, and Lixiong Liu. "Level Set Based Online Visual Tracking via Convolutional Neural Network." In Neural Information Processing, 280–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70090-8_29.

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Sjöberg, Mats, Jorma Laaksonen, Matti Pöllä, and Timo Honkela. "Retrieval of Multimedia Objects by Combining Semantic Information from Visual and Textual Descriptors." In Artificial Neural Networks – ICANN 2006, 75–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_8.

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Zhang, Jinjian, and Xiaodong Gu. "Desert Vehicle Detection Based on Adaptive Visual Attention and Neural Network." In Neural Information Processing, 376–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_47.

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Li, Jie, and Yue Zhou. "Visual Saliency Based Blind Image Quality Assessment via Convolutional Neural Network." In Neural Information Processing, 550–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70136-3_58.

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Conference papers on the topic "Neural networks; Visual information"

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Canziani, Alfredo, and Eugenio Culurciello. "Visual attention with deep neural networks." In 2015 49th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2015. http://dx.doi.org/10.1109/ciss.2015.7086900.

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Xiao, Youping, Ravi Rao, Guilermo Cecchi, and Ehud Kaplan. "Cortical representation of information about visual attributes: one network or many?" In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371228.

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Koprinkova-Hristova, Petia, Simona Nedelcheva, Nadejda Bocheva, Radolsava Kraleva, Velin Kralev, Miroslava Stefanova, and Bilyana Genova. "STDP Training of Hierarchical Spike Timing Model of Visual Information Processing." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207598.

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Belkaid, Marwen, Nicolas Cuperlier, and Philippe Gaussier. "Combining local and global visual information in context-based neurorobotic navigation." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727851.

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Rassadin, Alexandr G., and Andrey V. Savchenkov. "Compressing deep convolutional neural networks in visual emotion recognition." In Information Technology and Nanotechnology 2017. Samara University, 2017. http://dx.doi.org/10.18287/1613-0073-2017-1901-207-213.

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Song Ge, Peng Changgen, and Miao Xuelan. "Visual Cryptography Scheme Using Pi-sigma Neural Networks." In 2008 International Symposium on Information Science and Engineering (ISISE). IEEE, 2008. http://dx.doi.org/10.1109/isise.2008.208.

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Deng, Yuqiao, and Ge Song. "A Verifiable Visual Cryptography Scheme Using Neural Networks." In 2nd International Conference on Computer and Information Applications (ICCIA 2012). Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccia.2012.27.

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Kounavis, Michael E., Joel Morrissette, Sadagopan Srinivasan, and Raj Yavatkar. "Detecting non-transient anomalies in visual information using neural networks." In 2011 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2011. http://dx.doi.org/10.1109/iscc.2011.5983853.

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Guobao Xu, Yixin Yin, Lu Yin, Yanshuang Hao, and Zhenyu Wang. "Visual information processing using cellular neural networks for mobile robot." In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443432.

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Hou, Jen-Cheng, Syu-Siang Wang, Ying-Hui Lai, Jen-Chun Lin, Yu Tsao, Hsiu-Wen Chang, and Hsin-Min Wang. "Audio-visual speech enhancement using deep neural networks." In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2016. http://dx.doi.org/10.1109/apsipa.2016.7820732.

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Reports on the topic "Neural networks; Visual information"

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Koch, Christof. Controlling the Flow of Visual Information through the Lateral Geniculate Nucleus: From Single Cells to Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, October 1991. http://dx.doi.org/10.21236/ada250578.

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Levitan, Herbert. Microcomputer-Based Data Acquisition, Analysis and Control of Information Processing by Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada177170.

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Grossberg, Stephen. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada189981.

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Lugo-Garcia, Nidza, Damien P. Kuffler, and Rosa E. Blanco. Neural Networks: Structure and Repair. Part 1. Ground Squirrel Visual System. Part 2. Formation, Maintenance and Plasticity of Synaptic Connections. Fort Belvoir, VA: Defense Technical Information Center, July 1994. http://dx.doi.org/10.21236/ada282420.

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Abstract:
Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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