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

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

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 (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 network

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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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3

Seeland, Marco, and Patrick Mäder. "Multi-view classification with convolutional neural networks." PLOS ONE 16, no. 1 (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 extr

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MAINZER, KLAUS. "CELLULAR NEURAL NETWORKS AND VISUAL COMPUTING." International Journal of Bifurcation and Chaos 13, no. 01 (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 interdisci

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Hartono, Pitoyo. "A transparent cancer classifier." Health Informatics Journal 26, no. 1 (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 d

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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 (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 Netw

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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 informatio

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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 (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 evolu

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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 (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

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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 (2011): 507–20. http://dx.doi.org/10.15388/informatica.2011.339.

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Majerus, Steve, Arnaud D'Argembeau, Trecy Martinez Perez, et al. "The Commonality of Neural Networks for Verbal and Visual Short-term Memory." Journal of Cognitive Neuroscience 22, no. 11 (2010): 2570–93. http://dx.doi.org/10.1162/jocn.2009.21378.

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Although many neuroimaging studies have considered verbal and visual short-term memory (STM) as relying on neurally segregated short-term buffer systems, the present study explored the existence of shared neural correlates supporting verbal and visual STM. We hypothesized that networks involved in attentional and executive processes, as well as networks involved in serial order processing, underlie STM for both verbal and visual list information, with neural specificity restricted to sensory areas involved in processing the specific items to be retained. Participants were presented sequences o

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YANG, SIMON X. "VISUAL INFORMATION ACQUISITION IN VERTEBRATE RETINA." International Journal of Information Acquisition 01, no. 01 (2004): 67–76. http://dx.doi.org/10.1142/s0219878904000033.

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In this paper, visual information acquisition in vertebrate retina is investigated using a novel neural network model. The neural network is based on the neural anatomy and function of retinal neurons in tiger salamander and mudpuppy. All the main types of retinal neurons are modeled, and their response characteristics are studied. The objective is to model the information acquisition in vertebrate retina with a simple yet effective neural network architecture. The model predictions on the main characteristics of retinal neurons are in agreement with the neurophysiological data. This study not

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Al-Tahan, Haider, and Yalda Mohsenzadeh. "Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder." PLOS Computational Biology 17, no. 3 (2021): e1008775. http://dx.doi.org/10.1371/journal.pcbi.1008775.

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While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational simil

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Moskvin, A. A., and A. G. Shishkin. "Deep Learning Based Human Emotional State Recognition in a Video." Journal of Modeling and Optimization 12, no. 1 (2020): 51–59. http://dx.doi.org/10.32732/jmo.2020.12.1.51.

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Human emotions play significant role in everyday life. There are a lot of applications of automatic emotion recognition in medicine, e-learning, monitoring, marketing etc. In this paper the method and neural network architecture for real-time human emotion recognition by audio-visual data are proposed. To classify one of seven emotions, deep neural networks, namely, convolutional and recurrent neural networks are used. Visual information is represented by a sequence of 16 frames of 96 × 96 pixels, and audio information - by 140 features for each of a sequence of 37 temporal windows. To reduce

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Anderson, Dana Z. "Material Demands for Optical Neural Networks." MRS Bulletin 13, no. 8 (1988): 30–35. http://dx.doi.org/10.1557/s0883769400064654.

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From the time of their conception, holography and holograms have evolved as a metaphor for human memory. Holograms can be made so that the information they contain is distributed throughout the holographic medium—destroy part of the hologram and the stored information remains wholly intact, except for a loss of detail. In this property holograms evidently have something in common with human memory, which is to some extent resilient against physical damage to the brain. There is much more to the metaphor than simply that information is stored in a distributed manner.Research in the optics commu

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Medina, José M. "Effects of Multiplicative Power Law Neural Noise in Visual Information Processing." Neural Computation 23, no. 4 (2011): 1015–46. http://dx.doi.org/10.1162/neco_a_00102.

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The human visual system is intrinsically noisy. The benefits of internal noise as part of visual code are controversial. Here the information-theoretic properties of multiplicative (i.e. signal-dependent) neural noise are investigated. A quasi-linear communication channel model is presented. The model shows that multiplicative power law neural noise promotes the minimum information transfer after efficient coding. It is demonstrated that Weber's law and the human contrast sensitivity function arise on the basis of minimum transfer of information and power law neural noise. The implications of

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Ceroni, Andrea, Chenyang Ma, and Ralph Ewerth. "Mining exoticism from visual content with fusion-based deep neural networks." International Journal of Multimedia Information Retrieval 8, no. 1 (2019): 19–33. http://dx.doi.org/10.1007/s13735-018-00165-4.

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Cosi, P., M. Dugatto, F. Ferrero, E. Magno Caldognetto, and K. Vagges. "Phonetic recognition by recurrent neural networks working on audio and visual information." Speech Communication 19, no. 3 (1996): 245–52. http://dx.doi.org/10.1016/0167-6393(96)00034-9.

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KATAYAMA, Masazumi, and Mitsuo KAWATO. "Neural network model integrating visual and somatic information." Journal of the Robotics Society of Japan 8, no. 6 (1990): 757–65. http://dx.doi.org/10.7210/jrsj.8.6_757.

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LAGHARI, M. S., and A. BOUJARWAH. "WEAR PARTICLE TEXTURE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 03 (1999): 415–28. http://dx.doi.org/10.1142/s0218001499000240.

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Analysis of wear debris carried by a lubricant in an oil-wetted system provides important information about the condition of a machine. This paper describes the analysis of microscopic metal particles generated by wear using computer vision and image processing. The aim is to classify these particles according to their morphology and surface texture and by using the information obtained, to predict wear failure modes in engines and other machinery. This approach obviates the need for specialists and reliance on human visual inspection techniques. The procedure reported in this paper, is used t

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Kang, Byeongkeun, and Yeejin Lee. "High-Resolution Neural Network for Driver Visual Attention Prediction." Sensors 20, no. 7 (2020): 2030. http://dx.doi.org/10.3390/s20072030.

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Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver’s visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver’s attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in thi

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Moyal, Roy, and Shimon Edelman. "Dynamic Computation in Visual Thalamocortical Networks." Entropy 21, no. 5 (2019): 500. http://dx.doi.org/10.3390/e21050500.

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Contemporary neurodynamical frameworks, such as coordination dynamics and winnerless competition, posit that the brain approximates symbolic computation by transitioning between metastable attractive states. This article integrates these accounts with electrophysiological data suggesting that coherent, nested oscillations facilitate information representation and transmission in thalamocortical networks. We review the relationship between criticality, metastability, and representational capacity, outline existing methods for detecting metastable oscillatory patterns in neural time series data,

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Reddy*, M. Venkata Krishna, and Pradeep S. "Envision Foundational of Convolution Neural Network." International Journal of Innovative Technology and Exploring Engineering 10, no. 6 (2021): 54–60. http://dx.doi.org/10.35940/ijitee.f8804.0410621.

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1. Bilal, A. Jourabloo, M. Ye, X. Liu, and L. Ren. Do Convolutional Neural Networks Learn Class Hierarchy? IEEE Transactions on Visualization and Computer Graphics, 24(1):152–162, Jan. 2018. 2. M. Carney, B. Webster, I. Alvarado, K. Phillips, N. Howell, J. Griffith, J. Jongejan, A. Pitaru, and A. Chen. Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20. ACM, Honolulu, HI, USA, 2020. 3. A. Karpathy. CS231n Convolutional Neural Networks for Visual Recognition

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MARSHALL, JONATHAN A., and VISWANATH SRIKANTH. "CURVED TRAJECTORY PREDICTION USING A SELF-ORGANIZING NEURAL NETWORK." International Journal of Neural Systems 10, no. 01 (2000): 59–70. http://dx.doi.org/10.1142/s0129065700000065.

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Existing neural network models are capable of tracking linear trajectories of moving visual objects. This paper describes an additional neural mechanism, disfacilitation, that enhances the ability of a visual system to track curved trajectories. The added mechanism combines information about an object's trajectory with information about changes in the object's trajectory, to improve the estimates for the object's next probable location. Computational simulations are presented that show how the neural mechanism can learn to track the speed of objects and how the network operates to predict the

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Kádár, Ákos, Grzegorz Chrupała, and Afra Alishahi. "Representation of Linguistic Form and Function in Recurrent Neural Networks." Computational Linguistics 43, no. 4 (2017): 761–80. http://dx.doi.org/10.1162/coli_a_00300.

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We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings: The Visual pathway is trained on predicting the representations of the visual scene corresponding to an input sentence, and the Textual pathway is trained to predict the next word in the same sentence. We propose a method for estimating

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Wai Yong, Ching, Kareen Teo, Belinda Pingguan Murphy, Yan Chai Hum, and Khin Wee Lai. "CORSegNet: Deep Neural Network for Core Object Segmentation on Medical Images." Journal of Medical Imaging and Health Informatics 11, no. 5 (2021): 1364–71. http://dx.doi.org/10.1166/jmihi.2021.3380.

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In recent decades, convolutional neural networks (CNNs) have delivered promising results in vision-related tasks across different domains. Previous studies have introduced deeper network architectures to further improve the performances of object classification, localization, and segmentation. However, this induces the complexity in mapping network’s layer to the processing elements in the ventral visual pathway. Although CORnet models are not precisely biomimetic, they are closer approximations to the anatomy of ventral visual pathway compared with other deep neural networks. The uniqueness o

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Chu, Wei-Ta, Yu-Hsuan Liang, and Kai-Chia Ho. "Visual Weather Property Prediction by Multi-Task Learning and Two-Dimensional RNNs." Atmosphere 12, no. 5 (2021): 584. http://dx.doi.org/10.3390/atmos12050584.

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We attempted to employ convolutional neural networks to extract visual features and developed recurrent neural networks for weather property estimation using only image data. Four common weather properties are estimated, i.e., temperature, humidity, visibility, and wind speed. Based on the success of previous works on temperature prediction, we extended them in terms of two aspects. First, by considering the effectiveness of deep multi-task learning, we jointly estimated four weather properties on the basis of the same visual information. Second, we propose that weather property estimations co

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Rothlein, David, Joseph DeGutis, and Michael Esterman. "Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations." Journal of Cognitive Neuroscience 30, no. 9 (2018): 1209–28. http://dx.doi.org/10.1162/jocn_a_01306.

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Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of thi

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Vakhshiteh, Fatemeh, Farshad Almasganj, and Ahmad Nickabadi. "LIP-READING VIA DEEP NEURAL NETWORKS USING HYBRID VISUAL FEATURES." Image Analysis & Stereology 37, no. 2 (2018): 159. http://dx.doi.org/10.5566/ias.1859.

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Lip-reading is typically known as visually interpreting the speaker's lip movements during speaking. Experiments over many years have revealed that speech intelligibility increases if visual facial information becomes available. This effect becomes more apparent in noisy environments. Taking steps toward automating this process, some challenges will be raised such as coarticulation phenomenon, visual units' type, features diversity and their inter-speaker dependency. While efforts have been made to overcome these challenges, presentation of a flawless lip-reading system is still under the inve

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Casabianca, Pietro, and Yu Zhang. "Acoustic-Based UAV Detection Using Late Fusion of Deep Neural Networks." Drones 5, no. 3 (2021): 54. http://dx.doi.org/10.3390/drones5030054.

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Multirotor UAVs have become ubiquitous in commercial and public use. As they become more affordable and more available, the associated security risks further increase, especially in relation to airspace breaches and the danger of drone-to-aircraft collisions. Thus, robust systems must be set in place to detect and deal with hostile drones. This paper investigates the use of deep learning methods to detect UAVs using acoustic signals. Deep neural network models are trained with mel-spectrograms as inputs. In this case, Convolutional Neural Networks (CNNs) are shown to be the better performing n

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Gong, Yan, Georgina Cosma, and Hui Fang. "On the Limitations of Visual-Semantic Embedding Networks for Image-to-Text Information Retrieval." Journal of Imaging 7, no. 8 (2021): 125. http://dx.doi.org/10.3390/jimaging7080125.

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Visual-semantic embedding (VSE) networks create joint image–text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image–text retrieval, image captioning, and visual question answering. The most recent state-of-the-art VSE-based networks are: VSE++, SCAN, VSRN, and UNITER. This study evaluates the performance of those VSE networks for the task of image-to-text retrieval and identifies and analyses their strengths and limitations to guide future research on the topic. The experimental results on Flickr30K revealed

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Ni, Xubin, Lirong Yin, Xiaobing Chen, Shan Liu, Bo Yang, and Wenfeng Zheng. "Semantic representation for visual reasoning." MATEC Web of Conferences 277 (2019): 02006. http://dx.doi.org/10.1051/matecconf/201927702006.

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In the field of visual reasoning, image features are widely used as the input of neural networks to get answers. However, image features are too redundant to learn accurate characterizations for regular networks. While in human reasoning, abstract description is usually constructed to avoid irrelevant details. Inspired by this, a higher-level representation named semantic representation is introduced in this paper to make visual reasoning more efficient. The idea of the Gram matrix used in the neural style transfer research is transferred here to build a relation matrix which enables the relat

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Vihlman, Mikko, and Arto Visala. "Optical Flow in Deep Visual Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12112–19. http://dx.doi.org/10.1609/aaai.v34i07.6890.

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Single-target tracking of generic objects is a difficult task since a trained tracker is given information present only in the first frame of a video. In recent years, increasingly many trackers have been based on deep neural networks that learn generic features relevant for tracking. This paper argues that deep architectures are often fit to learn implicit representations of optical flow. Optical flow is intuitively useful for tracking, but most deep trackers must learn it implicitly. This paper is among the first to study the role of optical flow in deep visual tracking. The architecture of

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Garcia, Noa, Benjamin Renoust, and Yuta Nakashima. "ContextNet: representation and exploration for painting classification and retrieval in context." International Journal of Multimedia Information Retrieval 9, no. 1 (2019): 17–30. http://dx.doi.org/10.1007/s13735-019-00189-4.

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AbstractIn automatic art analysis, models that besides the visual elements of an artwork represent the relationships between the different artistic attributes could be very informative. Those kinds of relationships, however, usually appear in a very subtle way, being extremely difficult to detect with standard convolutional neural networks. In this work, we propose to capture contextual artistic information from fine-art paintings with a specific ContextNet network. As context can be obtained from multiple sources, we explore two modalities of ContextNets: one based on multitask learning and a

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Ghariba, Bashir, Mohamed S. Shehata, and Peter McGuire. "Visual Saliency Prediction Based on Deep Learning." Information 10, no. 8 (2019): 257. http://dx.doi.org/10.3390/info10080257.

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Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition,

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Kalinina, M. O., and P. L. Nikolaev. "Book spine recognition with the use of deep neural networks." Computer Optics 44, no. 6 (2020): 968–77. http://dx.doi.org/10.18287/2412-6179-co-731.

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Nowadays deep neural networks play a significant part in various fields of human activity. Especially they benefit spheres dealing with large amounts of data and lengthy operations on obtaining and processing information from the visual environment. This article deals with the development of a convolutional neural network based on the YOLO architecture, intended for real-time book recognition. The creation of an original data set and the training of the deep neural network are described. The structure of the neural network obtained is presented and the most frequently used metrics for estimati

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Gulshad, Sadaf, and Arnold Smeulders. "Counterfactual attribute-based visual explanations for classification." International Journal of Multimedia Information Retrieval 10, no. 2 (2021): 127–40. http://dx.doi.org/10.1007/s13735-021-00208-3.

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AbstractIn this paper, our aim is to provide human understandable intuitive factual and counterfactual explanations for the decisions of neural networks. Humans tend to reinforce their decisions by providing attributes and counterattributes. Hence, in this work, we utilize attributes as well as examples to provide explanations. In order to provide counterexplanations we make use of directed perturbations to arrive at the counterclass attribute values in doing so, we explain what is present and what is absent in the original image. We evaluate our method when images are misclassified into close

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Ergun, Hilal, Yusuf Caglar Akyuz, Mustafa Sert, and Jianquan Liu. "Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition." International Journal of Semantic Computing 10, no. 03 (2016): 379–97. http://dx.doi.org/10.1142/s1793351x16400158.

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Visual concept recognition is an active research field in the last decade. Related to this attention, deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition in videos. In this study, we investigate various aspects of convolutional neural networks for visual concept recognition. We analyze recent studies and different network architectures both in terms of running time and accuracy. In our proposed visual concept recognition system, we first discuss various important proper

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White, Robert L., and Lawrence H. Snyder. "Spatial constancy and the brain: insights from neural networks." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1479 (2007): 375–82. http://dx.doi.org/10.1098/rstb.2006.1965.

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To form an accurate internal representation of visual space, the brain must accurately account for movements of the eyes, head or body. Updating of internal representations in response to these movements is especially important when remembering spatial information, such as the location of an object, since the brain must rely on non-visual extra-retinal signals to compensate for self-generated movements. We investigated the computations underlying spatial updating by constructing a recurrent neural network model to store and update a spatial location based on a gaze shift signal, and to do so f

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Orbán, Levente L., and Sylvain Chartier. "Unsupervised Neural Network Quantifies the Cost of Visual Information Processing." PLOS ONE 10, no. 7 (2015): e0132218. http://dx.doi.org/10.1371/journal.pone.0132218.

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Michaels, Jonathan A., Stefan Schaffelhofer, Andres Agudelo-Toro, and Hansjörg Scherberger. "A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping." Proceedings of the National Academy of Sciences 117, no. 50 (2020): 32124–35. http://dx.doi.org/10.1073/pnas.2005087117.

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One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. However, no comprehensive model exists that links all steps of processing from vision to action. We hypothesized that a recurrent neural network mimicking the modular structure of the anatomical circuit and trained to use visual features of objects to generate the required muscle dynamics used by primates to gra

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Brunel, Nicolas. "Hebbian Learning of Context in Recurrent Neural Networks." Neural Computation 8, no. 8 (1996): 1677–710. http://dx.doi.org/10.1162/neco.1996.8.8.1677.

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Single electrode recordings in the inferotemporal cortex of monkeys during delayed visual memory tasks provide evidence for attractor dynamics in the observed region. The persistent elevated delay activities could be internal representations of features of the learned visual stimuli shown to the monkey during training. When uncorrelated stimuli are presented during training in a fixed sequence, these experiments display significant correlations between the internal representations. Recently a simple model of attractor neural network has reproduced quantitatively the measured correlations. An u

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Wang, Yong, Xinbin Luo, Lu Ding, Shan Fu, and Xian Wei. "Detection based visual tracking with convolutional neural network." Knowledge-Based Systems 175 (July 2019): 62–71. http://dx.doi.org/10.1016/j.knosys.2019.03.012.

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Liu, Yu, Xun Chen, Juan Cheng, Hu Peng, and Zengfu Wang. "Infrared and visible image fusion with convolutional neural networks." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 03 (2018): 1850018. http://dx.doi.org/10.1142/s0219691318500182.

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The fusion of infrared and visible images of the same scene aims to generate a composite image which can provide a more comprehensive description of the scene. In this paper, we propose an infrared and visible image fusion method based on convolutional neural networks (CNNs). In particular, a siamese convolutional network is applied to obtain a weight map which integrates the pixel activity information from two source images. This CNN-based approach can deal with two vital issues in image fusion as a whole, namely, activity level measurement and weight assignment. Considering the different ima

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Mei, Xiaoguang, Erting Pan, Yong Ma, et al. "Spectral-Spatial Attention Networks for Hyperspectral Image Classification." Remote Sensing 11, no. 8 (2019): 963. http://dx.doi.org/10.3390/rs11080963.

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Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while

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YUE, TAI-WEN, and SUCHEN CHIANG. "A NEURAL-NETWORK APPROACH FOR VISUAL CRYPTOGRAPHY AND AUTHORIZATION." International Journal of Neural Systems 14, no. 03 (2004): 175–87. http://dx.doi.org/10.1142/s012906570400198x.

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In this paper, we propose a neural-network approach for visual authorization, which is an application of visual cryptography (VC). The scheme contains a key-share and a set of user-shares. The administrator owns the key-share, and each user owns a user-share issued by the administrator from the user-share set. The shares in the user-share set are visually indistinguishable, i.e. they have the same pictorial meaning. However, the stacking of the key-share with different user-shares will reveal significantly different images. Therefore, the administrator (in fact, only the administrator) can vis

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Koji, Yukichi, Naoyoshi Takatsu, and Masanari Oh. "Visual solder inspection using neural network." Systems and Computers in Japan 27, no. 1 (1996): 92–100. http://dx.doi.org/10.1002/scj.4690270109.

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Seeliger, K., L. Ambrogioni, Y. Güçlütürk, L. M. van den Bulk, U. Güçlü, and M. A. J. van Gerven. "End-to-end neural system identification with neural information flow." PLOS Computational Biology 17, no. 2 (2021): e1008558. http://dx.doi.org/10.1371/journal.pcbi.1008558.

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Neural information flow (NIF) provides a novel approach for system identification in neuroscience. It models the neural computations in multiple brain regions and can be trained end-to-end via stochastic gradient descent from noninvasive data. NIF models represent neural information processing via a network of coupled tensors, each encoding the representation of the sensory input contained in a brain region. The elements of these tensors can be interpreted as cortical columns whose activity encodes the presence of a specific feature in a spatiotemporal location. Each tensor is coupled to the m

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Ramsey, Richard. "Neural Integration in Body Perception." Journal of Cognitive Neuroscience 30, no. 10 (2018): 1442–51. http://dx.doi.org/10.1162/jocn_a_01299.

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The perception of other people is instrumental in guiding social interactions. For example, the appearance of the human body cues a wide range of inferences regarding sex, age, health, and personality, as well as emotional state and intentions, which influence social behavior. To date, most neuroscience research on body perception has aimed to characterize the functional contribution of segregated patches of cortex in the ventral visual stream. In light of the growing prominence of network architectures in neuroscience, the current article reviews neuroimaging studies that measure functional i

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Tai, Lei, Shaohua Li, and Ming Liu. "Autonomous exploration of mobile robots through deep neural networks." International Journal of Advanced Robotic Systems 14, no. 4 (2017): 172988141770357. http://dx.doi.org/10.1177/1729881417703571.

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The exploration problem of mobile robots aims to allow mobile robots to explore an unknown environment. We describe an indoor exploration algorithm for mobile robots using a hierarchical structure that fuses several convolutional neural network layers with decision-making process. The whole system is trained end to end by taking only visual information (RGB-D information) as input and generates a sequence of main moving direction as output so that the robot achieves autonomous exploration ability. The robot is a TurtleBot with a Kinect mounted on it. The model is trained and tested in a real w

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