AIMC Topic: Pattern Recognition, Visual
Clear Filters Showing 81 to 90 of 145 articles
The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks.
NeuroImage
May 17, 2018
Visual object representations are commonly thought to emerge rapidly, yet it has remained unclear to what extent early brain responses reflect purely low-level visual features of these objects and how strongly those features contribute to later categ...
Sharpening of Hierarchical Visual Feature Representations of Blurred Images.
eNeuro
May 8, 2018
The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and top-down pathways that process the visual input in concordance with stored prior information. The inte...
Transferring and generalizing deep-learning-based neural encoding models across subjects.
NeuroImage
Apr 27, 2018
Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or ...
Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI.
International journal of neural systems
Apr 12, 2018
Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not h...
Learning from label proportions on high-dimensional data.
Neural networks : the official journal of the International Neural Network Society
Mar 20, 2018
Learning from label proportions (LLP), in which the training data is in the form of bags and only the proportion of each class in each bag is available, has attracted wide interest in machine learning. However, how to solve high-dimensional LLP probl...
Accelerated low-rank representation for subspace clustering and semi-supervised classification on large-scale data.
Neural networks : the official journal of the International Neural Network Society
Feb 2, 2018
The scalability of low-rank representation (LRR) to large-scale data is still a major research issue, because it is extremely time-consuming to solve singular value decomposition (SVD) in each optimization iteration especially for large matrices. Sev...
Coupled generative adversarial stacked Auto-encoder: CoGASA.
Neural networks : the official journal of the International Neural Network Society
Jan 31, 2018
Coupled Generative Adversarial Network (CoGAN) was recently introduced in order to model a joint distribution of a multi modal dataset. The CoGAN model lacks the capability to handle noisy data as well as it is computationally expensive and inefficie...
Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.
Neural networks : the official journal of the International Neural Network Society
Jan 31, 2018
This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Con...
A border-ownership model based on computational electromagnetism.
Neural networks : the official journal of the International Neural Network Society
Jan 10, 2018
The mathematical relation between a vector electric field and its corresponding scalar potential field is useful to formulate computational problems of lower/middle-order visual processing, specifically related to the assignment of borders to the sid...
STDP-based spiking deep convolutional neural networks for object recognition.
Neural networks : the official journal of the International Neural Network Society
Dec 23, 2017
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively sha...
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