AIMC Topic: Pattern Recognition, Visual

Clear Filters Showing 141 to 150 of 152 articles

Unsupervised neural network models of the ventral visual stream.

Proceedings of the National Academy of Sciences of the United States of America
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream...

Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms-a Deep-Learning Study.

Journal of digital imaging
Visual search behaviour and the interpretation of mammograms have been studied for errors in breast cancer detection. We aim to ascertain whether machine-learning models can learn about radiologists' attentional level and the interpretation of mammog...

Perceptual dissociations among views of objects, scenes, and reachable spaces.

Journal of experimental psychology. Human perception and performance
In everyday experience, we interact with objects and we navigate through space. Extensive research has revealed that these visual behaviors are mediated by separable object-based and scene-based processing mechanisms in the mind and brain. However, w...

Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

Cerebral cortex (New York, N.Y. : 1991)
Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magn...

Application of a brain-computer interface for person authentication using EEG responses to photo stimuli.

Journal of integrative neuroscience
In this paper, a personal authentication system that can effectively identify individuals by generating unique electroencephalogram signal features in response to self-face and non-self-face photos is presented. To achieve performance stability, a se...

A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

Journal of vision
Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important...

Visual properties and memorising scenes: Effects of image-space sparseness and uniformity.

Attention, perception & psychophysics
Previous studies have demonstrated that humans have a remarkable capacity to memorise a large number of scenes. The research on memorability has shown that memory performance can be predicted by the content of an image. We explored how remembering an...

Central and peripheral vision for scene recognition: A neurocomputational modeling exploration.

Journal of vision
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is m...

Parsing Based on Parselets: A Unified Deformable Mixture Model for Human Parsing.

IEEE transactions on pattern analysis and machine intelligence
Human parsing, namely partitioning the human body into semantic regions, has drawn much attention recently for its wide applications in human-centric analysis. Previous works often consider solving the problem of human pose estimation as the prerequi...