AIMC Topic: Pattern Recognition, Visual

Clear Filters Showing 101 to 110 of 152 articles

Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes.

Current biology : CB
Even with great advances in machine vision, animals are still unmatched in their ability to visually search complex scenes. Animals from bees [1, 2] to birds [3] to humans [4-12] learn about the statistical relations in visual environments to guide a...

A Reading Model from the Perspective of Japanese Orthography: Connectionist Approach to the Hypothesis of Granularity and Transparency.

Journal of learning disabilities
This study presents a computer simulation model of reading in Japanese syllabic kana and morphographic kanji. The model was based on the simulation model developed by Harm and Seidenberg for reading in English. The purpose of building the current mod...

Convolutional neural network-based encoding and decoding of visual object recognition in space and time.

NeuroImage
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of vi...

Deep learning-based artificial vision for grasp classification in myoelectric hands.

Journal of neural engineering
OBJECTIVE: Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision sy...

Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees.

PLoS computational biology
The ability to generalize over naturally occurring variation in cues indicating food or predation risk is highly useful for efficient decision-making in many animals. Honeybees have remarkable visual cognitive abilities, allowing them to classify vis...

Statistical learning of parts and wholes: A neural network approach.

Journal of experimental psychology. General
Statistical learning is often considered to be a means of discovering the units of perception, such as words and objects, and representing them as explicit "chunks." However, entities are not undifferentiated wholes but often contain parts that contr...

The neural representation of the gender of faces in the primate visual system: A computer modeling study.

Psychological review
We use an established neural network model of the primate visual system to show how neurons might learn to encode the gender of faces. The model consists of a hierarchy of 4 competitive neuronal layers with associatively modifiable feedforward synapt...

Minimalistic optic flow sensors applied to indoor and outdoor visual guidance and odometry on a car-like robot.

Bioinspiration & biomimetics
Here we present a novel bio-inspired optic flow (OF) sensor and its application to visual  guidance and odometry on a low-cost car-like robot called BioCarBot. The minimalistic OF sensor was robust to high-dynamic-range lighting conditions and to var...

Effects of adaptation on numerosity decoding in the human brain.

NeuroImage
Psychophysical studies have shown that numerosity is a sensory attribute susceptible to adaptation. Neuroimaging studies have reported that, at least for relatively low numbers, numerosity can be accurately discriminated in the intra-parietal sulcus....

Event Recognition Based on Deep Learning in Chinese Texts.

PloS one
Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features u...