Paper Info
Title
Gaze Movement Control Neural Network Based on Multidimensional Topographic Class Grouping.
Abstract
Target search is an important ability of the human visual system. One major problem is that the real human visual cognitive process, which requires only few samples for learning, has abilities of inference with obtained knowledge for searching when he meets the new target. Based on the Topographic Class Grouping TCG [1] and a series of models of Visual Perceiving and Eyeball-Motion Controlling Neural Networks [2---5], we make effective improvements to the models, by incorporating the cerebral self-organizing feature mapping function in terms of multidimensional TCG. In this paper, we propose the gaze movement control neural network based on multidimensional TCG. Experiments show that gaze movement control neural network by adding a block of multidimensional TCG and by self-organizing visual field image features-spatial relationship clustering achieves the visual inference and stable results on the target search tasks.
Year
DOI
Venue
2016
10.1007/978-3-319-46672-9_67
ICONIP
Keywords
Field
DocType
Target searching,Self-organizing maps,Gaze movement control
Pattern recognition,Gaze,Inference,Computer science,Human visual system model,Self-organizing map,Artificial intelligence,Cluster analysis,Artificial neural network,Cognition,Visual field,Machine learning
Conference
Volume
ISSN
Citations 
9948
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Wenqi Zhong101.01
Jun Miao222022.17
Laiyun Qing333724.66