Attention can improve a simple model for object recognition

Authors:
E. Bermudez-Contreras;H. Buxton;E. Spier
Affiliations:
University of Sussex, Department of Informatics, Falmer, Brighton BN1 9HQ, UK;University of Sussex, Department of Informatics, Falmer, Brighton BN1 9HQ, UK;University of Sussex, Department of Informatics, Falmer, Brighton BN1 9HQ, UK
Venue:
Image and Vision Computing
Year:
2008

Citing 8
Cited 0

Animate vision

Artificial Intelligence
Active Perception

Active Perception
Attentional Selection for Object Recognition A Gentle Way

BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A Binocular Stereo Algorithm for Log-Polar Foveated Systems

BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
A Similarity-Based Method for the Generalization of Face Recognition over Pose and Expression

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coevolution of active vision and feature selection

Biological Cybernetics
Object Recognition with Features Inspired by Visual Cortex

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A biologically inspired solution for an evolved simulated agent

Proceedings of the 9th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Object recognition is one of the most important tasks of the visual cortex. Even though it has been closely studied in the field of computer vision and neuroscience, the underlying processes in the visual cortex are not completely understood. A model that lately has gained attention is the HMAX model, which describes a feedforward hierarchical structure. This model shows a degree of scale and translation invariance. Our work explores and compares the HMAX model with a simpler model for object recognition emulating simple cells in the primary visual cortex, V1. This model shows a better performance than the HMAX model for translation and scale invariance experiments when an attentional mechanism is employed in realistic conditions.