Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Proto-object Based Visual Attention Model
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
A neuromorphic approach to computer vision
Communications of the ACM
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
Research has shown that the application of an attention algorithm to the front-end of an object recognition system can provide a boost in performance over extracting regions from an image in an unguided manner. However, when video imagery is taken from a moving platform, attention algorithms such as saliency can lose their potency. In this paper, we show that this loss is due to the motion channels in the saliency algorithm not being able to distinguish object motion from motion caused by platform movement in the videos, and that an object recognition system for such videos can be improved through the application of image stabilization and saliency. We apply this algorithm to airborne video samples from the DARPA VIVID dataset and demonstrate that the combination of stabilization and saliency significantly improves object recognition system performance for both stationary and moving objects.