Toward a computational model of visual attention
Early vision and beyond
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Incorporating Background Invariance into Feature-Based Object Recognition
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Sequential Monte Carlo for Bayesian Matching of Objects with Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Significance Tests and Statistical Inequalities for Region Matching
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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We experiment a vision architecture for object matching based on a hierarchy of independent agents running asynchronously in parallel. Agentscommunicate through bidirectional signals, enabling themix of topdown and bottom-up influences. Following the so-called a contrario principle, each signal is given a strength according to the statistical relevance of its associated visual data. By handling most important signals first, the system focuses onmost promising hypotheses and provides relevant results as soon as possible. Compared to an equivalent feed-forward and sequential algorithm, our architecture is shown capable of handling more visual data and thus reach higher detection rates in less time.