Selective Attention in the Learning of Viewpoint and Position Invariance
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Implementing the expert object recognition pathway
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Learning 3D object recognition from an unlabelled and unordered training set
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Towards artificial systems: what can we learn from human perception?
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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Psychophysical studies have shown that humans actively exploit temporal information such as contiguity of images in object recognition. We have recently developed a recognition system which uses temporal contiguity to learn extensible representations of objects on-line. The system performs well both on real-world and synthetic data and shows robustness under illumination changes. In this paper, we present results which compare the proposed representation against simple image-based representations of the same complexity using Minkowski Minimum Distance classifiers and Support Vector Machine classifiers. Recognition results for all classifiers show large improvements with incorporated temporal information.