Predicting Detection Events from Bayesian Scene Recognition View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2003-06-24

AUTHORS

Georg Ogris , Lucas Paletta

ABSTRACT

This work is conceptually based on psychological findings in human perception that highlight the utility of scene interpretation in object detection processes. Objects of interest are embedded in their visual context, i.e., in visual events within their spatial neighborhood. The implication for a detection system is that early recognition of this environment might provide information to directly map to an object event. The original contribution of this work is to outline a detection system that gains prospective information out of rapid scene analysis in order to focus attention on estimated object locations. Scene recognition is outlined on the basis of rapid detection of triplet configurations of landmarks which determine the discriminability of a particular location within the scene. Formulating scene recognition in terms of posterior landmark interpretation enables a recursive integration of target predictions and hence a probabilistic representation for attention based object detection. More... »

PAGES

1058-1065

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-45103-x_139

DOI

http://dx.doi.org/10.1007/3-540-45103-x_139

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027283211


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