Predicting detection events from Bayesian scene recognition

Authors:
Georg Ogris;Lucas Paletta
Affiliations:
JOANNEUM RESEARCH, Institute of Digital Image Processing, Graz, Austria;JOANNEUM RESEARCH, Institute of Digital Image Processing, Graz, Austria
Venue:
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Year:
2003

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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.