Reinforcement Learning for Decision Making in Sequential Visual Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Building detection from mobile imagery using informative SIFT descriptors
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Attentive object detection using an information theoretic saliency measure
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clutter in detection tasks. This work contributes to this research by a thorough analysis of the discriminative power of local appearance patterns and by proposing to exploit local information content for object representation and recognition. In a first processing stage, we localize discriminative regions in the object views from a posterior entropy measure, and then derive object models from selected discriminative local patterns. Object recognition is then applied to test patterns with associated low entropy using an efficient voting process. The method is evaluated by various degrees of partial occlusion and Gaussian image noise, resulting in highly robust recognition even in the presence of severe occlusion effects.