A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Visual Attention Guided Seed Selection for Color Image Segmentation
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
A Computational Model of Depth-Based Attention
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Computing Visual Attention from Scene Depth
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Bimodal Laser-Based Attention System
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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In this paper we address the problem of obtaining meaningful saliency measures that tie in coherently with other methods and modalities within larger robotic systems. We learn probabilistic models of various saliency cues from labeled training data and fuse these into probability maps, which while appearing to be qualitatively similar to traditional saliency maps, represent actual probabilities of detecting salient features. We show that these maps are better suited to pick up task-relevant structures in robotic applications. Moreover, having true probabilities rather than arbitrarily scaled saliency measures allows for deeper, semantically meaningful integration with other parts of the overall system.