Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation
International Journal of Computer Vision
Object Recognition Using Multidimensional Receptive Field Histograms
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Binocular Stereo Algorithm for Log-Polar Foveated Systems
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Towards a Spatial Model for Humanoid Social Robots
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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This work adds the concept of object to an existent low-level attention system of the humanoid robot iCub. The objects are defined as clusters of SIFT visual features. When the robot first encounters an unknown object, found to be within a certain (small) distance from its eyes, it stores a cluster of the features present within an interval about that distance, using depth perception. Whenever a previously stored object crosses the robot's field of view again, it is recognized, mapped into an egocentrical frame of reference, and gazed at. This mapping is persistent, in the sense that its identification and position are kept even if not visible by the robot. Features are stored and recognized in a bottom-up way. Experimental results on the humanoid robot iCub validate this approach. This work creates the foundation for a way of linking the bottom-up attention system with top-down, object-oriented information provided by humans.