Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
MESH-based active Monte Carlo recognition (MESH-AMCR)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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In this paper we introduce Active Monte Carlo Recognition (AMCR), a new approach for object recognition. The method is based on seeding and propagating "relational" particles that represent hypothetical relations between low-level perception and high-level object knowledge. AMCR acts as a filter with each individual step verifying fragments of different objects, and with the sequence of resulting steps producing the overall recognition. In addition to the object label, AMCR also yields the point correspondences between the input object and the stored object. AMCR does not assume a given segmentation of the input object. It effectively handles object transformations in scale, translation, rotation, affine and non-affine distortion. We describe the general AMCR in detail, introduce a particular implementation, and present illustrative empirical results.