Abstract
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.
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von Hundelshausen, F., Veloso, M. (2007). Active Monte Carlo Recognition. In: Freksa, C., Kohlhase, M., Schill, K. (eds) KI 2006: Advances in Artificial Intelligence. KI 2006. Lecture Notes in Computer Science(), vol 4314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69912-5_18
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DOI: https://doi.org/10.1007/978-3-540-69912-5_18
Publisher Name: Springer, Berlin, Heidelberg
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