Abstract
This paper proposes an approach for the unsupervised learning of object models from local image feature correspondences. The object models are learned from an unlabeled sequence of training images showing one object after the other. The obtained object models enable the recognition of these objects in cluttered scenes, under occlusion, in-plane rotation and scale change. Maximally stable extremal regions are used as local image features and two different types of descriptors characterising the appearance and shape of the regions allow a robust matching. Experiments with real objects show the recognition performance of the presented approach under various conditions.
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Leitner, R., Bischof, H. (2005). Recognition of 3D Objects by Learning from Correspondences in a Sequence of Unlabeled Training Images. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_46
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DOI: https://doi.org/10.1007/11550518_46
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