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Model-Based Recognition of 3D Objects from Single Images
February 2001 (vol. 23 no. 2)
pp. 116-128

Abstract—In this work, we treat major problems of object recognition which have received relatively little attention lately. Among them are the loss of depth information in the projection from a 3D object to a single 2D image, and the complexity of finding feature correspondences between images. We use geometric invariants to reduce the complexity of these problems. There are no geometric invariants of a projection from 3D to 2D. However, given certain modeling assumptions about the 3D object, such invariants can be found. The modeling assumptions can be either a particular model or a generic assumption about a class of models. Here, we use such assumptions for single-view recognition. We find algebraic relations between the invariants of a 3D model and those of its 2D image under general projective projection. These relations can be described geometrically as invariant models in a 3D invariant space, illuminated by invariant “light rays,” and projected onto an invariant version of the given image. We apply the method to real images.

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Index Terms:
Object recognition, invariance, model-based.
Citation:
Isaac Weiss, Manjit Ray, "Model-Based Recognition of 3D Objects from Single Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 116-128, Feb. 2001, doi:10.1109/34.908963
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