Issue No. 02 - February (2001 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.908963
<p><b>Abstract</b>—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.</p>
Object recognition, invariance, model-based.
Isaac Weiss, Manjit Ray, "Model-Based Recognition of 3D Objects from Single Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 23, no. , pp. 116-128, February 2001, doi:10.1109/34.908963