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Isaac Weiss, Manjit Ray, "ModelBased Recognition of 3D Objects from Single Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 116128, February, 2001.  
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@article{ 10.1109/34.908963, author = {Isaac Weiss and Manjit Ray}, title = {ModelBased Recognition of 3D Objects from Single Images}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {23}, number = {2}, issn = {01628828}, year = {2001}, pages = {116128}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.908963}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  ModelBased Recognition of 3D Objects from Single Images IS  2 SN  01628828 SP116 EP128 EPD  116128 A1  Isaac Weiss, A1  Manjit Ray, PY  2001 KW  Object recognition KW  invariance KW  modelbased. VL  23 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
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 singleview 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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