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| K.I. Kanatani, "3D Euclidean Versus 2D Non-Euclidean: Two Approaches to 3D Recovery from Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 3, pp. 329-332, March, 1989. | |||
| BibTex | x | ||
| @article{ 10.1109/34.21802, author = {K.I. Kanatani}, title = {3D Euclidean Versus 2D Non-Euclidean: Two Approaches to 3D Recovery from Images}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {11}, number = {3}, issn = {0162-8828}, year = {1989}, pages = {329-332}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.21802}, 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 - 3D Euclidean Versus 2D Non-Euclidean: Two Approaches to 3D Recovery from Images IS - 3 SN - 0162-8828 SP329 EP332 EPD - 329-332 A1 - K.I. Kanatani, PY - 1989 KW - 2D nonEuclidean space; computerised picture processing; 3D recovery; computer vision; shape; motion; projected images; object model; 3D Euclidean space; computer vision; computerised picture processing VL - 11 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Methods of 3D recovery in computer vision for computing the shape and motion of an object from projected images when an object model is available are classified into two types: the 3D Euclidean approach, which is based on geometrical constraints in 3D Euclidean space, and the 2D non-Euclidean space. Implications of these two approaches are discussed, and some illustrating examples are presented.
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