3D shape reconstruction from multiple silhouettes: Generalization from few views by neural network learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2059, Issue , 2001, Pages 687-695

  Kumazawa, Itsuo ^a   Ohno, Masayoshi ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

3D MODELING; BACKPROPAGATION; GEOMETRY; IMAGE RECONSTRUCTION;

3D SHAPE MODEL; 3D SHAPE RECONSTRUCTION; MULTIPLE VIEWS; NEURAL NETWORK LEARNING; TARGET SHAPE;

NEURAL NETWORKS;

EID: 84957582522     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45129-3_63     Document Type: Conference Paper

References (12)

1. Bardinet, E., Cohen, L.D., 1998. A Parametric Deformable Model to Fit Unstructured 3D Data. Computer Vision and Image Understanding 71,(1),39-54.
2. Barr, A., 1984. Global and Local Deformations of Solid Primitives, Computer Graphics, 18, 21-30.
3. Chan, M., Metaxas, D., 1994 Physics-Based Object Pose and Shape Estimation from Multiple Views, Proc. International Conference on Pattern Recognition, Vol.1, IEEE Computer Society Press, 326-330.
4. Chung, P.C., Tsai, C.T., Y.N. Sun., 1994. Polygonal Approximation Using a Competitive Hopfield Neural Network. Pattern Recognition 27, 1,505-1,512.
5. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., 1992 Training Models of Shape from Sets of Examples, Proc. British Machine Vision Conference, Springer-Verlag, 9-18.
6. Hartley, R.I., 1997. In defense of the eight-point algorithm, IEEE Trans. Pattern Analysis and Machine Intelligence 19,(6), 580-593.
7. Igarashi, T., Matsuoka, S., Tanaka, H., 1999. Teddy: A Sketching Interface for 3D Freeform Design, ACM SIGGRAPH’99, Los Angels, 409-416.
8. Jain, A.K., Zhong, Y., Lakshmanan, S., 1996. Object Matching Using Deformable Templates, IEEE Trans. Pattern Analysis and Machine Intelligence 18, (3), 267-278.
9. Kumazawa, I., 2000. Compact and parametric shape representation by a tree of sigmoid functions for automatic shape modeling, Pattern Recognition Letters 21 651-660.
10. Shum, H.Y., Hebert, M., Ikeuchi, K., Reddy, R., 1997. An Integral Approach to Free-Form Object Modeling, IEEE Trans. Pattern Analysis and Machine Intelligence 19, (12), 1,366-1,375.
11. Zhang, Z., 1998. Image-based Geometrically-Correct Photorealistic Scene/Object Modeling(IBPhM): A Review, Proc. 3rd Asian Conference on Computer Vision (ACCV’98), 340-349.
12. Zhang, Z., 1998 Determining the epipolar geometry and its uncertainty: A review, International Jourmal of Computer Vision.