Higher-order neural networks applied to 2D and 3D object recognition-Reference-Cited by-同舟云学术

Higher-order neural networks applied to 2D and 3D object recognition

Published:1994-05 Issue:2 Volume:15 Page:169-199
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Spirkovska Lilly,Reid Max B.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/BF00993276.pdf

Reference20 articles.

1. Casasent, D., & Chang, W.-T. (1985.) Parameter estimation and in-plane distortion invariant chord processing. SPIE,579, 2?10.

2. Chen, Z., & Ho, S.-Y. (1986.) Computer vision for robust 3D aircraft recognition with fast library search.Pattern Recognition, 24, 375?390.

3. Giles, G.L., & Maxwell, T. (1987.) Learning, invariances, and generalization in high-order neural networks.Applied Optics, 26, 4972?4978.

4. Giles, G.L., Griffin, R.D., & Maxwell, T. (1988.) Encoding geometric invariances in higher-order neural networks.Neural Information Processing Systems, American Institute of Physics Conference Proceedings, (pp. 301?309).

5. Haberman, R. (1983.)Elementary applied partial differential equations. Englewood Cliffs, NJ: Prentice-Hall.

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