Using model-driven feedback in neural network object recognition

David M. Doria; Allen Gee; James D. Leonard Jr.

doi:10.1117/12.152558

2 September 1993 Using model-driven feedback in neural network object recognition

David M. Doria, Allen Gee, James D. Leonard Jr.

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152558
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

In this paper we deal with the problem of edge extraction for the purpose of matching to a known model or set of models. We describe an approach to using geometric model based information within a feedback system, without the requirement for prior pose estimation by a matching process. We call this process model driven feedback (MDF). The feedback system uses a chord based transform of the image edges that is invariant either to translation or both translation and rotation, depending on its form. By representing both the data and model information using a geometrically invariant transform, and iteratively minimizing a function of the differences between the model and data transforms, the system is able to eliminate background edges while retaining object edges that are similar in shape to the model.

Citation Download Citation

David M. Doria, Allen Gee, and James D. Leonard Jr. "Using model-driven feedback in neural network object recognition", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152558

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