Statistical and neural network classifiers in model-based 3-D object recognition

Scott C. Newton; Brian S. Nutter; Sunanda Mitra

doi:10.1117/12.25213

1 February 1991 Statistical and neural network classifiers in model-based 3-D object recognition

Scott C. Newton, Brian S. Nutter, Sunanda Mitra

Proceedings Volume 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods; (1991) https://doi.org/10.1117/12.25213
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

For autonomous machines equipped with vision capabilities and in a controlled environment 3-D model-based object identification methodologies will in general solve rigid body recognition problems. In an uncontrolled environment however several factors pose difficulties for correct identification. We have addressed the problem of 3-D object recognition using a number of methods including neural network classifiers and a Bayesian-like classifier for matching image data with model projection-derived data [1 21. Neural network classifiers used began operation as simple feature vector classifiers. However unmodelled signal behavior was learned with additional samples yielding great improvement in classification rates. The model analysis drastically shortened training time of both classification systems. In an environment where signal behavior is not accurately modelled two separate forms of learning give the systems the ability to update estimates of this behavior. Required of course are sufficient samples to learn this new information. Given sufficient information and a well-controlled environment identification of 3-D objects from a limited number of classes is indeed possible. 1.

Citation Download Citation

Scott C. Newton, Brian S. Nutter, and Sunanda Mitra "Statistical and neural network classifiers in model-based 3-D object recognition", Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); https://doi.org/10.1117/12.25213

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