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

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

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

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. 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.

Original languageEnglish
Pages (from-to)209-218
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1382
StatePublished - 1991
EventIntelligent Robots and Computer Vision IX: Neural, Biological, and 3-D Methods - Boston, MA, USA
Duration: Nov 7 1990Nov 9 1990

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