Performance of a neural-network-based 3-D object recognition system

Steven J. Rak; Paul J. Kolodzy

doi:10.1117/12.44876

1 August 1991 Performance of a neural-network-based 3-D object recognition system

Steven J. Rak, Paul J. Kolodzy

Proceedings Volume 1471, Automatic Object Recognition; (1991) https://doi.org/10.1117/12.44876
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

Object recognition in laser radar sensor imagery is a challenging application of neural networks. The task involves recognition of objects at a variety of distances and aspects with significant levels of sensor noise. These variables are related to sensor parameters such as sensor signal strength and angular resolution, as well as object range and viewing aspect. The effect of these parameters on a fixed recognition system based on log-polar mapped features and an unsupervised neural network classifier are investigated. This work is an attempt to quantify the design parameters of a laser radar measurement system with respect to classifying and/or identifying objects by the shape of their silhouettes. Experiments with vehicle silhouettes rotated through 90 deg-of-view angle from broadside to head-on ('out-of-plane' rotation) have been used to quantify the performance of a log-polar map/neural-network based 3-D object recognition system. These experiments investigated several key issues such as category stability, category memory compression, image fidelity, and viewing aspect. Initial results indicate a compression from 720 possible categories (8 vehicles X 90 out-of-plane rotations) to a classifier memory with approximately 30 stable recognition categories. These results parallel the human experience of studying an object from several viewing angles yet recognizing it through a wide range of viewing angles. Results are presented illustrating category formation for an eight vehicle dataset as a function of several sensor parameters. These include: (1) sensor noise, as a function of carrier-to-noise ratio; (2) pixels on the vehicle, related to angular resolution and target range; and (3) viewing aspect, as related to sensor-to-platform depression angle. This work contributes to the formation of a three- dimensional object recognition system.

Citation Download Citation

Steven J. Rak and Paul J. Kolodzy "Performance of a neural-network-based 3-D object recognition system", Proc. SPIE 1471, Automatic Object Recognition, (1 August 1991); https://doi.org/10.1117/12.44876

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