A Parallel Implementation of a Neural-Network Based Object Classifier

نویسندگان

  • Enrique V. Carrera
  • Paulo C. Amaral Pereira
چکیده

This paper presents a parallel implementation of an intelligent 2-D object classifier. The artificial vision system, based on neural networks, allows for the distinction among several different classes of images and provides invariance in respect to rotation, translation and scaling of the input image. The parallel implementation was made on the P-RIO environment that facilitates parallel programming on a cluster of workstations. The results obtained in this work show a significant reduction of time in both learning and recognition tasks. Moreover, they demonstrate that a low-cost network of workstations may be used to achieve good speed-up in solving artificial vision tasks.

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تاریخ انتشار 2006