  This is a presentation of a new system for invariant recognition of 2D
objects with overlapping classes, that can not be effectively recognized with
the traditional methods. The translation, scale and partial rotation invariant
contour object description is transformed in a DCT spectrum space. The obtained
frequency spectrums are decomposed into frequency bands in order to feed
different BPG neural nets (NNs). The NNs are structured in three stages -
filtering and full rotation invariance; partial recognition; general
classification. The designed multi-stage BPG Neural Structure shows very good
accuracy and flexibility when tested with 2D objects used in the discontinuous
production. The reached speed and the opportunuty for an easy restructuring and
reprogramming of the system makes it suitable for application in different
applied systems for real time work.
