Invariant Representation and Hierarchical Network for Inspection of Nuts from X-ray Images
Imaging and Distributed Computing Group
Information and Computing Sciences Division
Lawrence Berkeley Laboratory
Berkeley, CA 94720
Publication number: LBL-xxxxx
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An X-ray based system for the inspection of pistachio nuts
and wheat kernels for internal
insect infestation is presented. The novelty of this system is two-fold.
First, we construct an invariant representation of
infested nuts from X-ray images that
is rich, robust, and compact. Insect infestation creates a
tunnel, in the X-ray image, with reduced density of the natural material.
The tunneling effect is encoded by linking troughs on
the image and constructing a joint
curvature-proximity distribution table for each nut. The latter step
is designed to accentuate separation of those tunneling effects that
are due to the natural structure of the nut.
Second, since the representation is sparse,
we partition the joint distribution table into several regions, where
each region is used independently to train a backpropagation (BP) network.
The outputs of these subnets are then collectively trained with another
BP network. We show
that the resulting hierarchical network has the advantage
of reduced dimensionality while maintaining a performance similar to the
standard BP network.