Invariant Representation and Hierarchical Network for Inspection of Nuts from X-ray Images

    A. Sim
    B. Parvin
    P. Keagy

    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.