Visual Processing and Classification of Items on a Moving Conveyor: A Selective Perception Approach
Isil Bozma, Hulya Yalcin


Many industrial applications require some sort of automated visual processing and classification of items placed on a moving conveyor. In this paper, we present a selective perception based approach to visual processing. The novelty of this approach is that instead of processing the whole image, only areas that are deemed "interesting" and hence calling for attention are analyzed. The attentional sequences thus constructed can then be used for a variety of tasks including shape determination. Since only a small portion of the whole image is processed, visual processing can be real-time and flexible without requiring special hardware. Two different applications based on this approach are described. In a defective item detection task, we explain in detail how attentional sequences can be used. As a second application, the approach has been implemented in an automated remote controller sorter in a TV manufacturing plant––thus confirming its practical applicability.

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