Machine vision algorithm generation using human visual models

Wayne D. Daley; Theodore J. Doll; Shane W. McWhorter; Anthony A. Wasilewski

doi:10.1117/12.336906

14 January 1999 Machine vision algorithm generation using human visual models

Wayne D. Daley, Theodore J. Doll, Shane W. McWhorter, Anthony A. Wasilewski

Proceedings Volume 3543, Precision Agriculture and Biological Quality; (1999) https://doi.org/10.1117/12.336906
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

The design of robust machine vision algorithms is one of the most difficult parts of developing and integrating automated systems. Historically, most of the techniques have been developed using ad hoc methodologies. This problem is more severe in the area of natural/biological products. In this arena, it has been difficult to capture and model the natural variability to be expected in the products. This present difficulty in performing quality and process control in the meat, fruit and vegetable industries. While some systems have been introduced, they do not adequately address the wide range of needs. This paper will propose an algorithm development technique that utilizes modes of the human visual system. It will address that subset of problems that humans perform well, but have proven difficult to automate with the standard machine vision techniques. The basis of the technique evaluation will be the Georgia Tech Vision model. This approach demonstrates a high level of accuracy in its ability to solve difficult problems. This paper will present the approach, the result, and possibilities for implementation.

Citation Download Citation

Wayne D. Daley, Theodore J. Doll, Shane W. McWhorter, and Anthony A. Wasilewski "Machine vision algorithm generation using human visual models", Proc. SPIE 3543, Precision Agriculture and Biological Quality, (14 January 1999); https://doi.org/10.1117/12.336906

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