Sheep-pelt grading using laser scanning and pattern recognition

Chris C. Bowman; Peter J. Hilton; P. Wayne Power; Michael P. Hayes; Richard P. Gabric

doi:10.1117/12.257274

31 October 1996 Sheep-pelt grading using laser scanning and pattern recognition

Chris C. Bowman, Peter J. Hilton, P. Wayne Power, Michael P. Hayes, Richard P. Gabric

Proceedings Volume 2908, Machine Vision Applications, Architectures, and Systems Integration V; (1996) https://doi.org/10.1117/12.257274
Event: Photonics East '96, 1996, Boston, MA, United States

Abstract

This paper presents an overview of work underway at Industrial Research Limited directed ultimately at developing an automated grading system for pickled sheep pelts. The wide variety of defects and indistinct nature of some of them illustrate the difficulties associated with the automatic inspection of natural and varying products which poser significant technical challenges. A novel imaging approach has been taken to highlight the features of interest and thus simplify the inspection task. A laser scanner has been developed which provides simultaneous acquisition of three image types representing transmission, reflectance and fluorescent properties of the pelts. Of particular interest is the fluorescence image which highlights pelt defects not normally apparent with either the naked eye or a camera. The transmission and reflection images can be used in conjunction with the fluorescence image for defect detection as well as for calculating pelt area and for recognition of pelt identification marks. Various types of pattern recognition algorithms are under investigation to assess their potential for automating the grading process using as inputs the three image types. The approach taken is based on supervised learning using feature vectors derived in various ways for the pelt images.

Citation Download Citation

Chris C. Bowman, Peter J. Hilton, P. Wayne Power, Michael P. Hayes, and Richard P. Gabric "Sheep-pelt grading using laser scanning and pattern recognition", Proc. SPIE 2908, Machine Vision Applications, Architectures, and Systems Integration V, (31 October 1996); https://doi.org/10.1117/12.257274

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