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Separation of touching grain kernels in an image by ellipse fitting algorithm

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  023129,  2002 ASAE Annual Meeting . (doi: 10.13031/2013.10461) @2002
Authors:   Gong Zhang, Digvir S.Jayas, Chithra Karunakaran, Noel White
Keywords:   machine Vision, digital image processing, grain grading, grain cleaning, automation

An ellipse fitting algorithm was developed and tested for the separation of touching grain kernels in images. This algorithm randomly tracks the edge of touching clusters to find the base points for fitted ellipses. When fitted ellipses were created, a grouping and classifying model was used to identify the best representative ellipse for each kernel of the touching cluster. With representative ellipses, touching grain kernels were separated by morphology transform. Random touching kernel patterns of durum wheat, Canada West Red Spring (CWRS) wheat, oats, barley were used to test this algorithm, the accuracy of separation were: 91%(barley), 98%(durum wheat), 94.5%(oats), and 95%(CWRS wheat).

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