  Image segmentation of touching objects plays a key role in providing accurate
classification for computer vision technologies. A new line profile based
imaging segmentation algorithm has been developed to provide a robust and
accurate segmentation of a group of touching corns. The performance of the line
profile based algorithm has been compared to a watershed based imaging
segmentation algorithm. Both algorithms are tested on three different patterns
of images, which are isolated corns, single-lines, and random distributed
formations. The experimental results show that the algorithm can segment a
large number of touching corn kernels efficiently and accurately.
