Line Profile Based Segmentation Algorithm for Touching Corn Kernels
This work addresses a domain-specific challenge in agricultural image analysis for improving classification accuracy, but it is incremental as it builds on existing segmentation techniques.
The paper tackled the problem of segmenting touching corn kernels in images by developing a line profile based algorithm, which was tested against a watershed method and achieved efficient and accurate segmentation across different image patterns.
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.