Line Profile Based Segmentation Algorithm for Touching Corn Kernels

TL;DR

This paper introduces a new line profile based segmentation algorithm that accurately separates touching corn kernels in images, outperforming traditional watershed methods across various image patterns.

Contribution

The paper presents a novel line profile based segmentation algorithm specifically designed for touching objects, demonstrating improved accuracy and robustness over existing watershed techniques.

Findings

The new algorithm effectively segments touching corn kernels in diverse image patterns.

It outperforms watershed-based segmentation in accuracy and efficiency.

Experimental results confirm robustness across different image configurations.

Abstract

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.