Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant

Authors:
Liying Zheng;Daming Shi;Jingtao Zhang
Affiliations:
School of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China and Hejiang Institute of Agriculture Science, Harbin 150001, China;School of Electrical Engineering and Computer Science, Kyungpook National University, South Korea;Hejiang Institute of Agriculture Science, Harbin 150001, China
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

In this paper, a hybrid method of combining the mean shift (MS) with the Fisher linear discriminant (FLD) is implemented to improve the performance of crop image segmentation. The highlight is the adoption of a point-line-distance-based strategy for weighting training data at the stage of the FLD. A wide set of images was employed to test the proposed method, and the results demonstrate its high quality and stable performance. In addition, the simulation results show that the point-line-distance-based strategy takes affect via enlarging the distance of class means, increasing the between-class scatter while decreasing the within-class scatter.