A Method for Tree Image Segmentation Combined Adaptive Mean Shifting with Image Abstraction


Ting-ting Yang, Su-yin Zhou, Ai-jun Xu, and Jian-xin Yin, Journal of Information Processing Systems Vol. 16, No. 6, pp. 1424-1436, Dec. 2020  

10.3745/JIPS.02.0151
Keywords: Adaptive Mean Shift, Image Abstraction, Image Segmentation, Mathematical Morphology, Tree Segmentation
Fulltext:

Abstract

Although huge progress has been made in current image segmentation work, there are still no efficient segmentation strategies for tree image which is taken from natural environment and contains complex background. To improve those problems, we propose a method for tree image segmentation combining adaptive mean shifting with image abstraction. Our approach perform better than others because it focuses mainly on the background of image and characteristics of the tree itself. First, we abstract the original tree image using bilateral filtering and image pyramid from multiple perspectives, which can reduce the influence of the background and tree canopy gaps on clustering. Spatial location and gray scale features are obtained by step detection and the insertion rule method, respectively. Bandwidths calculated by spatial location and gray scale features are then used to determine the size of the Gaussian kernel function and in the mean shift clustering. Furthermore, the flood fill method is employed to fill the results of clustering and highlight the region of interest. To prove the effectiveness of tree image abstractions on image clustering, we compared different abstraction levels and achieved the optimal clustering results. For our algorithm, the average segmentation accuracy (SA), over-segmentation rate (OR), and under-segmentation rate (UR) of the crown are 91.21%, 3.54%, and 9.85%, respectively. The average values of the trunk are 92.78%, 8.16%, and 7.93%, respectively. Comparing the results of our method experimentally with other popular tree image segmentation methods, our segmentation method get rid of human interaction and shows higher SA. Meanwhile, this work shows a promising application prospect on visual reconstruction and factors measurement of tree.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Ting-ting Yang, Su-yin Zhou, Ai-jun Xu, & and Jian-xin Yin (2020). A Method for Tree Image Segmentation Combined Adaptive Mean Shifting with Image Abstraction. Journal of Information Processing Systems, 16(6), 1424-1436. DOI: 10.3745/JIPS.02.0151.

[IEEE Style]
T. Yang, S. Zhou, A. Xu and a. J. Yin, "A Method for Tree Image Segmentation Combined Adaptive Mean Shifting with Image Abstraction," Journal of Information Processing Systems, vol. 16, no. 6, pp. 1424-1436, 2020. DOI: 10.3745/JIPS.02.0151.

[ACM Style]
Ting-ting Yang, Su-yin Zhou, Ai-jun Xu, and and Jian-xin Yin. 2020. A Method for Tree Image Segmentation Combined Adaptive Mean Shifting with Image Abstraction. Journal of Information Processing Systems, 16, 6, (2020), 1424-1436. DOI: 10.3745/JIPS.02.0151.