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

Yang, Ting-ting; Zhou, Su-yin; Xu, Ai-jun; Yin, Jian-xin

doi:10.3745/jips.02.0151

[국내논문] A Method for Tree Image Segmentation Combined Adaptive Mean Shifting with Image Abstraction 원문보기

Journal of information processing systems, v.16 no.6, 2020년, pp.1424 - 1436

Yang, Ting-ting (Zhejiang Agriculture and Forestry University) , Zhou, Su-yin (Zhejiang Agriculture and Forestry University) , Xu, Ai-jun (Zhejiang Agriculture and Forestry University) , Yin, Jian-xin (Zhejiang Agriculture and Forestry University)

Abstract ▼ AI-Helper

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.

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그림 Fig. 1. Block diagram for the proposed algorithm.
그림 Fig. 2. Image abstraction process, where a×b is the image size.
그림 Fig. 3. Clustering results of different abstraction levels. From left to right: t is 0, 5, 10, and 15, respectively. From up to down: l is 0, 5, and 10, respectively.
그림 Fig. 4. An evaluation of the crown and trunk segmentation result database, where t ranges from 0 to 15 and l is 0, 5, or 10: (a) segmentation accuracy, (b) over-segmentation rate, and (c) under-segmentation rate. The solid line shows the segmentation results of the crown, and the dashed line shows the segmentation results of the trunk.
그림 Fig. 5. Segmentation results of a tree image: (a) original tree image; (b) part of (a); (c) abstracted salient tree image; (d) part of (c); (e) clustering result; (f) filled clustering result; (g) ROI exaction result; and (h) segmentation result.
$Fig. 6. Comparison of segmentation results from four methods. From left to right: original image, ground truth, segmentation results by color feature, segmentation results by color and fractional dimension method, segmentation results using the grab cut algorithm, segmentation results using the graph cut algorithm, and our proposed AMSM algorithm.$ 그림 Fig. 6. Comparison of segmentation results from four methods. From left to right: original image, ground truth, segmentation results by color feature, segmentation results by color and fractional dimension method, segmentation results using the grab cut algorithm, segmentation results using the graph cut algorithm, and our proposed AMSM algorithm.
표 Table 1. Average segmentation accuracy for 31 tree images

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