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Fig. 7 Different steps of the segmentation procedure.
Preliminary results (unpublished), obtained by reusing the two first steps of the segmentation pipeline (rough segmentation and stem segmentation) on corn, allowed to isolate the main stem, the leaves, and inter-nodes, and allowed the direct computation of corn specific data.
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The last step of the segmentation is a merger algorithm, which intends to make the coordinates unique.
In the final step of the segmentation, morphological processing is employed to separate touching parasites.
The petioles are also segmented (and separated) from the leaves in each regions N i (created in the first step of the segmentation) using tubular fitting.
Repeat step (ii) to step (iv) for all the segments in. Figure 7 illustrates the steps of the proposed segmentation method.
In the following, the sequential steps of the proposed segmentation algorithm (whose flowchart is shown in Figure 7) are detailed.
Note that the accuracy of the final radius estimates depends on the reliable estimation of several parameters at the different steps of the method (segmentation, camera parameters, etc).
Step 3: Convolutional neural network-based pixel-wise segmentation The final step of the automated segmentation technique was the application of another convolutional neural network trained to perform pixel-wise segmentation of the CT image.
Figure 2 Outcomes obtained at subsequent steps of the adaptive skin segmentation process.
The major steps of the technique are segmentation, to isolate the breast region from mammogram background, and adaptive layer GL remapping.
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