Abstract
In this paper, we analyze the background and foreground images of jujube leaf, and propose a new Adaptive Thresholding algorithm that can segment single leaves in a leaf image extracted randomly from an online system. We use the OTSU and CANNY operators to segment the area of the target leaf by choosing the thresholds with the Mapping Function, the Shape Identification algorithm and pattern recognition. The optimization process of the algorithm, which includes Mapping Function, the Shape Identification algorithm, morphological methods and logical operations, is designed to precisely obtain the entire leaf edge. This algorithm has an advantage when segmenting complicated leaf images that contain overlapping laminas and have an uneven gray scale in the leaf region itself. Experiments show that this algorithm is both feasible and effective in segmenting jujube leaf images from real-time video systems, and we can obtain clear, smooth, accurate edge images. The algorithm can be used for other kinds of leaf or fruit image segmentation tasks after debugging and improvement.
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