Piecewise cubic mapping functions for image registration
Pattern Recognition
Using resolution pyramids for watershed image segmentation
Image and Vision Computing
A Crop Field Remote Monitoring System Based on Web-Server-Embedded Technology and CDMA Service
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
Pattern Recognition Letters
Computer Vision and Image Understanding
Segmentation of beef marbling based on vision threshold
Computers and Electronics in Agriculture
A similarity-based leaf image retrieval scheme: Joining shape and venation features
Computer Vision and Image Understanding
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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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.