Optical EngineeringLeaf segmentation, classification, and three-dimensional recovery from a few images with close viewpoints
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In this paper, we incorporate a set of sophisticated algorithms to implement a leaf segmentation and classification system. This system inherits the advantages of these algorithms while eliminating the difficulties each algorithm faced. Our system can segment leaves from images of live plants with arbitrary image conditions, and classify them against sketched leaf shapes or real leaves. This system can also estimate the three-dimensional (3-D) information of leaves which is not only useful for leaf segmentation but is also beneficial for further 3-D shape recovery. Although our system requires more than one image to reconstruct the 3-D structure of the scene, it has been designed so that only a few images with close viewpoints are sufficient to achieve the task, thus the system is still flexible and easy to use in image acquisition. For leaf classification, we adopt the normalized centroid-contour distance as our classification feature and employ a circular-shift comparing scheme to measure leaf similarity so that the system has the advantage of being invariant to leaf translation, rotation and scaling. We have conducted a series of experiments on many leaf images and the results are encouraging. The leaves can be well segmented and the classification results are also acceptable.