Journal Article10.1117/1.3549927
Leaf segmentation, classification, and three-dimensional recovery from a few images with close viewpoints
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TL;DR: This system can segment leaves from images of live plants with arbitrary image conditions, and classify them against sketched leaf shapes or real leaves, and estimate the three-dimensional information of leaves which is not only useful for leaf segmentation but is also beneficial for further 3-D shape recovery.
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Abstract: 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.
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Citations
Leaf segmentation in plant phenotyping: a collation study
Hanno Scharr,Massimo Minervini,Andrew P. French,Christian Klukas,David Kramer,Xiaoming Liu,Imanol Luengo,Jean-Michel Pape,Gerrit Polder,Danijela Vukadinovic,Xi Yin,Sotirios A. Tsaftaris +11 more
- 01 May 2016
TL;DR: Comparing several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments finds that although separating plant from background can be accomplished with satisfactory accuracy, individual Leaf Segmentation and counting remain challenging when leaves overlap.
In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation.
TL;DR: The proposed method is able to segment individual leaves from heavy occlusions in the complicated natural scene and half of the experimental results show segmentation rates of individual leaves higher than 90%.
105
Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods
Manuel Grand-Brochier,Antoine Vacavant,Guillaume Cerutti,Camille Kurtz,Jonathan Weber,Laure Tougne +5 more
TL;DR: A comparative study of various segmentation methods applied to the extraction of tree leaves from natural images shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria.
An individual grape leaf disease identification using leaf skeletons and KNN classification
N. Krithika,A. Grace Selvarani +1 more
- 17 Mar 2017
TL;DR: The most challenging process in agricultural applications is identification of leaf individually and in this paper, the classification of grape leaf diseases is proposed along with the leaf identification.
68
Robotized Plant Probing: Leaf Segmentation Utilizing Time-of-Flight Data
TL;DR: Plant modeling for long-lasting extensive botanic experiments with strong demands, particularly in what concerns three-dimensional information gathering and speed, is studied.
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